This paper proposes a system for recognizing human facial actions from images using image processing and machine learning techniques. The system first detects faces in images using a pretrained detector. Facial landmarks are then extracted to locate features like eyes, nose, mouth etc. Features extracted from the landmarks are used to recognize six basic facial expressions (happy, sad, angry, surprised, disgusted and neutral). The system is trained on a facial expression dataset to learn the patterns associated with each expression. The trained model can then be used to automatically recognize the expression in new input images. The proposed system has applications in areas like human-computer interaction, lie detection, sentiment analysis etc.
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This document provides a review of digital image processing techniques. It discusses several key areas:
- Digital image processing is used in applications like video editing, biometric systems, and more. It covers techniques such as image acquisition, segmentation, modification, restoration, and compression.
- Algorithms like SIFT, SURF, BRIEF, and ORB are explored along with their benefits and drawbacks.
- Image processing techniques including segmentation, enhancement, compression, restoration, and representation are defined and explained. Applications in areas like facial recognition, target detection, and biometrics are also covered.
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Topics covered: Biomedical imaging, Need of image processing in medicine, Principles of image processing, Components of image processing, Application of image processing in different medical imaging systems
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1. The document discusses the evaluation of a proposed satellite image design project and necessary corrective actions.
2. The objectives of the project are to construct a land cover classification taxonomy, classify satellite images by type (e.g. vegetation, buildings, water), and use MapReduce to process large amounts of satellite image data.
3. Satellite images play a major role in event detection like changing landscapes, monitoring glaciers, and detecting disasters. The project aims to detect land changes over time, store and classify the data, and retrieve it using defined mechanisms.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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This document discusses lung nodules and digital image processing for lung cancer detection. It defines lung nodules as small masses in the lungs that can be used to identify potentially cancerous tissue. Computed tomography (CT) scans are commonly used but interpreting large numbers of scans can be challenging. Computer-aided diagnosis (CAD) systems help by automatically analyzing scans. The document then provides an overview of digital image processing, describing it as the science of manipulating digital images to extract useful information. Key steps include acquisition, preprocessing, segmentation, representation, recognition, and knowledge-based analysis.
Quality assessment of resultant images after processingAlexander Decker
This document discusses quality assessment of images after processing. It provides an overview of traditional perceptual image quality assessment approaches, which are based on measuring errors between distorted and reference images. These methods involve channel decomposition, error normalization based on visual sensitivity, and error pooling. The document also discusses information theoretic approaches to quality assessment, which view it as an information fidelity problem rather than just a signal fidelity problem. These approaches relate visual quality to the mutual information shared between the reference and test images. However, these methods make assumptions that are difficult to validate.
Image Segmentation Based Survey on the Lung Cancer MRI ImagesIIRindia
Educational data mining (EDM) creates high impact in the field of academic domain. The methods used in this topic are playing a major advanced key role in increasing knowledge among students. EDM explores and gives ideas in understanding behavioral patterns of students to choose a correct path for choosing their carrier. This survey focuses on such category and it discusses on various techniques involved in making educational data mining for their knowledge improvement. Also, it discusses about different types of EDM tools and techniques in this article. Among the different tools and techniques, best categories are suggested for real world usage.
Discover the fundamentals, Characteristics & types of digital image analysis. Learn about pixels, bit depth, challenges, and AI impacts on image processing.
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This document provides a review of digital image processing techniques. It discusses several key areas:
- Digital image processing is used in applications like video editing, biometric systems, and more. It covers techniques such as image acquisition, segmentation, modification, restoration, and compression.
- Algorithms like SIFT, SURF, BRIEF, and ORB are explored along with their benefits and drawbacks.
- Image processing techniques including segmentation, enhancement, compression, restoration, and representation are defined and explained. Applications in areas like facial recognition, target detection, and biometrics are also covered.
Biomedical Image Processing
Topics covered: Biomedical imaging, Need of image processing in medicine, Principles of image processing, Components of image processing, Application of image processing in different medical imaging systems
Evaluation Of Proposed Design And Necessary Corrective ActionSandra Arveseth
1. The document discusses the evaluation of a proposed satellite image design project and necessary corrective actions.
2. The objectives of the project are to construct a land cover classification taxonomy, classify satellite images by type (e.g. vegetation, buildings, water), and use MapReduce to process large amounts of satellite image data.
3. Satellite images play a major role in event detection like changing landscapes, monitoring glaciers, and detecting disasters. The project aims to detect land changes over time, store and classify the data, and retrieve it using defined mechanisms.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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Quality assessment of resultant images after processingAlexander Decker
This document discusses quality assessment of images after processing. It provides an overview of traditional perceptual image quality assessment approaches, which are based on measuring errors between distorted and reference images. These methods involve channel decomposition, error normalization based on visual sensitivity, and error pooling. The document also discusses information theoretic approaches to quality assessment, which view it as an information fidelity problem rather than just a signal fidelity problem. These approaches relate visual quality to the mutual information shared between the reference and test images. However, these methods make assumptions that are difficult to validate.
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Educational data mining (EDM) creates high impact in the field of academic domain. The methods used in this topic are playing a major advanced key role in increasing knowledge among students. EDM explores and gives ideas in understanding behavioral patterns of students to choose a correct path for choosing their carrier. This survey focuses on such category and it discusses on various techniques involved in making educational data mining for their knowledge improvement. Also, it discusses about different types of EDM tools and techniques in this article. Among the different tools and techniques, best categories are suggested for real world usage.
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This document provides an overview of image analysis, including:
1) It defines image analysis and discusses its use in recognizing, differentiating, and quantifying images across various fields including food quality assessment.
2) It describes the process of creating a digital image through digitization and discusses key aspects of digital images like resolution, pixel bit depth, and color.
3) It outlines common image processing actions like compression, preprocessing, and analysis and provides examples of applying image analysis to evaluate food products.
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This document provides an overview of image processing. It defines image processing as extracting useful information from images through modification and analysis. Some key applications mentioned include astronomy, medicine, biometrics, remote sensing, and personal photos. Essential aspects of image processing include signal processing, matrix theory, and probability theory. The main purposes of image processing are visualization, image enhancement, retrieval, measurement, and recognition. Future developments may integrate optical computing to match or exceed human capabilities in image analysis.
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Using Image Acquisition Is The Input Text DocumentLisa Williams
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Image segmentation is an important task in computer vision and object recognition. Since
fully automatic image segmentation is usually very hard for natural images, interactive schemes with a
few simple user inputs are good solutions. In image segmentation the image is dividing into various
segments for processing images. The complexity of image content is a bigger challenge for carrying out
automatic image segmentation. On regions based scheme, the images are merged based on the similarity
criteria depending upon comparing the mean values of both the regions to be merged. So, the similar
regions are then merged and the dissimilar regions are merged together.
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This document provides an overview of medical image processing. It discusses the importance of medical imaging for diagnosing and treating patients without harm. It covers various image modalities like X-ray, CT, MRI, ultrasound. It also discusses low, intermediate and high level image processing including preprocessing techniques like enhancement, filtering and registration. Segmentation, feature extraction and classification are covered for image analysis. Performance measures and applications of medical image processing are also summarized.
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This document is a project report on noise reduction in images using filters. It was submitted by 4 students - Priya M, Dondla Leela Vasundhara, Inderpreet Kaur, and Nisha Mathew - to the Department of Computer Science at Mount Carmel College in Bengaluru, India. The report discusses image processing techniques including different types of noise, noise reduction methods, and the use of filters to reduce noise in digital images.
Cellular Neural Networks are used to identify abnormalities in medical images like MRI in real time. The algorithm compares input images to a standard normal image and extracts pixel values that differ, representing abnormalities. It then uses median filtering and an inpainting technique to clean and fill in the extracted abnormality image for clearer viewing. The simple and efficient CNN algorithm allows for fast real-time processing of medical images to aid in quicker diagnosis.
Intensity Enhancement in Gray Level Images using HSV Color Coding TechniqueIRJET Journal
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Diabetic retinopathy also known as diabetic eye disease, is when damage occurs to the retina
due to diabetes. It can eventually lead to blindness. By analyzing and detecting vasculature structures
in retinal image the diabetes can be detected in advanced stages by comparing its states of retinal
blood vessels. In blood vessel classification approach computer based retinal image analysis can be
used to extract the retinal image vessels. Stationary wavelet transform (SWT) are used to extract the
features from the fundus image and classification can be performed using Support Vector
Machine(SVM). SVM has become an essential machine learning method for the detection and
classification of particular patterns in medical images. It is used in a wide range of applications for its
ability to detect patterns in experimental databases. If the vessels are present, then it is extracted by
using segmentation. Mathematical morphology and K-means clustering is used to segment the vessels.
To enhance the blood vessels and suppress the background information, smoothing operation can be
performed on the retinal image using mathematical morphology. Then the enhanced image is
segmented using K-means clustering algorithm to detect the diseases easily.
MRIIMAGE SEGMENTATION USING LEVEL SET METHOD AND IMPLEMENT AN MEDICAL DIAGNOS...cseij
Image segmentation plays a vital role in image processing over the last few years. The goal of image segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using level set method for segmenting the MRI image which investigates a new variational level set algorithm without re- initialization to segment the MRI image and to implement a competent medical diagnosis system by using MATLAB. Here we have used the speed function and the signed distance function of the image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising results by detecting the normal or abnormal condition specially the existence of tumers. This system will be applied to both simulated and real images with promising results
MRI Image Segmentation Using Level Set Method and Implement an Medical Diagno...CSEIJJournal
Image segmentation plays a vital role in image processing over the last few years. The goal of image
segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual
surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using
level set method for segmenting the MRI image which investigates a new variational level set algorithm
without re- initialization to segment the MRI image and to implement a competent medical diagnosis
system by using MATLAB. Here we have used the speed function and the signed distance function of the
image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique
and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising
results by detecting the normal or abnormal condition specially the existence of tumers. This system will be
applied to both simulated and real images with promising results.
This document discusses image processing. It begins by defining image processing as the conversion of an image to digital form and performing operations to enhance the image or extract useful information. The main steps are importing, analyzing/manipulating, and outputting the image. Types of image processing include analog and digital. Applications include computer vision, medical imaging, and document processing. Advantages include manipulation and compact storage, while limitations include cost, time consumption, and lack of professionals. The document provides details on several image processing techniques and applications.
How To Write An Effective Essay Introd. Online assignment writing service.Melissa Moore
The document provides instructions for using the HelpWriting.net service to have essays written. It outlines a 5-step process: 1) Create an account with a password and email; 2) Complete a form with assignment details and deadline; 3) Choose a writer based on qualifications and reviews; 4) Review the completed paper and authorize payment; 5) Request revisions to ensure satisfaction. It emphasizes the site's guarantee of original, high-quality work or a full refund.
Literary Essay Example Literary Essay, Poem Analysis, LiteraryMelissa Moore
The document provides instructions for requesting and completing an assignment writing request through the HelpWriting.net website. It outlines a 5-step process: 1) Create an account with a password and email. 2) Complete an order form with instructions, sources, and deadline. 3) Review bids from writers and select one. 4) Review the completed paper and authorize payment. 5) Request revisions until satisfied with the work. The document emphasizes that original, high-quality content will be provided, with refunds offered for plagiarized work.
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features from the fundus image and classification can be performed using Support Vector
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segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual
surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using
level set method for segmenting the MRI image which investigates a new variational level set algorithm
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system by using MATLAB. Here we have used the speed function and the signed distance function of the
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Alfred Binet was a French psychologist who invented the first intelligence test, known as the Binet-Simon Scale, in 1905. The test assessed verbal and performance abilities in children and is considered the precursor to modern IQ tests. Binet believed intelligence was multifaceted and could be improved with education and experience. He helped establish the field of educational psychology and focused on studying exceptional children.
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This document discusses demonstrative communication, which involves sending and receiving verbal and nonverbal messages. Verbal communication includes oral and written messages, while nonverbal communication consists of facial expressions, body language, eye contact, and gestures. Nonverbal communication can reinforce verbal messages, stand alone, and convey meaning without words through body language and gestures. It discusses how nonverbal cues like proximity, touching, and clothing can express attitudes and messages. While nonverbal communication allows for self-expression, it may lack complexity compared to verbal exchanges.
Find Someone To Write My College Paper, World WarMelissa Moore
This document discusses steps for requesting someone to write your college paper through the website HelpWriting.net. It outlines a 5-step process: 1) Create an account with a password and email, 2) Complete an order form providing instructions, sources, and deadline, 3) Review bids from writers and choose one, 4) Review the completed paper and authorize payment, 5) Request revisions until satisfied. The document emphasizes that original, high-quality content will be provided, with refunds offered for plagiarized work.
How To Write A Research Paper Fast. Online assignment writing service.Melissa Moore
This document discusses the organizational culture at Uber and how culture and conflict intersected. Uber's culture prioritized innovation, results and pushing boundaries, which helped transform transportation but also lacked standards and accountability, allowing inappropriate behavior. While convenient for customers and employees, Uber's culture damaged its reputation through conflicts over issues like treatment of employees and accountability.
Professional Essay Writers For Hire - YouTubeMelissa Moore
The document discusses the psychodynamic theory approach used by Dr. Paul Weston in treating Sophie, a teenage gymnast seeing him after an accident. While Paul largely uses psychodynamic techniques, drawing on the patient's past and unconscious motivations, he also combines other universal therapeutic qualities and some cognitive methods. Key examples throughout Sophie's sessions illustrate Paul's emphasis on psychodynamics but blended approach. His goal is to convey reliable support to help Sophie understand her emotions, without criticism, in line with psychodynamic principles.
Blank Writing Paper With Box For Illustration (With ImagesMelissa Moore
The document provides instructions for completing a stop words and stemming assignment in Python. It includes sample code to import a stemmer, define stop words and punctuation, stem tokens, and remove tokens containing stop words or punctuation. The summary discusses tracking term and document frequencies, building an inverted index, and calculating TF-IDF weights. It also notes the code outputs stopped word and punctuation tokens to .dat files.
Top 10 IELTS Writing Tips And Tricks To GeMelissa Moore
This document discusses vasculopathy in systemic sclerosis. Systemic sclerosis is a heterogeneous disease of unknown etiology characterized by consistent vascular involvement. A key feature of systemic sclerosis is vascular damage including endothelial cell dysfunction, intimal thickening, and luminal narrowing or occlusion of small and medium sized arteries.
How To Captivate Journal Readers. Online assignment writing service.Melissa Moore
The document provides instructions for requesting writing assistance from HelpWriting.net. It outlines a 5-step process: 1) Create an account with a password and email; 2) Complete a 10-minute order form providing instructions, sources, and deadline; 3) Review bids from writers and select one; 4) Review the completed paper and authorize payment; 5) Request revisions to ensure satisfaction. The service aims to provide original, high-quality content and offers refunds for plagiarized work.
Essay About My School. Online assignment writing service.Melissa Moore
This document provides instructions for requesting and completing an assignment writing request through the HelpWriting.net website. It outlines a 5-step process: 1) Create an account with a password and email. 2) Complete a 10-minute order form providing instructions, sources, and deadline. 3) Review bids from writers and select one. 4) Review the completed paper and authorize payment. 5) Request revisions to ensure satisfaction, with a full refund option for plagiarized work.
Writing Prompt Volcano Abcteach. Online assignment writing service.Melissa Moore
The document provides instructions for requesting writing assistance from HelpWriting.net. It outlines a 5-step process: 1) Create an account with a password and email. 2) Complete a 10-minute order form providing instructions, sources, and deadline. 3) Review bids from writers and choose one based on qualifications. 4) Review the completed paper and authorize payment if satisfied. 5) Request revisions to ensure satisfaction, with a refund offered for plagiarized work. The document promises original, high-quality content to meet customer needs.
Super Hero Themed Writing Papers By Erica DodsonMelissa Moore
Fabletics is an online retailer of women's activewear and fitness clothing founded in 2013 by Kate Hudson and other executives. The company aims to provide stylish, high-quality activewear at affordable prices, filling a gap in the market. Customers can shop for clothing designed for workouts, yoga, and everyday wear. Fabletics uses a monthly membership and personalized recommendations to drive sales of new arrivals each month at competitive prices.
Contract Law Essay Example. Contract Law Essay ExamMelissa Moore
This document discusses the importance of bioscience in everyday life. Bioscience, also called biology and life science, is the study of living organisms from microorganisms to large animals. It affects many aspects of life including health, agriculture, food, industry, and energy. Bioscience has led to innovations like pharmaceuticals, agrochemicals, and disease diagnostics. Some areas of bioscience discussed are biochemistry, which studies the chemical reactions of life, and biomedical engineering, which develops medical procedures and devices. Overall, bioscience has improved human lives for centuries through advances in food production, medicine, and more.
Essay Writing Basics. Online assignment writing service.Melissa Moore
The document provides steps for using the writing service HelpWriting.net, including creating an account, submitting a request for paper writing help by completing an order form, reviewing bids from writers and choosing one, authorizing payment after receiving the paper, and requesting revisions if needed. The service offers original, plagiarism-free content and refunds if plagiarized work is provided.
Self Evaluation Examples Template BusinessMelissa Moore
This document provides instructions for creating an account and submitting a request for an assignment writing service on the website HelpWriting.net. It outlines a 5-step process: 1) Create an account with an email and password. 2) Complete a form with assignment details. 3) Review bids from writers and select one. 4) Review the completed paper and authorize payment. 5) Request revisions if needed, and the site offers refunds for plagiarized work.
The Science of Learning: implications for modern teachingDerek Wenmoth
Keynote presentation to the Educational Leaders hui Kōkiritia Marautanga held in Auckland on 26 June 2024. Provides a high level overview of the history and development of the science of learning, and implications for the design of learning in our modern schools and classrooms.
How to Create a Stage or a Pipeline in Odoo 17 CRMCeline George
Using CRM module, we can manage and keep track of all new leads and opportunities in one location. It helps to manage your sales pipeline with customizable stages. In this slide let’s discuss how to create a stage or pipeline inside the CRM module in odoo 17.
Information and Communication Technology in EducationMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 2)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐈𝐂𝐓 𝐢𝐧 𝐞𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧:
Students will be able to explain the role and impact of Information and Communication Technology (ICT) in education. They will understand how ICT tools, such as computers, the internet, and educational software, enhance learning and teaching processes. By exploring various ICT applications, students will recognize how these technologies facilitate access to information, improve communication, support collaboration, and enable personalized learning experiences.
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐢𝐧𝐭𝐞𝐫𝐧𝐞𝐭:
-Students will be able to discuss what constitutes reliable sources on the internet. They will learn to identify key characteristics of trustworthy information, such as credibility, accuracy, and authority. By examining different types of online sources, students will develop skills to evaluate the reliability of websites and content, ensuring they can distinguish between reputable information and misinformation.
Post init hook in the odoo 17 ERP ModuleCeline George
In Odoo, hooks are functions that are presented as a string in the __init__ file of a module. They are the functions that can execute before and after the existing code.
Cross-Cultural Leadership and CommunicationMattVassar1
Business is done in many different ways across the world. How you connect with colleagues and communicate feedback constructively differs tremendously depending on where a person comes from. Drawing on the culture map from the cultural anthropologist, Erin Meyer, this class discusses how best to manage effectively across the invisible lines of culture.
Images as attribute values in the Odoo 17Celine George
Product variants may vary in color, size, style, or other features. Adding pictures for each variant helps customers see what they're buying. This gives a better idea of the product, making it simpler for customers to take decision. Including images for product variants on a website improves the shopping experience, makes products more visible, and can boost sales.
General Review Of Algorithms Presented For Image Segmentation
1. General Review of Algorithms Presented for Image Segmentation
Image segmentation commonly known as partitioning of an image is one of the intrinsic parts of any
image processing technique. In this image pre processing step, the digital image of choice is
segregated into sets of pixels on the basis of some predefined and preselected measures or standards.
There have been presented many algorithms for segmenting a digital image. This paper presents a
general review of algorithms that have been presented for the purpose of image segmentation.
Segmenting or dividing a digital image into region of interests or meaningful structures in general
plays a momentous role in quite a few image processing tasks. Image analysis, image visualization,
object representation are some of them. The prime objective of segmenting a digital image is to
change its representation so that it looks more expressive for image analysis. During the course of
action in image segmentation, each and every pixel of the image segmentation is assigned a label or
value. The pixels that share the same value also share homogeneous traits. The examples can include
color, texture, intensity or some other features. Image segmentation can be defined as the technique
to divide the an image f (x, y) into a non empty subset f1, f2, ...., fn which is continuous and
disconnected. This step contributes in feature extraction. There are quite a few applications where
image segmentation plays a pivotal role. These applications vary from image filtering, face
recognition, medical imaging
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2.
3. A Literature Study Of Watermarking Techniques On Contrast...
A LITERATURE STUDY OF WATERMARKING TECHNIQUES ON CONTRAST
ENHANCEMENT OF COLOR IMAGES Rajendra Kumar Mehra1, Amit Mishra2 1. Dept of ECE,
M–TECH student, VITS, JABALPUR, M.P., INDIA, 2. Dept of ECE, H.O.D., VITS, JABALPUR,
M.P., INDIA. ABSTRACT: In this paper a watermarking method with contrast enhancement is
presented for digital images. Digital Watermarking is a technology which is used to identify the
owner, distributor of a given image. If the watermarked images is low contrast & poor visual quality
or due to poor illumination in some imaging system, the contrasts of the obtained images are often
needs to be improve. In recent years, digital watermarking plays a vital role in providing the
appropriate solution and various researches have been carried out. In this paper, an extensive review
of the literature related to the color image watermarking is presented together with contrast
enhancement by utilizing an assortment of techniques. This method outperforms other present
algorithm by enhancing the contrast of images well without introducing undesirable artifacts.
KEYWORDS: Watermarking, Histogram equalization, CLAHE, CAHE, PSNR, MSE. I.
INTRODUCTION DIGITAL image watermarking has become a necessity in many applications
such as data authentication, broadcast monitoring on the Internet and ownership identification.
Various watermarking schemes have been proposed to protect the copyright information. There are
three indispensable, yet contrasting requirements for a
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4.
5. A Short Note On Diabetic Retinopathy ( Dr ) Is The...
Abstract– Diabetic Retinopathy (DR) is the deterioration of human eye as a result of increase in the
blood glucose level. Longer the patient has DR, higher the chance to develop purblind. The robust
detection of lesions in digital colour fundus images is an important step in the development of
automated screening system for diabetic retinopathy. In this work a novel method is introduced for
automatic detection of red lesions in the fundus image. A new set of shape features extracted from
the detected red lesion called the dynamic shape features that differentiate between the lesions and
vessel segments. The detected lesion candidates are classified using dynamic shape features based
on the medical values. The simulation analysis indicates that the proposed work is better than the
previous works in terms of accuracy, sensitivity, precision and specificity.
Keywords: Diabetic retinopathy, Fundus, Lesions, Dynamic shape features, Retina
Introduction
Diabetic Retinopathy (DR) affects the diabetic patients. Generally diabetics are of three types Type
I, II and III. The Type I diabetic is due to the genetic predisposition, Type II diabetic which usually
affects the adults. This is owing to over weight of children beyond their age limit and Type III is
seen only in pregnant women. The patients with Type I diabetics will only suffer from DR which
influence the retina. This leads the way to damage of retina and finally blindness.
DR is caused by red lesion which is composed of
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6.
7. Definition Of Image Quality Of Digital Imaging
CHAPTER 1: INTRODUCTION
1.1. Background
1.1.1. Limits to image quality
Digital imaging systems have a lot of applications including digital photography for recreational and
commercial purposes, electronic surveillance, satellite imaging and ground based geographic
information systems, medical imaging systems like computed tomography (CT) and magnetic
resonance imaging (MRI), forensics and even particle physics.
In many applications of digital imaging, a high quality image is required to allow human
interpretation or machine perception. Image quality is defined in terms of spatial resolution, pixel
resolution, temporal resolution and spectral resolution. For our application, we are interested in
spatial resolution.
Spatial resolution is measured in terms of pixel density and refers to the number of pixels used per
unit area to construct the image. It defines the minimum separation distance for 2 features in the
original scene for them to be distinguishable. Spatial resolution is determined by the density of
imaging sensors. Imaging sensors are charge coupled devices (CCD) or CMOS active pixel sensors,
arranged in a two dimensional array. The higher the sensor density, the higher the spatial resolution.
Higher sensor density can be achieved either by reducing the sensor size or increasing the size of the
chip carrying the sensors. Increasing the pixel density is limited by:
1. Reducing the size of sensors results in less light falling on the sensors, thus generating shot
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8.
9. Data Processing : Image Processing
1
1. INTRODUCTION
1.1. Introduction to broad area of research
1.1.1. Image processing: Image processing is a methodology to perform some operations on an
image, so as to urge an enhanced image or to extract some helpful data from it. It is treated as an
area of signal processing where both the input and output signals are images. Images are portrayed
as two dimensional matrix, and we are applying already having signal processing strategies to input
matrix. Images processing finds applications in several fields like photography, satellite imaging,
medical imaging, and image compression, just to name a few. Basically Image processing includes
the following steps: Reading the image via image acquisition tools like cameras, caners etc.
Analysing and manipulating the acquired image to have enhanced quality and locate the data of
interest; Output in which result can be altered image or report that is based on image analysis.
Originally image processing is proposed for space exploration and biomedical field. But later on
with the increase in use of digital images in everybody's lives it considered as powerful tool for
arbitrarily manipulating images to gain useful information. It defined as the means of conversion
between human visual system and digital imaging devices.The main purpose of image processing
are listed below: 1. Visualization – Observe the objects which are not visible. 2. Image sharpening
and restoration – To increase quality of image. 3. Image retrieval –
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10.
11. Chapter 1: Camera Modeling And Computer Video
CHAPTER (5)
CAMERA MODELING AND COMPUTER VISION Introduction
As mentioned before the computer vision role in this study is to identify and locate the desired parts
on the system's conveyor. Fig. (5.1) shows the block diagram for this process. Fig. (5.1) computer
vision block diagram
The camera streaming a real time video to the vision algorithm. MATLAB/SIMULINK of
MathWorks–Company is used to analysis the video streaming and detect the parts position in pixels.
The camera model and camera calibration equations then transform the pixel positions to a real
world (x, y) position related to the robot reference coordinate (home position). Robot inverse
kinematic equations take the (x, y) positions and convert them to a number of steps to ... Show more
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The field of digital image processing refers to processing digital images by means of a digital
computer [11]. Image coordinates
Assume that an image f(x,y) is sampled so that the resulting image has M rows and N columns so,
the image size is M x N. The values of the coordinates are discrete quantities. The image origin is
usually defined to be at (x, y) = (0, 0). The next coordinate values along the first row of the image
are (x, y) = (0, 1). The notation (0, 1) is used to signify the second sample along the first row. It does
not mean that these are the actual values of physical coordinates when the image was sampled. Fig.
(5.3) shows this coordinate convention, where x ranges from 0 to (M–1) and y from 0 to (N–1) in
integer increments [11].
Equation (5.1) represents the digital image with respect to the image coordinate system discussed
above [11]. f(x,y)=[■(■(f(0,0)@f(1,0))&■(f(0,1)@f(1,1))&■(f(0,N–1)@f(0,N–1))@⋯&⋯&⋯
@f(M–1,0)&f(M–1,1)&f(M–1,N–1))] (5.1)
Fig. (5.3) Digital image coordinate conventions [11].
Both sides of this equation are equivalent ways of expressing a digital image quantitatively. The
right side is a matrix of real numbers. Each element of this matrix is called an image element,
picture element, or pixel. The term pixel is used throughout the rest of this study [11]. Camera
Modeling
Introduction
In this section the basic camera model is developed based on [12].as a
14. Using Image Acquisition Is The Input Text Document
1. INPUT TEXT DOCUMENT Image acquisition is the input text document. Acquire image of any
document with the help of camera or scanner. Image acquisition is used to Acquire/obtain the image
of document in color, gray level or binary format. 2. PRE–PROCESSING These are the pre–
processing steps often performed in OCR 1. Binarization The simplest way to use image
binarization is to choose a threshold value, and classify all pixels with values above this threshold as
white, and all other pixels as black. Selecting proper threshold is very important task. In many cases,
finding one threshold compatible to the entire image is very difficult, and in many cases even
impossible. Therefore, adaptive image binarization is needed where an optimal threshold is chosen
for each image area. Binarization is processing of converting color image in to binary image. In
binarization, first we are converting color image in to Gray scale image using following formula.
[2]There are various Binerization methods and in that various different algorithm used are as
follows. Color image is converted into gray image and following algorithms are applied on gray
scale image for converting it in to binary image. Niblack Algorithm It is local thresholding
algorithm. Local thresholding algorithms give good results for document because it calculate
different threshold for different part of the image, considering pixel value. Niblack's algorithm
calculates a pixel–wise threshold by sliding a
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15.
16. Image Processing And Image Enhancement
Abstract
Image enhancement is to process an image, in order to make the result more suitable than original
image for specific application. i.e. the image is enhanced.For that many image enhancement
techniques are used. Appropriate choice of such techniques is very important.Image Enhancement is
simple and it's the area based on digital image processing techniques. It improves the quality of the
images by working with the existing data.
Keywords:
Image processing, Image enhancement
1. Introduction
Image processing is the input image which is converted from one form to another. Digital image
processing plays a vital role in real world applications. Before processing an image, it must be
converted into a digital form.
One of part of the image processing is the image enhancement. The main objective of image
enhancement is to modify attributes of an image to make it more suitable for a given task. Here, one
or more attributes of the image get modified. The main purpose of image enhancement is to bring
out details which are hidden in an image, or to increase the contrast in a low contrast image. It
produces an output image that is better than the original image by changing the pixel's intensity of
the input image. Image enhancement is applied in many fields. For example, medical image
analysis, analysis of images from satellites, Aerial imaging, Satellite imaging, Digital camera
applications, Remote sensing etc.
2.Enhancement Techniques
[1]The enhancement methods are mainly
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17.
18. Ultrasound Images Of The Patients Suffering From...
Abstract–This paper presents the approach to analyze the ultrasound images of the patients suffering
from Cholelithiasis. The occurrence of Cholelithiasis is the commonest biliary disease to be reported
in India. Our research is aimed to apply the potential of image processing in diagnosing the presence
of gall bladder stones. In this paper we propose a technique, a combination of preprocessing
morphological techniques and Entropy calculation of the pixels representing gallstones in the gall
bladder.
Keywords–Cholelithiasis, entropy calculation, image processing, morphological techniques,
preprocessing
INTRODUCTION
Gallstone diseases are one of the most common biliary diseases, demanding a great progress in
understanding the gallstones. The historical background of Cholelithiasis helps the researchers for
easy classification of Gallstones. According to Japanese, there are two types of Gallstones are
widely discussed: the Cholesterol stone, which is further of three types, the Pure Cholesterol stone,
the Combination stone and the Mixed stone. Second is the Pigment stone, which is further classified
as the Black stone and the Calcium Bilirubinate stone. The division line between Cholesterol and the
pigment stones depends upon the proportion of Cholesterol. If the proportion of cholesterol is equal
to or more than 70% then the stone is a Cholesterol stone; otherwise the stone is a pigment stone
with calcium bilirubinate as its principal constituent. The purpose of this
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19.
20. Analysis Of Underwater Image For Future Requirement Using
Analysis of underwater image for future requirement using
Wavelet Transform analysis
Abstract:
Optical information is transmitted in the form of digital images is becoming a large method of
communication in the modern age but still the images reach after transmission is often depraved
with noises so the received images demand processing before it can be used in application. Our
motive is that to eliminate the noise from images that is underwater images also improve the image ,
underwater images consist of different kinds of noises like random noise, speckle noise, Gaussian
noise, salt and pepper noise, Brownian noise etc. Image De–noising is involved manipulation of
images data to produce a visually high quality, images processing of improving the quality of
images by enhancing its features. The underwater image processing area has accepted appreciable
attention within the last decades so using some proper kind of filter it is possible. The filter we will
employ is a bilateral filter for smoothing the images. It is required because of a lot researchers like
forensic department, argeologiest geologist, and underwater marine lab and underwater inside hydro
lab and so on, for their research activity. The underwater images have poor image condition. First it
uses some preprocessing methodology which is to be complete before wavelet threshold de–nosing.
Then it will use CLAHE method for image enhancement along with wavelet transform then we get
some adaptive output and the images
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21.
22. Image And Image Of Image Enhancement
CHAPTER 1
INTRODUCTION
Image processing refers to the construction of an image for further analysis and use. Image taken by
a camera or same techniques are not actual in a form that can be used by image analysis process.
The technique involves in image enhancement need to be simplified, enhanced, filtered, altered,
segmented or need improvement to reducing noise, etc. Image processing is the collection of
routines and techniques that alter, improve, enhance or simplify an image. Image enhancement is
one of the important parts of digital image processing where image undergo for visual inspection or
for machine analysis without knowledge of its source of degradation. The processes involve to bring
out specific application of an image so that the result is more suitable that the original image. Image
can be enhanced in various ways such as contrast enhancement, intensity, density slicing, edge
enhancement, removal of noise, and saturation transformation.[1]
Over several past years, contrast image enhancement has generated across many applications like
robot sensing, electronic products, fault detection, medical image analysis, etc. Thus, increasing in
popularity of contrast enhancement of images has forces researchers to study their enhancement
techniques and their effectiveness for the interpretability or perception of human viewers. Contrast
enhancement is a vital part of various fields, such as X–ray image analysis, biomedical image
analysis, machine vision where pixel
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23.
24. The Human Face Action Recognition System
Abstract– In this paper we implement the Human Face Action Recognition System in Wireless
Sensor Network. Detecting movements of human is one of the key applications of wireless sensor
networks. Existing technique is detecting movements of a target using face tracking in wireless
sensor network work efficiently but here we implementing face action recognition system by using
image processing and algorithms with sensors nodes. Using sensor node we can collect the
information, data about human facial expressions and movements of human body and comparing old
data captured by sensors to the new capturing data, if data is match then we can say that detecting
human is same as early. Here we create new framework for face tracking and its movements
capturing, achieve tracking ability with high accuracy using Wireless Sensor
Networks. We use the Edge Detection Algorithms, Optimal Selection Algorithm, Image Processing
Technique, Action Recognition, the big data analysis. Using java language, various types of sensors.
Keywords– Mobile Network, Ad–hoc Network, Routing Protocol, Sensor Networks, Surveillance
system, Pattern Recognition.
I. Introduction Face Recognition is a technology to extract facial features by computer and a
technique for authentication according to the characteristics of these features. Face Recognition
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25.
26. The Image Processing Techniques For Breast Cancer
Abstract– In recent years the image processing techniques are used commonly in various medical
areas for improving earlier detection and treatment stages, in which the time span or elapse is very
important to discover the disease in the patient as possible as fast, especially in many tumours such
as the lung cancer, breast cancer. This system generally first segments the area of interest (lung) and
then analyses the separately obtained area for nodule detection in order to examine the disease. Even
with several lung tumour segmentations have been presented, enhancing tumour segmentation
methods are still interesting because lung tumour CT images has some complex characteristics, such
as large difference in tumour appearance and uncertain tumour boundaries. To address this problem,
tumour segmentation method for CT Images which separates non–enhancing lung tumours from
healthy tissues has been carried out by clustering method. The proposed method uses pre–processing
technique that remove unwanted artifacts using median and wiener filters. Initially, the segmentation
of the CT images has been carried out by using K– Means clustering method. To the clustered result,
EK–Mean clustering is applied . Further the features like entrpy, Contrast, Correlation,Homogenity
and the area are extracted from the tumorous part of Fuzzy Ek– Means segmented Image. For
feature extraction, statistic method called Gray Level Co–occurrence Matrix (GLCM). Classification
is done by using the
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27.
28. Content-Based Image Retrieval Case Study
INTRODUCTION
Pertaining to the tremendous growth of digitalization in the past decade in areas of healthcare,
administration, art & commerce and academia, large collections of digital images have been created.
Many of these collections are the product of digitizing existing collections of analog photographs,
diagrams, drawings, paintings, and prints with which the problem of managing large databases and
its repossession based on user specifications came into the picture. Due to the incredible rate, at
which the size of image and video collection is growing, it is eminent to skip the subjective task of
manual keyword indexing and to pave the way for the ambitious and challenging idea of the
contend–based description of imagery.
Many ... Show more content on Helpwriting.net ...
In this paper, we will be looking at different methods for comparative study of the state of the art
image processing techniques stated below (K means clustering, wavelet transforms and DiVI
approach) which consider attributes like color, shape and texture for image retrieval which helps us
in solving the problem of managing image databases easier.
Figure 1: Traditional Content–Based Image Retrieval System
LITERATURE SURVEY–
DiVI– Diversity and Visually–Interactive Method
Aimed at reducing the semantic gap in CBIR systems, the Diversity and Visually–Interactive (DiVI)
method [2] combines diversity and visual data mining techniques to improve retrieval efficiency. It
includes the user into the processing path, to interactively distort the search space in the image
description process, forcing the elements that he/she considers more similar to be closer and
elements considered less similar to be farther in the search space. Thus, DiVI allows inducing in the
space the intuitive perception of similarity lacking in the numeric evaluation of the distance
function. It also allows the user to express his/her diversity preference for a query, reducing the
effort to analyze the result when too many similar images are returned.
Figure 2: Pipeline of DiVI processing embedded in a CBIR–based tool.
Processing of
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29.
30. A Literature Study Of Robust Color Image Watermarking...
A LITERATURE STUDY OF ROBUST COLOR IMAGE WATERMARKING ALGORITHM
PANKAJ SONI 1, VANDANA TRIPATI2, RITESH PANDEY3
1. Dept of ECE, ME student, G.N.C.S.G.I., JABALPUR, M.P., INDIA,
2–Dept of ECE, Asst. Prof., G.N.C.S.G.I., JABALPUR, M.P., INDIA,
2–Dept of ECE, Asst. Prof., G.N.C.S.G.I., JABALPUR, M.P., INDIA,
ABSTRACT: Digital Watermarking is a technology which is used to identify the owner, distributor
of a given image. In recent years, digital watermarking plays a vital role in providing the appropriate
solution and various researches have been carried out. In this paper, an extensive review of the
literature related to the color image watermarking is presented together with compression by
utilizing an assortment of techniques. The proposed method should provide better security while
transferring the data or messages from one end to the other end. The main objective of the paper is
to hide the message or a secret data into an image which acts as a carrier file having secret data and
to transmit to the intention securely. The watermark can be extracted with minimum error. In terms
of PSNR, the visual quality of the watermarked image is exceptional. The proposed algorithm is
robust to many image attacks and suitable for copyright protection applications.
KEYWORDS: Watermarking, Discrete wavelet transform, Discrete Cosine Transform, PSNR, MSE.
I. INTRODUCTION
DIGITAL image watermarking has become a necessity in many applications such as data
authentication, broadcast
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31.
32. Essay On Homomorphic Filter
Abstract
In spite of the significant research conducted on multiplicative noise removal using homomorphic
filter, the development of efficient de–noising methods is still one of the most important tasks. Noise
effects badly on the signal. In many times signals are consolidated in a complicated way. Sending
visual digital images is one of the main problems that we face in modern data communication
network. Sometimes the image may not be received from the source by the receiver and it may get
interrupted with noise. To get high quality image we must reduce the noise in image which involves
the manipulation of the image data. For noise reduction we have various solutions are available. We
need to design a filter that will handle most of the ... Show more content on Helpwriting.net ...
Content
List of figures................................................................................................
Abstract.........................................................................................................
Introduction...................................................................................................
Operation......................................................................................................
Results...........................................................................................................
Conclusion.....................................................................................................
References.....................................................................................................
Introduction
Chapter 1:
Image processing:
Image processing is a signal processing where it's input signal is image. In image Processing system
we treat the images as 2D signals. We have two types of image processing which is digital and
analog. Analogue image processing used in hard copies while digital image processing use
computers for the manipulation of the digital images. Digital image processing have many types like
binary, RGB and grayscale.
Chapter 2:
Noise:
Noise is a random signal which affects badly on the wanted signal. Due to noise the signal may not
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33.
34. Digital Image Of A Optical Signature Recognition
3.4.1 Offline Signature Recognition
In this type of recognition, the text is not recognized at the same time as it is produced but after the
user has finished writing. In this case, the text is originally written on a surface such as paper and
from there on it is recognized by the computer by scanning the surface. In the scanned Signature is
first stored digitally in grey scale format. bitmap image, and then further processing is done on it to
have a good recognition accuracy.
Features for recognition are enhanced and extracted from the stored bitmap image by using digital
image processing. Offline signature recognition is known as Optical Signature Recognition (OCR),
because the image of writing is converted into bit pattern by an optically digitizing device such as
optical camera or scanner. The recognition is done on this bit pattern data for machine–printed or
hand–written text [3]. Recognition of machine printed signatures is also a part of Optical Signature
Recognition. In offline, methods are less suitable for man–machine communication because no real
time interactivity is present. It is suitable for automatic conversion of paper documents to electric
documents, which then may be interpreted by computers. Some applications of the off–line
recognition are large–scale data processing such as postal address reading; check sorting, office
automation for text entry automatic inspection and identification [11].
3.4.2 Online Signature Recognition
In contrast to the offline
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35.
36. Character Recognition By Machines, An Innovative Way By...
Abstract–Character Recognition by machines is an innovative way by which the dependence on
manpower is reduced. Character recognition provides a reliable alternative of converting manual
text into digitized format. Now–a–days, as technology becomes integral part of human life, many
applications have enabled the incorporation of English OCR for real time inputs. The advantages
that the English alphabet has is its simplicity offered by less number of letters i.e. 26 and easier
classification due to the concept of lowercase and uppercase. If we consider Devnagari script in this
scenario, we will come across myriad hurdles because this script lacks the simplicity of English. The
concept of fused letters, modifiers, shirorekha and spitting similarities in some letters make
recognition difficult. Also, character recognition for handwritten text is far more complex than that
for machine printed characters. This is because of the versatility and different writing techniques
adopted by people. The direction of strokes, pressure applied on writing equipments, quality of
writing equipment and the mentality of the writer itself highly affects the written text. These
problems when combined with the intricate details of Devnagari script, the complications in
constructing a HCR of this script are increased. The proposed system focuses on these two issues by
adopting Hough transform for detecting features from lines and curves. Further, for classification,
SVM is used. These two methods
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37.
38. Image Processing Essay
Abstract: – A Measurement is must before going to the further calculations in various fields of work
or study. In order to find out something we definitely need some calculations. In different sectors,
determining exact size and shape are progressively becoming an issue and based on that the latency
is going up. As we cannot measure everything with a scale or a tape, we use some optical methods
of Image Processing. In this paper, we present an approach that can be used to determine the lengths
and some other degrees of measurements like diameter, spline, Caliper(perpendicular angle) etc. We
used mostly the Image Processing techniques because all the measurements are done on an Image.
We also use some other techniques like Euclidean ... Show more content on Helpwriting.net ...
The image can be enhanced to mark down the accurate end points. It actually can mark the end of a
single pixel which is almost invisible as a single pixel to the naked eye. A set of operations need to
be carried out respectively to achieve this. Initially the image need to be acquired and smoothened to
mark the pixel actually need to be. Then the neighborhood pixels collision should be eliminated
followed by the image segmentation. Finally, using the Euclidean algorithm the exact length can be
found.
II. IMAGE AQUSITION AND SMOOTHING: –
In Image Processing mostly the initial step will be the Image acquisition and smoothing. As the
input for the tool of any Image Processing technique is an image, the input image should be taken
and enhanced in all the ways possible. Enhancement involves smoothing the image, grey scaling,
removing the unwanted blur, differentiating the subject from background and so on. In this project,
for enhancing or smoothing the image we use the median filter. The median filter is non–linear
digital filtering technique where the noise reduction is the pre–processing step before going to the
further processing. Because the signal is big in the case of images, we chose median filter as it can
handle the larger signal and the run–time is literally less. The major advantage of the median filter is
the edge preservation. It processes each signal individually and replaces the edges of the pixel with
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39.
40. Optical And Analog Image Processing
In imaging science, image processing is processing of images using mathematical operations by
using any conformation of signal processing for which the input is an image, such as a picture or
video frame, the out turn of image processing may be either an image or a set of features or
parameters corrsponding to the image.Most image–processing techniques implicate treating the
image as a 2D signal and appealing worth signal–processing techniques to it.
Image processing usually refers to digital image processing, but optical and analog image processing
also are possible. This article is about general techniques that apply to all of them. The acquisition of
images (producing the input image in the first place) is referred to as imaging.
Closely related to image processing are computer graphics and computer vision. In computer
graphics, images are manually made from physical models of objects, environments, and lighting,
instead of being acquired (via imaging devices such as cameras) from natural scenes, as in most
animated movies. Computer vision, on the other hand, is often considered high–level image
processing out of which a machine/computer/software intends to decipher the physical contents of
an image or a sequence of images (e.g., videos or 3D full–body magnetic resonance scans).
In modern sciences and technologies, images also gain much broader scopes due to the ever growing
importance of scientific visualization (of often large–scale complex scientific/experimental
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41.
42. Types of Image Compression for Medical Imaging Essay
Medical imaging, as we all know, is the process of taking images of various parts of the human body
for diagnostic and surgical purposes. Some of the popular medical imaging modalities are X–ray
radiography, Magnetic resonance imaging, Medical ultrasound, Computed tomography etc. Since,
these images contain clinical data of extreme importance for treatment follow–ups and are acquired
at cost of radiation exposure, infrastructure, money and time involved. Thus, once acquired, the
medical imaging data should not be disposed off casually, instead it should be retained so that it can
be utilized for various medical applications and the chances of repeated testing can be minimized.
Also, maintaining electronic health records of patients serves ... Show more content on
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In other words an optimal compression ratio should be chosen so as to suit the needs of medical
examination, without compromising with its diagnostic value [2].
1.2 Types of Compression
Image compression can be classified into two types viz. lossless and lossy compression.
Lossless compression is the technique of reducing the size of an image without any virtual loss of
information. It is also known as reversible form of image compression since the image obtained
after compression and then decompression resembles the original one. Typical compression ratios
that can be achieved ranges from 1.5 to 3.6 [3].
Conversely, lossy or irreversible form of compression techniques are those in which some or the
other information is always lost. Though, lossy compression algorithms are capable of compressing
images at ratios much higher than that achieved from lossless compression thus, ensuring faster
rates of transmission and lesser storage space. However, the regenerated image is not guaranteed to
be an exact replica of the original image, as some data is lost permanently, which will cause error
during decompression. Typical compression ratios achieved may range from 5 to 50.
Though lossy data compression is often acceptable but the game is not that easy when it comes to
medical images. The data from medical imaging examination should possess certain requirements
for fidelity [3].
1.3 Barriers to image compression
Lossy compression:
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43.
44. Blood Count Literature Review
REVIEW ON IMAGE PROCESSING USED IN HAEMOTOLOGY
Abstract– In medical analysis blood cell count plays vital role. Variations in the count of blood cells
cause many diseases in the human body. For overall health assessment and diagnosis of many
disorders complete blood count is required. Abnormal increase or decrease in cell count indicates
that person has indispensable medical condition. The Complete Blood Count (CBC) is a blood test,
extensively used to check various disorders such as infections, allergies, problems with clotting,
anaemia, leukaemia etc. In order to perform CBC test, the blood film is stained and then imaged
with a transmission light microscope. Here the analysis of the blood sample is done manually in
order to count number of blood cells and also to identify disorders in blood samples through a
microscope. But it is a time consuming process and also leads to undesirable human error. In
essence, the goal of this review paper is to find out and validate the necessary image processing
steps and different methods and algorithms used to count blood cells on blood smear slides. This
paper aims to provide: mitigate problems posed by different conditions such as noisy and degraded
images; detect the overlapping cells; to differentiate RBCs ,WBCs and also platelets which are
present in a blood smear slide counting RBCs and WBCs and even platelets and also to detect the
disease related to blood.
INTRODUCTION
In early days microscopists have manually viewed
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45.
46. Digital Image Processing : A Multi Dimensional Visual...
ABSTRACT:
Face is a analyzable multi–dimensional visual model and processing a process model for face
recognition is challenging. This paper presents a methodological analysis for face identification
based on content explanation formulation of coding and decoding the face image. categorization
using the Euclidian distance. The content is to use the system for a particular face and separate from
a large number of stored faces with some real time variations as well. The Eigen face attack uses
particular faces with some real time variation. The Eigen face formulation uses principal
components analysis (PCA) algorithm for the acceptance of the images. It gives us prompt way to
insight the lower dimensional space.
Digital Image processing: ... Show more content on Helpwriting.net ...
The sampling theorem states that for a signal to be completely reconstruct able, it must satisfy the
following equation:
Were Ws=sampling frequency W = frequency of sampled signal
. To explain all of this, first consider the simple sinusoidal function given by f(x) = cos(x). Figure 1
shows a plot of this function and Fig. 2 shows a plot of its Fourier transform.
Figure 3 shows a truncated version of that function, and Fig.4 shows the equivalent Fourier
transform.
Figure 1. Cosine function with amplitude A and frequency of 1 Hz.
Figure 2. Power spectrum of the cosine function with amplitude A and frequency of 1 Hz. Figure 3.
Truncated cosine function. The truncation is in the variable x (e.g., time), not in the amplitude.
Figure 4. The power spectrum of the truncate cosine function is a continuous one, with maximum
values at the same points, like the power spectrum of the continuous cosine function.
This is called as folding. In the above fig4 shows that lower frequencies of signal contains most of
signal's powers. A standard analog filter transfer function may be given as
Where the damping factor of the filter and w is is its natural frequency. By cascading first and
second order filters, one of them will get higher order systems which have higher performances.
Bessel filters are used for high performance applications, this is because of two factors.
1) The damping factors
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47.
48. The Image Of Image Processing
1. INTRODUCTION
1.1. Introduction to broad area of research
Image processing:
Image processing is a methodology to perform some operations on an image, so as to urge an
enhanced image or to extract some helpful data from it. It is treated as an area of signal processing
where both the input and output signals are images. Images are portrayed as two dimensional
matrix, and we are applying already having signal processing strategies to input matrix. Images
processing finds applications in several fields like photography, satellite imaging, medical imaging,
and image compression, just to name a few. Basically Image processing includes the following
steps:
Reading the image via image acquisition tools like cameras, caners etc.
Analysing and manipulating the acquired image to have enhanced quality and locate the data of
interest;
Output in which result can be altered image or report that is based on image analysis.
Originally image processing is proposed for space exploration and biomedical field. But later on
with the increase in use of digital images in everybody's lives it considered as powerful tool for
arbitrarily manipulating images to gain useful information. It defined as the means of conversion
between human visual system and digital imaging devices.The main purpose of image processing
are listed below:
1. Visualization – Observe the objects which are not visible.
2. Image sharpening and restoration – To increase quality of image.
3. Image retrieval – finding
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49.
50. Taking a Look at Image Processing
Image Processing is a technique to enhance raw images received from cameras/sensors placed on
satellites, space probes and aircrafts or pictures taken in normal day–today life for various
applications. Various techniques have been developed in Image Processing during the last four to
five decades. Most of the techniques are developed for enhancing images obtained from unmanned
spacecrafts, space probes and military reconnaissance flights. Image Processing systems are
becoming popular due to easy availability of powerful personnel computers, large size memory
devices, graphics software's etc. The common steps in image processing are image scanning,
storing, enhancing and interpretation.
Image Processing is used in various applications such as,
Remote Sensing
Medical Imaging
Non–destructive Evaluation
Forensic Studies
Textiles
Material Science.
Military
Film industry
Document processing
Graphic arts
Printing Industry
1.1. METHODS OF IMAGE PROCESSING There are two methods available in Image Processing.
(1)Analog image processing
(2)Digital image processing
1.1.1. ANALOG IMAGE PROCESSING Analog Image Processing refers to the alteration of image
through electrical means. The most common example is the television image. The television
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51.
52. Statistical Analysis Of Early Detection Of Liver Cirrhosis
Statistical Analysis Of Early Detection Of Liver Cirrhosis Through Medical Image Processing
Megha Bahdauria1,Chetna Garg1, Dr. Saurabh Mukherjee2, K.F. Rahman2
1.Mtech Scholar, Department of Computer Science, Banasthali University, Rajasthan, India
2. Associate Professor, Department of Computer Science, Banasthali University, Rajasthan, India
Abstract:
Statistical operations provide the means of principle of solving the many type of problems which
require the uncertain information in cirrhosis. This paper discusses the statistical operations.
Computed Tomography, Magnetic Resonance Imaging, Ultrasound etc has been proved very helpful
in diagnosing liver cirrhosis. Cirrhosis is an endemic disease across the world that leads to observed
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To let the liver function properly it is important to detect cirrhosis in early stage. Now a days several
noninvasive imaging techniques have been developed recently for detection of liver cirrhosis such
as CT, USG, MRI. In this paper we have used CT scan images of liver cirrhosis and applied some
statistical operations on those CT images such as mean, median, standard deviation and mode.
II. Methodology: CT scans are challenging because of the different image characteristics that must
be considered. Here we will be considering the statistical features of a CT scan of liver which is
having liver cirrhosis as a disease. The methodology followed is given below:
Fig.1 Flow Chart of Methodology Used
(1).Image Acquisition : To get an image of which you want to extract some features.
(2).Image Preprocessing : It is common practice to perform preprocessing on acquired CT scan
images before extracting the features of images. Here we have applied the statistical operation on
the preprocessed images After acquiring the image various preprocessing methods can be apply. The
aim of this step is to improve the quality of the image that suppress unwanted distortion and enhance
the image features which is important for further processing. Such as increase or decrease
brightness, shape, contrast, remove the noise from the image.
(3).Statistical analysis : Image analysis
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53.
54. Content Based Image Compression Using Dct And Dwt Technique
CONTENT BASED IMAGE COMPRESSION USING DCT AND DWT TECHNIQUE
Abstract: Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are the most
known methods used in digital image compression. Wavelet transform has better efficiency
compared to Fourier transform because it describe any type of signals both in time and frequency
domain simultaneously. In this paper, we will discuss the use of Discrete Cosine Transform (DCT)
and Discrete wavelet transformation (DWT) based Image compression Algorithmand compare the
efficiency of both methods. We do the numerical experiment by considering various types of images
and by applying DCT and DWT–SPIHT to compress an image. We found that DWT yields better
result as compared to DCT.
In this paper, we will do comparison with discrete cosine transform (DCT) which is heart of JPEG
(Joint Photographic Experts Group) standard and widely used wavelet based image compression
algorithm set partitioning in hierarchical tree based on different performance measure such as Peak
to Noise Ratio (PSNR), Mean Square Error (MSE) and CR.
Keywords – Discrete Cosine Transform, Discrete Wavelet Transform, filters, Image Compression.
Introduction:
1.1 Image Processing
A digital image which is portrayed in a[m,n] which is described as a 2D discrete space is received
from a simple image a(x,y) in a constant space using sampling process which is known as a
digitalization. The 2D steady image a(x,y) can be separated into M rows and N
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55.
56. Evaluation Of Proposed Design And Necessary Corrective Action
Assignment No: 1
Title :
Review of proposed design and necessary corrective action is taking to consider and submit
publication/presentation details with review report.
Objectives :
1. Constructing a semantic taxonomy for the land–cover classification of satellite images. 2.
Classifying satellite images according to their types such as vegetation, building, water etc.
3. Implementing MapReduce for processing large amount of data (Satellite Images).
Introduction :
Satellite images play a major role in today's world in real–time event detection. These events may
vary from changing landforms, depleting glaciers to catastrophic events like earthquakes, tsunamis
and sand storms. The drastic changes after such events need to be monitored and capturing satellite
images for such event detection can be helpful. The idea behind this project is to detect the changing
landforms across different vegetations, store this data, classify it on the basis of certain specified
parameters and retrieve the classified data using well defined mechanisms. Segmentation and event
detection is highly scalable in satellite images. With the increasing need to have real–time, classified
data for specific applications there is an increasing need to store this chunk of data in a distributed
environment to have better access. The basic idea to is to capture the satellite images and store them
in a distributed environment. The environment to be chosen is Hadoop Distributed Environment.
Hadoop makes it
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57.
58. Digital Image And Its Effect On The Quality Of Image
Abstract: In image processing, noise reduction and restoration of image is expected to improve the
qualitative inspection of an image and the performance criteria of quantitative image analysis
techniques Digital image is inclined to a variety of noise which affects the quality of image. The
main purpose of de–noising the image is to restore the detail of original image as much as possible.
The criteria of the noise removal problem depends on the noise type by which the image is
corrupting .In the field of reducing the image noise several type of linear and non linear filtering
techniques have been proposed . Different approaches for reduction of noise and image
enhancement have been considered, each of which has their own limitation and advantages.
Index Terms– Digital Image Processing, Images Types, Image Noise Model, Filters
INTRODUCTION
Digital Image process could be a part of digital signal process .The area of digital image process
refers to handling digital pictures by means of a computing device. Digital image process has many
merits on analog image process; it permits a significantly wider assortment of algorithms to be apply
to input file and may keep from issues for instance the build–up of noise and signal deformation
throughout processing. Digital Image process involves the modification of digital information for
improving the image qualities with the help of system. The process helps in maximize the clarity,
sharpness of image and details of options of
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59.
60. Image And Image Of Image Enhancement
INTRODUCTION
Image processing refers to the construction of an image for further analysis and use. Image taken by
a camera or same techniques are not actual in a form that can be used by image analysis process.
The technique involves in image enhancement need to be simplified, enhanced, filtered, altered,
segmented or need improvement to reducing noise, etc. Image processing is the collection of
techniques in which implementation is done for industrial applications to resolve various issues that
alter, improve, enhance or simplify an image. Image enhancement is one of the important parts of
digital image processing where image undergo for visual inspection or for machine analysis without
knowledge of its source of degradation. The processes involve in enhancement techniques to bring
out specific application of an image so that the result is satisfactory which more visible as compare
to original image. Image can be enhanced in various ways such as contrast enhancement, intensity,
density slicing, edge enhancement, removal of noise, and saturation transformation.[1]
Over several past years, contrast image enhancement has generated across many applications like
robot sensing, electronic products, fault detection, medical image analysis, etc. Thus, increasing in
popularity of contrast enhancement of images has forces researchers to study their enhancement
techniques and their effectiveness for the interpretability or perception of human viewers. Contrast
enhancement is a
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61.
62. Essay On Feature Extraction
Feature plays a very important role in the area of image processing. Different feature extraction
techniques are applied on different types of images to get features that will be useful in classifying
and recognition of images. Features describes the important information of images that helps to
classify images correctly and remarkably reduce the dimension of the images. In pattern recognition
and image processing, feature extraction is a special form of dimensionality reduction. The main
goal of feature extraction is to obtain the most relevant information from the original data and
represent that information in a lower dimensional space. Effective feature extraction from various
intensity or color in images have been an important topic ... Show more content on Helpwriting.net
...
The histogram gives the feature vector for entire window. Example of LBP feature extraction is
given in the Figure 2.1
Figure 2.1: Finding decimal value for central pixel using LBP
LBP has some limitations that reduces its application fields. LBP is not rotation invariant and the
size of the features increases exponentially with the number of neighbors which leads to an increase
of computational complexity in terms of time and space.
2.2.1 Noise Adaptive Binary Pattern (NABP)
Noise adaptive binary pattern [12] is a modification of local binary pattern. Though LPB is powerful
in extraction local features, it has a lack of discriminative power and sensitive to noise. LBP may
produce same pattern for big difference and same difference of the central pixel with neighboring
pixel. LBP is also affected by noise. So, a modification is proposed on LBP to face fluctuation of
intensity and noise in image. They proposed a threshold (square root of central pixel + central pixel).
If neighboring pixel value is greater than the pixel then the pattern value is 1 otherwise 0. Figure 2.2
illustrates calculation of NABP.
Figure 2.2: Finding decimal value for central pixel using NABP
2.2.3 Completed Local Binary Pattern (CLBP)
CLBP [1] is also very similar to LBP. Main
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63.
64. Review On Fruit Disease Detection Using Color, Texture...
Review on Fruit Disease Detection Using Color, Texture Analysis and ANN with E–nose
Shalaka Koske Minal Bhalgat
Computer Engineering Computer Engineering
DYPSOE, Pune, DYPSOE, Pune,
Maharashtra, India. Maharashtra, India.
Pratiksha Kale Neha Mundokar
Computer Engineering Computer Engineering
DYPSOE, Pune , DYPSOE, Pune ,
Maharashtra, India. Maharashtra, India.
Prof. Yogesh A Thorat
Assistant Professor,
DYPSOE, Pune,
Maharashtra, India.
Abstract:
In agricultural industry, along with vegetables, fruit production also plays a vital role. For better
yield of fruit, detection of fruit diseases at early stage is necessary for taking preventive measures,
so as to reduce the loss of farmer. For detecting the disease an earlier approach was to hire an expert
which was time consuming for large farms, hence to reduce human efforts and to improve the yield
of fruits we are proposing a system which includes smart farming technique .In the proposed system
image processing is used for getting the required output, we are using Open Cv library which is an
image processing software. Images are classified and mapped to respective diseases on basis of
following features: color, texture, morphology, structure of hole and odour. E–NOSE is used which
is a
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65.
66. Design Of Image Capture, Display, Colour Processing And...
INTRODUCTION
Aim: Throughout this laboratory we aimed to understand the processes used to achieve the
development of image capture, display, colour processing and finally object tracking. In particular,
we aim to learn the I2C protocols to program the registers used to configure the camera, how to
convert a raw image to a full colour image, detect a selected colour and then track it.
Block Diagrams and images for the image processing steps:
The block diagram in Figure 1, illustrates the processing blocks that were created to being the image
processing steps. It also shows the variables created in the code and how they interact to produce the
initial output of display an image from the camera to the screen. The clock for the 640x480 (frame
size 800x525) display image runs at a frequency of 25.2 MHz and the clock for the camera runs at a
frequency of 48.825 MHz to synchronize the display. The I2C setup, involves using I2C protocols to
program registers within the camera. It is a two wire protocol, where one wire acts as the clock to
pass from the FPGA to the device, and the other wire is the data wire which is bidirectional. The
data wire is a top level entity and requires the setup module to have 3 data connections. These are
input data from the camera to the controller, output data from the FPGA controller to the camera and
output enable (tristate control), which determines whether the data is input or output.
Producing the image on the VGA display, involves using
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67.
68. Incidence Rate Of Skin Cancer
Abstract: Incidence rate of skin cancer are increasing day by day. Skin cancer is one of the deadliest
forms of cancer but detected earlier can save the life time of the human being. An automated
screening system is introduced to identify the presence of skin cancer in advance. In this paper,
texture distinctiveness lesion segmentation algorithm is used. Experience and training–based
characteristics of back propagation neural network is used with texture distinctiveness lesion
segmentation algorithm, for identifying the normal and abnormal portions of skin .The most
commonly occurring skin cancers are Melanoma, Basal and squamous cell carcinoma and actinic
keratosis. The proposed system is to diagnose the presence of these skin cancers with high
segmentation accuracy.
Keywords: Melanoma, segmentation, skin cancer, texture, neural network.
1. INTRODUCTION
Cancer is a life threatening disease caused primarily by genetic instability and accumulation of
multiple molecular alternations [1] [2].Present diagnostic and prognostic classifications are
insufficient to make prediction for successful treatment and patient outcome [3] [4].Among many
types of cancer, Skin cancers are the most common form of cancers in human [5]. The common
types of skin cancers are melanoma, basal and squamous cell carcinoma, and Actinic Keratosis
[6].Digital Dermoscopy is widely considered as one of the most cost effective method to identify
and classify skin–cancer. The rate of detection of melanoma
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69.
70. The Advantages And Disadvantages Of Digital Radiography
Digital radiography (DR) is a revolutionary invention in radiography. With this technology, no
cassette is needed for an x–ray examination meaning that there is no need to reload films or to erase
imaging plate in every examination. This is a distinctive feature which conventional radiography
and computed radiography (CR) do not have. DR was first introduced in 1996 (Carroll, 2011).
Miniature electronic x–ray detectors are used as the image receptor. The detectors enable the direct
capture of the x–ray image without conversion steps (like the conversion of x–ray photos into light
photons). This technology is widely used nowadays since it has many advantages and it brings much
convenience to radiographers. One of the main advantages of DR is image post–processing in which
the quality of the film (in terms of contrast and brightness, etc.) can be adjusted to reach the desired
standard. Therefore, the tolerance of the deviation of the exposure factors is greater and the need of
repeating the examination is greatly reduced so the patient dose is reduced. This follows the as low
as reasonably achievable principle for radiation protection and this also improve the final image
quality simultaneously. Besides, many DR systems were installed with preset for numerous
anatomical studies which can improve the post processing. Like CR, the images produced are in
digital format so this provides convenience for radiographers to store and retrieve the image easily.
DR is also capable to work with PACS ... Show more content on Helpwriting.net ...
There are three main components of DR system. They are imaging system, image processing system
and image communication& archiving system.
1) Imaging
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71.
72. Analysis : Automated Tissue Image Analysis
Topic1: image analysis
JIAN GAO 13050902
This report is about automated tissue image analysis, there are 5 parts in this article:
1. Introduction of image analysis
2. How image analysis be used in slide image of histology
3. What can be obtained from slide of diagnostic use
4. Discuss the advantages and disadvantages of image analysis
5. Conclusion
1. What is the image analysis
Histology is a microscopic study of organic tissue, is an important tool to diagnosis of cancer and
other diseases. The traditional method is artificial test, which needs to make a tissue slide and
obtaining under a microscope by naked eyes, for this method, the processing of analysis is a
monotonous and long work, and there are unavoidable artificial errors. So develop an automated
tissue image analysis is a very important study.
The history of development of automated image analysis technology: scientists has done the study
since 1920, start for application on 1960, the range of application expanded rapidly after 1970, and
nowadays: the application of image analysis technology in almost every fields of nature science. Of
course, Image analysis also can be used in medical science for histology tissue study().
Image analysis system is a digital technique, which consist of two parts: hardware and computer
software: the hardware includes are input device
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73.
74. Hidden Reasons for Kodak's Digital Revolution Essay
Kodak and the Digital Revolution: Case Analysis
Since the early 1880's, Kodak had proven themselves to be great innovators and had worked on
building their brand on a domestic and international front. They invested heavily in marketing to
establish their image and realized early on that their profits would come from consumables rather
than hardware. They sold their equipment at low prices in order to fuel their highly profitable film
sales. This use of a razor–blade strategy, coupled with strong relationships with retailers positioned
Kodak as an industry leader. Additionally, their heavy investment in R&D allowed Kodak to
grow organically, proving fruitful with the advent of color film. Thus, Kodak's expertise in color
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In traditional imaging, the image chain was as follows: Image Capture > Roll of Film >
Printing > Storage.b This was a change from the digital imaging chain which was: Image Capture
> Digitization > Storage > Retrieval, Transmission, Printing, Manipulation, and
Projection.a See custom attachment for graphical representations of traditional imaging chain and
figure A taken from page 9 of Kodak and the Digital Revolution case. Kodak's response to Sony's
introduction of the Mavica in 1981 was one of trepidation as well as acceptance. Kodak clearly
realized that the Mavica had the potential to greatly alter the landscape of its industry. Kodak
acknowledged this occurrence as a major paradigm shift; however, due to the escalating
commitment and its deep roots in traditional photography, Kodak failed to react accordingly.
Kodak's CEO at the time, Colby Chandler, outwardly recognized the public's affinity for color prints
– the product that made Kodak a household name. Yet, others at Kodak went as far as to make
doomsday predictions. Some managers within Kodak felt that the inception of the Mavica would be
the death of traditional photography. It is apparent that Kodak should have invested in research and
development as traditional film was reaching its natural limit, thus causing the referenced paradigm
shift. Without Kodak's willingness to outwardly adapt to the change, whether it be through
R&D or other channels, Kodak's ability to
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