Texture Analysis Using A Markov Model
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Texture Analysis Using A Markov Model
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Author : Xiao Gong
language : en
Publisher:
Release Date : 1991
Texture Analysis Using A Markov Model written by Xiao Gong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with categories.
Texture Analysis And Synthesis Using A Generic Markov Gibbs Image Model
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Author : Dongxiao Zhou
language : en
Publisher:
Release Date : 2006
Texture Analysis And Synthesis Using A Generic Markov Gibbs Image Model written by Dongxiao Zhou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Image processing categories.
Texture Analysis Using Pseudo Markov Statistics
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Author : Keith Andrew Bentley
language : en
Publisher:
Release Date : 1981
Texture Analysis Using Pseudo Markov Statistics written by Keith Andrew Bentley and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1981 with Markov processes categories.
Handbook Of Texture Analysis
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Author : Ayman El-Baz
language : en
Publisher: CRC Press
Release Date : 2024-06-24
Handbook Of Texture Analysis written by Ayman El-Baz and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-24 with Computers categories.
The major goals of texture research in computer vision are to understand, model, and process texture, and ultimately, to simulate the human visual learning process using computer technologies. In the last decade, artificial intelligence has been revolutionized by machine learning and big data approaches, outperforming human prediction on a wide range of problems. In particular, deep learning convolutional neural networks (CNNs) are particularly well suited to texture analysis. This book examines four major application domains related to texture analysis and their relationship to AI-based industrial applications: texture classification, texture segmentation, shape from texture, and texture synthesis. This volume: Discusses texture-based segmentation for extracting image shape features, modeling and segmentation of noisy and textured images, spatially constrained color-texture model for image segmentation, and texture segmentation using Gabor filters Examines textural features for image classification, a statistical approach for classification, texture classification from random features, and applications of texture classifications Describes shape from texture, including general principles, 3D shapes, and equations for recovering shape from texture Surveys texture modeling, including extraction based on Hough transformation and cycle detection, image quilting, gray level run lengths, and use of Markov random fields Aimed at researchers, academics, and advanced students in biomedical engineering, image analysis, cognitive science, and computer science and engineering, this is an essential reference for those looking to advance their understanding in this applied and emergent field.
Texture Analysis In Machine Vision
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Author : Matti Pietikinen
language : en
Publisher: World Scientific
Release Date : 2000
Texture Analysis In Machine Vision written by Matti Pietikinen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Computers categories.
d104ure analysis is an important generic research area of machine vision. The potential areas of application include biomedical image analysis, industrial inspection, analysis of satellite or aerial imagery, content-based retrieval from image databases, document analysis, biometric person authentication, scene analysis for robot navigation, texture synthesis for computer graphics and animation, and image coding. d104ure analysis has been a topic of intensive research for over three decades, but the progress has been very slow.A workshop on ?d104ure Analysis in Machine Vision? was held at the University of Oulu, Finland, in 1999, providing a forum for presenting recent research results and for discussing how to make progress in order to increase the usefulness of texture in practical applications. This book contains extended and revised versions of the papers presented at the workshop. The first part of the book deals with texture analysis methodology, while the second part covers various applications. The book gives a unique view of different approaches and applications of texture analysis. It should be of great interest both to researchers of machine vision and to practitioners in various application areas.
Image Texture Analysis Based On Gaussian Markov Random Fields
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Author : Chathurika Dharmagunawardhana
language : en
Publisher:
Release Date : 2014
Image Texture Analysis Based On Gaussian Markov Random Fields written by Chathurika Dharmagunawardhana and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Markov Random Field Texture Models
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Author : George Robert Cross
language : en
Publisher:
Release Date : 1980
Markov Random Field Texture Models written by George Robert Cross and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1980 with Image processing categories.
Handbook Of Texture Analysis
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Author : Ayman El-Baz
language : en
Publisher: CRC Press
Release Date : 2024-06-21
Handbook Of Texture Analysis written by Ayman El-Baz and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-21 with Computers categories.
The major goals of texture research in computer vision are to understand, model, and process texture and, ultimately, to simulate the human visual learning process using computer technologies. In the last decade, artificial intelligence has been revolutionized by machine learning and big data approaches, outperforming human prediction on a wide range of problems. In particular, deep learning convolutional neural networks (CNNs) are particularly well suited to texture analysis. This volume presents important branches of texture analysis methods which find a proper application in AI-based medical image analysis. This book: Discusses first-order, second-order statistical methods, local binary pattern (LBP) methods, and filter bank-based methods Covers spatial frequency-based methods, Fourier analysis, Markov random fields, Gabor filters, and Hough transformation Describes advanced textural methods based on DL as well as BD and advanced applications of texture to medial image segmentation Is aimed at researchers, academics, and advanced students in biomedical engineering, image analysis, cognitive science, and computer science and engineering This is an essential reference for those looking to advance their understanding in this applied and emergent field.
Hierarchical Markov Random Field Modeling For Texture Analysis And Classification In Radiographic Image Processing
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Author : Rene Vargas-Voracek
language : en
Publisher:
Release Date : 1995
Hierarchical Markov Random Field Modeling For Texture Analysis And Classification In Radiographic Image Processing written by Rene Vargas-Voracek and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computer graphics categories.
Advanced Color Image Processing And Analysis
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Author : Christine Fernandez-Maloigne
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-07-11
Advanced Color Image Processing And Analysis written by Christine Fernandez-Maloigne and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-11 with Technology & Engineering categories.
This volume does much more than survey modern advanced color processing. Starting with a historical perspective on ways we have classified color, it sets out the latest numerical techniques for analyzing and processing colors, the leading edge in our search to accurately record and print what we see. The human eye perceives only a fraction of available light wavelengths, yet we live in a multicolor world of myriad shining hues. Colors rich in metaphorical associations make us “purple with rage” or “green with envy” and cause us to “see red.” Defining colors has been the work of centuries, culminating in today’s complex mathematical coding that nonetheless remains a work in progress: only recently have we possessed the computing capacity to process the algebraic matrices that reproduce color more accurately. With chapters on dihedral color and image spectrometers, this book provides technicians and researchers with the knowledge they need to grasp the intricacies of today’s color imaging.