High Order Markov Gibbs Random Field Models For Texture Recognition
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High Order Markov Gibbs Random Field Models For Texture Recognition
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Author : Ni Liu
language : en
Publisher:
Release Date : 2016
High Order Markov Gibbs Random Field Models For Texture Recognition written by Ni Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Image processing categories.
Texture analysis plays a fundamental role in computer vision applications such as image understanding, object recognition, scene segmentation and so on. This thesis investigates novel types of high-order Markov-Gibbs random field (MGRF) models of images in application to texture recognition and retrieval. The goal is to discriminate a texture class represented by a single training (query) sample from other classes. Frequent in practice spatially variant perceptive (contrast/o set) deviations that preserve the image appearance hinder the recognition by signal co-occurrence statistics. Contrast/offset-invariant descriptors of ordinal signal relations, such as local binary or ternary patterns (LBP/LTPs), are efficient and popular means to overcome such drawback. The thesis explores the use of ordinal relations instead of more conventional signal co-occurrences or responses of filter banks used in today's MGRFs. Textured images are considered as samples from high-order ordinal models, which allow for learning, rather than prescribing characteristic shapes, sizes, and numbers of these patterns for texture recognition and retrieval. Approximate analytical estimates of the model parameters guide the selection of characteristic LBP/LTPs of a given order. The higher-order patterns are learned on the basis of the already found lower-order ones. The thesis introduces first the MGRFs based on complete ordinal relations of the signals. Because the cardinality of their set grows quickly, only up to the 4th{5th-order ordinal MGRFs remain feasible. Partial ordinal LBP/LTP-based relations have less limitations, so that higher-order models can be learned (up to 12th-order in this thesis) for representing textures. Comparative experiments on six databases confirmed that classi ers using multiple learned LTPs from the 8th{12th-order consistently outperform more conventional ones with prescribed fixed-shape LBP/LTPs or other local filters. The proposed learned models are mostly good enough to describe the textures. However, heuristic rules for feasible learning of the high-order models sometimes miss characteristic short-range dependencies. Adding the nearest-neighbour circular LTPs to the learned characteristic high-order LTPs overcomes this drawback and further improves the performance over the previous models. A learnable 5th-order LTP-based MGRF was also applied as a visual appearance prior for segmenting kidney images for automated medical diagnostics. Experiments confirmed that the proposed model outperforms two more conventional counterparts both qualitatively and quantitatively.
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.
Gaussian Markov Random Fields
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Author : Havard Rue
language : en
Publisher: CRC Press
Release Date : 2005-02-18
Gaussian Markov Random Fields written by Havard Rue and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-02-18 with Mathematics categories.
Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to-date reference works are available. This is the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects. This book includes extensive case-studie
Markov Random Field Modeling In Image Analysis
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Author : Stan Z. Li
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-04-03
Markov Random Field Modeling In Image Analysis written by Stan Z. Li 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 2009-04-03 with Computers categories.
Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This third edition includes the most recent advances and has new and expanded sections on topics such as: Bayesian Network; Discriminative Random Fields; Strong Random Fields; Spatial-Temporal Models; Learning MRF for Classification. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas.
Image Textures And Gibbs Random Fields
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Author : Georgiĭ Lʹvovich Gimelʹfarb
language : en
Publisher: Springer Science & Business Media
Release Date : 1999
Image Textures And Gibbs Random Fields written by Georgiĭ Lʹvovich Gimelʹfarb 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 1999 with Computers categories.
This text presents techniques for describing image textures. Contrary to the usual practice of embedding the images to known modelling frameworks borrowed from statistical physics or other domains, this book deduces the Gibbs models from basic image features and tailors the modelling framework to the images. This approach results in more general Gibbs models than can be either Markovian or non-Markovian and possess arbitrary interaction structures and strengths. The book presents computationally feasible algorithms for parameter estimation and image simulation and demonstrates their abilities and limitations by numerous experimental results.
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.
Markov Random Fields In Image Analysis
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Author : Chaur-Chin Chen
language : en
Publisher:
Release Date : 1988
Markov Random Fields In Image Analysis written by Chaur-Chin Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Computer vision categories.
Texture Modeling
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Author : Rosalind Wright Picard
language : en
Publisher:
Release Date : 1991
Texture Modeling written by Rosalind Wright Picard 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.
Markov Random Fields With Applications To Texture Classification And Discrimination
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Author : Jun Zhang
language : en
Publisher:
Release Date : 1985
Markov Random Fields With Applications To Texture Classification And Discrimination written by Jun Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985 with categories.
Generation Of Random Textures Using Markov Random Fields And Gibbs Distribution
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Author : Heitor Vita
language : en
Publisher:
Release Date : 1984
Generation Of Random Textures Using Markov Random Fields And Gibbs Distribution written by Heitor Vita and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1984 with Image processing categories.