Handbook of Texture Analysis
Handbook of Texture Analysis

Author: Ayman El-Baz

Publisher: CRC Press

Published: 2024-06-21

Total Pages: 271

ISBN-13: 1040008909

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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.

Handbook of Pattern Recognition and Computer Vision
Handbook of Pattern Recognition and Computer Vision

Author: C. H. Chen

Publisher: World Scientific

Published: 1999

Total Pages: 1045

ISBN-13: 9812384731

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The very significant advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. However, the chapters of both editions are well written for permanent reference.

Image Texture Analysis
Image Texture Analysis

Author: Chih-Cheng Hung

Publisher: Springer

Published: 2019-06-05

Total Pages: 264

ISBN-13: 3030137732

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This useful textbook/reference presents an accessible primer on the fundamentals of image texture analysis, as well as an introduction to the K-views model for extracting and classifying image textures. Divided into three parts, the book opens with a review of existing models and algorithms for image texture analysis, before delving into the details of the K-views model. The work then concludes with a discussion of popular deep learning methods for image texture analysis. Topics and features: provides self-test exercises in every chapter; describes the basics of image texture, texture features, and image texture classification and segmentation; examines a selection of widely-used methods for measuring and extracting texture features, and various algorithms for texture classification; explains the concepts of dimensionality reduction and sparse representation; discusses view-based approaches to classifying images; introduces the template for the K-views algorithm, as well as a range of variants of this algorithm; reviews several neural network models for deep machine learning, and presents a specific focus on convolutional neural networks. This introductory text on image texture analysis is ideally suitable for senior undergraduate and first-year graduate students of computer science, who will benefit from the numerous clarifying examples provided throughout the work.

Introduction to Texture Analysis
Introduction to Texture Analysis

Author: Olaf Engler

Publisher: CRC Press

Published: 2009-11-16

Total Pages: 490

ISBN-13: 1420063669

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The first edition of Introduction to Texture Analysis: Macrotexture, Microtexture, and Orientation Mapping broke new ground by collating seventy years worth of research in a convenient single-source format. Reflecting emerging methods and the evolution of the field, the second edition continues to provide comprehensive coverage of the concepts, pra

Handbook of Texture Analysis
Handbook of Texture Analysis

Author: Ayman El-Baz

Publisher: CRC Press

Published: 2024-06-24

Total Pages: 226

ISBN-13: 1040008917

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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 Materials Science
Texture Analysis in Materials Science

Author: H.-J. Bunge

Publisher: Elsevier

Published: 2013-09-03

Total Pages: 614

ISBN-13: 1483278395

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Texture Analysis in Materials Science Mathematical Methods focuses on the methodologies, processes, techniques, and mathematical aids in the orientation distribution of crystallites. The manuscript first offers information on the orientation of individual crystallites and orientation distributions. Topics include properties and representations of rotations, orientation distance, and ambiguity of rotation as a consequence of crystal and specimen symmetry. The book also takes a look at expansion of orientation distribution functions in series of generalized spherical harmonics, fiber textures, and methods not based on the series expansion. The publication reviews special distribution functions, texture transformation, and system of programs for the texture analysis of sheets of cubic materials. The text also ponders on the estimation of errors, texture analysis, and physical properties of polycrystalline materials. Topics include comparison of experimental and recalculated pole figures; indetermination error for incomplete pole figures; and determination of the texture coefficients from anisotropie polycrystal properties. The manuscript is a dependable reference for readers interested in the use of mathematical aids in the orientation distribution of crystallites.

Computer Analysis of Visual Textures
Computer Analysis of Visual Textures

Author: Fumiaki Tomita

Publisher: Springer Science & Business Media

Published: 2013-11-11

Total Pages: 179

ISBN-13: 1461315530

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This book presents theories and techniques for perception of textures by computer. Texture is a homogeneous visual pattern that we perceive in surfaces of objects such as textiles, tree barks or stones. Texture analysis is one of the first important steps in computer vision since texture provides important cues to recognize real-world objects. A major part of the book is devoted to two-dimensional analysis of texture patterns by extracting statistical and structural features. It also deals with the shape-from-texture problem which addresses recovery of the three-dimensional surface shapes based on the geometry of projection of the surface texture to the image plane. Perception is still largely mysterious. Realizing a computer vision system that can work in the real world requires more research and ex periment. Capability of textural perception is a key component. We hope this book will contribute to the advancement of computer vision toward robust, useful systems. vVe would like to express our appreciation to Professor Takeo Kanade at Carnegie Mellon University for his encouragement and help in writing this book; to the members of Computer Vision Section at Electrotechni cal Laboratory for providing an excellent research environment; and to Carl W. Harris at Kluwer Academic Publishers for his help in preparing the manuscript.

Handbook of Texture Analysis
Handbook of Texture Analysis

Author: Ayman El-Baz

Publisher:

Published: 2024-04

Total Pages: 0

ISBN-13: 9781032727417

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This two-volume handbook provides a comprehensive view of texture analysis in both AI-based industrial and medical imaging applications. The first volume covers texture analysis for neuroradiology; information-theoretic entropy; chronic liver diseases; clinical management of focal liver lesions; abdominal imaging; optical coherence tomography images; thoracic imaging; prostate cancer; breast cancer; bladder cancer, quality evaluation of meat products; and detection of powdery mildew on strawberry leaves. The second volume covers Local Binary Descriptors for Texture Classification; Precision Grading of Glioma; Liver Tumor Detection and Grading; Texture Analysis in Radiology; Texture Analysis Using a Self-Organizing Feature Map; Sensor-Based Human Activity Recognition Analysis Using Machine Learning and Topological Data Analysis; Texture Analysis in Retinal OCT Imaging; Pneumonia Detection; Prostatic Adenocarcinoma; and Texture Analysis in Cancer Prognosis. Aimed at researchers, academics, and advanced students in biomedical engineering, image analysis, cognitive science, and computer science and engineering, this handbook is an essential reference for those looking to advance their understanding in this applied and emergent field.