Computer Analysis of Visual Textures

Computer Analysis of Visual Textures PDF

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 Pattern Recognition and Computer Vision

Handbook of Pattern Recognition and Computer Vision PDF

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 PDF

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.

Handbook Of Texture Analysis

Handbook Of Texture Analysis PDF

Author: Majid Mirmehdi

Publisher: World Scientific

Published: 2008-10-28

Total Pages: 424

ISBN-13: 1908978929

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Texture analysis is one of the fundamental aspects of human vision by which we discriminate between surfaces and objects. In a similar manner, computer vision can take advantage of the cues provided by surface texture to distinguish and recognize objects. In computer vision, texture analysis may be used alone or in combination with other sensed features (e.g. color, shape, or motion) to perform the task of recognition. Either way, it is a feature of paramount importance and boasts a tremendous body of work in terms of both research and applications.Currently, the main approaches to texture analysis must be sought out through a variety of research papers. This collection of chapters brings together in one handy volume the major topics of importance, and categorizes the various techniques into comprehensible concepts. The methods covered will not only be relevant to those working in computer vision, but will also be of benefit to the computer graphics, psychophysics, and pattern recognition communities, academic or industrial./a

Handbook of Texture Analysis

Handbook of Texture Analysis PDF

Author: Ayman El-Baz

Publisher: CRC Press

Published: 2024-06-24

Total Pages: 262

ISBN-13: 1040008984

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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 Machine Vision

Texture Analysis in Machine Vision PDF

Author: M K Pietikäinen

Publisher: World Scientific

Published: 2000-10-13

Total Pages: 280

ISBN-13: 981449268X

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Texture 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. Texture analysis has been a topic of intensive research for over three decades, but the progress has been very slow. A workshop on “Texture 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. Contents:Nonparametric Texture Analysis with Complementary Spatial Operators (M Pietikäinen & T Ojala)Multi-Resolution Clustering of Texture Images (S Battiato & G Gallo)Robustness of Local Binary Pattern (LBP) Operators to Tilt-Compensated Textures (M Soriano et al.)Using Texture in Image Similarity and Retrieval (S Aksoy & R M Haralick)Tongue Texture Analysis Using Gabor Wavelet Opponent Colour Features for Tongue Diagnosis in Traditional Chinese Medicine (P C Yuen et al.)Feature Evaluation of Texture Test Objects for Magnetic Resonance Imaging (A Materka et al.)Automatic Detection of Errors on Textures Using Invariant Grey-Scale Features and Polynomial Classifiers (M Schael & H Burkhardt)Combining Analysis and Synthesis of Textures for the Animation Industry (M Ollila et al.)and other papers Readership: Researchers, graduate students and industrialists in the field of machine vision. Keywords:Computer Vision;Image Analysis;Pattern Recognition;Feature Extraction;Classification;Segmentation;Texture Synthesis;Surface Properties;Color Texture;Applications

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning

Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning PDF

Author: Rani, Geeta

Publisher: IGI Global

Published: 2020-10-16

Total Pages: 586

ISBN-13: 1799827437

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By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.

Biomedical Texture Analysis

Biomedical Texture Analysis PDF

Author: Adrien Depeursinge

Publisher: Academic Press

Published: 2017-08-25

Total Pages: 432

ISBN-13: 0128123214

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Biomedical Texture Analysis: Fundamentals, Applications, Tools and Challenges describes the fundamentals and applications of biomedical texture analysis (BTA) for precision medicine. It defines what biomedical textures (BTs) are and why they require specific image analysis design approaches when compared to more classical computer vision applications. The fundamental properties of BTs are given to highlight key aspects of texture operator design, providing a foundation for biomedical engineers to build the next generation of biomedical texture operators. Examples of novel texture operators are described and their ability to characterize BTs are demonstrated in a variety of applications in radiology and digital histopathology. Recent open-source software frameworks which enable the extraction, exploration and analysis of 2D and 3D texture-based imaging biomarkers are also presented. This book provides a thorough background on texture analysis for graduate students and biomedical engineers from both industry and academia who have basic image processing knowledge. Medical doctors and biologists with no background in image processing will also find available methods and software tools for analyzing textures in medical images. Defines biomedical texture precisely and describe how it is different from general texture information considered in computer vision Defines the general problem to translate 2D and 3D texture patterns from biomedical images to visually and biologically relevant measurements Describes, using intuitive concepts, how the most popular biomedical texture analysis approaches (e.g., gray-level matrices, fractals, wavelets, deep convolutional neural networks) work, what they have in common, and how they are different Identifies the strengths, weaknesses, and current challenges of existing methods including both handcrafted and learned representations, as well as deep learning. The goal is to establish foundations for building the next generation of biomedical texture operators Showcases applications where biomedical texture analysis has succeeded and failed Provides details on existing, freely available texture analysis software, helping experts in medicine or biology develop and test precise research hypothesis

Computer Analysis of Images and Patterns

Computer Analysis of Images and Patterns PDF

Author: Mario Vento

Publisher: Springer Nature

Published: 2019-08-23

Total Pages: 688

ISBN-13: 3030298884

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The two volume set LNCS 11678 and 11679 constitutes the refereed proceedings of the 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019, held in Salerno, Italy, in September 2019. The 106 papers presented were carefully reviewed and selected from 176 submissions The papers are organized in the following topical sections: Intelligent Systems; Real-time and GPU Processing; Image Segmentation; Image and Texture Analysis; Machine Learning for Image and Pattern Analysis; Data Sets and Benchmarks; Structural and Computational Pattern Recognition; Posters.