Encompassing the concepts, practice, and application of orientation analysis, Introduction to Texture Analysis is an essential reference source for reserachers in textiles. The author uses an accessible style to share her expertise, providing comprehensive coverage of the theory and practice of the texture techniques now available and discusses their applications in research and industry. The text considers the merits of each technique for specific applications. Case studies expand upon the author's explanations and help illustrate the main principles involved. Topics include applications of diffraction, SEM- and TEM-based techniques, and crystallographic analyses.

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

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

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.

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.

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.

Reflecting emerging methods and the evolution of the field, Introduction to Texture Analysis: Macrotexture, Microtexture, and Orientation Mapping keeps mathematics to a minimum in covering both traditional macrotexture analysis and more advanced electron-microscopy-based microtexture analysis. The authors integrate the two techniques and address the subsequent need for a more detailed explanation of philosophy, practice, and analysis associated with texture analysis. The book illustrates approaches to orientation measurement and interpretation and elucidates the fundamental principles on which measurements are based. Thoroughly updated, this Third Edition of a best-seller is a rare introductory-level guide to texture analysis. Discusses terminology associated with orientations, texture, and their representation, as well as the diffraction of radiation, a phenomenon that is the basis for almost all texture analysis. Covers data acquisition, as well as representation and evaluation related to the well-established methods of macrotexture analysis. Updated to include experimental details of the latest transmission or scanning electron microscope-based techniques for microstructure analysis, including EBSD. Describes how microtexture data are evaluated and represented and emphasizes the advances in orientation microscopy and mapping, and advanced issues concerning crystallographic aspects of interfaces and connectivity. Offers new and innovative grain boundary descriptions and examples. This book is an ideal tool to help readers in the materials sciences develop a working understanding of the practice and applications of texture.

This volume contains papers presented at The 15th International Conference on the Texture of Materials from June 1-5th, 2008 in Pittsburgh, PA. Chapters include: Thin Films Texture at Non-Ambient Conditions Novel Texture Measurement Techniques Including 3D Complex Oxides Interface Textures Recrystallization Texture Biomaterials Texture Effects on Damage Accumulation Digital Microstructures View information on Materials Processing and Texture: Ceramic Transactions, Volume 200.

This book contains 31 selected papers (out of 136 accepted) from the 9th Scandinavian Conference on Image Analysis, held in Uppsala, Sweden, 6?9 June 1995. They represent the very best of what is currently done in image analysis, world-wide, describing very recent work. The papers have been both considerably expanded and updated compared to the version in the conference proceedings, giving the readers a much better understanding of the issues at hand.The papers cover both theory and successful applications. There are chapters on Edges and Curves, d104ure, Depth and Stereo, Scene Analysis, and 3D Motion, thus covering the chain from feature extraction to computer vision. Two important application areas are covered: Medical and Industrial.