Nothing Special   »   [go: up one dir, main page]

skip to main content
Skip header Section
Handbook of Texture AnalysisMarch 2009
Publisher:
  • Imperial College Press
  • 516 Sherfield building Imperial College London SW7 2AZ
  • United Kingdom
ISBN:978-1-84816-115-3
Published:31 March 2009
Pages:
413
Skip Bibliometrics Section
Reflects downloads up to 27 Nov 2024Bibliometrics
Skip Abstract Section
Abstract

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.

Cited By

  1. Liu Y, Gao Y, Sadia N, Qi L and Dong J (2024). A Sketch-texture Retrieval Framework using Perceptual Similarity, Knowledge-Based Systems, 286:C, Online publication date: 28-Feb-2024.
  2. ACM
    Yan Z, Wu Y, Luo D, Zhang C, Jin Q, Chen W, Wu Y, Chen X, Wang G and Mi H (2023). NaCanva: Exploring and Enabling the Nature-Inspired Creativity for Children, Proceedings of the ACM on Human-Computer Interaction, 7:MHCI, (1-25), Online publication date: 11-Sep-2023.
  3. de Melo Langoni V and Gonzaga A (2019). Evaluating dynamic texture descriptors to recognize human iris in video image sequence, Pattern Analysis & Applications, 23:2, (771-784), Online publication date: 1-May-2020.
  4. Nsimba C and Levada A Nonlinear Dimensionality Reduction in Texture Classification: Is Manifold Learning Better Than PCA? Computational Science – ICCS 2019, (191-206)
  5. Liu L, Chen J, Fieguth P, Zhao G, Chellappa R and Pietikäinen M (2019). From BoW to CNN, International Journal of Computer Vision, 127:1, (74-109), Online publication date: 1-Jan-2019.
  6. Vard A (2018). A new combination active contour model for segmenting texture image with low contrast and high illumination variations, Multimedia Tools and Applications, 77:15, (20021-20042), Online publication date: 1-Aug-2018.
  7. Mirhashemi A (2018). Introducing spectral moment features in analyzing the SpecTex hyperspectral texture database, Machine Vision and Applications, 29:3, (415-432), Online publication date: 1-Apr-2018.
  8. Fraj O, Ghozi R and Jaïdane-Saïdane M (2017). Audio texturedness indicator based on a direct and reverse short listening time analysis, Multimedia Tools and Applications, 76:24, (26177-26200), Online publication date: 1-Dec-2017.
  9. Kuppili V, Biswas M, Sreekumar A, Suri H, Saba L, Edla D, Marinhoe R, Sanches J and Suri J (2017). Extreme Learning Machine Framework for Risk Stratification of Fatty Liver Disease Using Ultrasound Tissue Characterization, Journal of Medical Systems, 41:10, (1-20), Online publication date: 1-Oct-2017.
  10. Marcolin F and Vezzetti E (2017). Novel descriptors for geometrical 3D face analysis, Multimedia Tools and Applications, 76:12, (13805-13834), Online publication date: 1-Jun-2017.
  11. Pourfard M, Abdollahifard M, Faez K, Motamedi S and Hosseinian T (2017). PCTO-SIM, Computers & Geosciences, 102:C, (116-138), Online publication date: 1-May-2017.
  12. ACM
    Fahmi H, Zen R, Sanabila H, Nurhaida I and Arymurthy A Feature Selection and Reduction for Batik Image Retrieval Proceedings of the Fifth International Conference on Network, Communication and Computing, (47-52)
  13. Boudra S, Yahiaoui I and Behloul A A Comparison of Multi-scale Local Binary Pattern Variants for Bark Image Retrieval Proceedings of the 16th International Conference on Advanced Concepts for Intelligent Vision Systems - Volume 9386, (764-775)
  14. Shrivastava V, Londhe N, Sonawane R and Suri J (2015). Exploring the color feature power for psoriasis risk stratification and classification, Computers in Biology and Medicine, 65:C, (54-68), Online publication date: 1-Oct-2015.
  15. Hiremath P and Bhusnurmath R RGB - Based Color Texture Image Classification Using Anisotropic Diffusion and LDBP Proceedings of the 8th International Workshop on Multi-disciplinary Trends in Artificial Intelligence - Volume 8875, (101-111)
  16. ACM
    Dong X, Methven T and Chantler M How Well Do Computational Features Perceptually Rank Textures? A Comparative Evaluation Proceedings of International Conference on Multimedia Retrieval, (281-288)
  17. Acharya U, Sree S, Muthu Rama Krishnan M, Krishnananda N, Ranjan S, Umesh P and Suri J (2013). Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images, Computer Methods and Programs in Biomedicine, 112:3, (624-632), Online publication date: 1-Dec-2013.
  18. Cusano C, Napoletano P and Schettini R Illuminant Invariant Descriptors for Color Texture Classification Proceedings of the 4th International Workshop on Computational Color Imaging - Volume 7786, (239-249)
  19. Gangeh M, Ghodsi A and Kamel M Supervised Texture Classification Using a Novel Compression-Based Similarity Measure Proceedings of the International Conference on Computer Vision and Graphics - Volume 7594, (379-386)
  20. Rahtu E, Heikkilä J, Ojansivu V and Ahonen T (2012). Local phase quantization for blur-insensitive image analysis, Image and Vision Computing, 30:8, (501-512), Online publication date: 1-Aug-2012.
  21. Acharya R, Faust O, Alvin A, Sree S, Molinari F, Saba L, Nicolaides A and Suri J (2012). Symptomatic vs. Asymptomatic Plaque Classification in Carotid Ultrasound, Journal of Medical Systems, 36:3, (1861-1871), Online publication date: 1-Jun-2012.
  22. Allili M and Baaziz N Contourlet-based texture retrieval using a mixture of generalized gaussian distributions Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II, (446-454)
  23. Gangeh M, Ghodsi A and Kamel M Dictionary learning in texture classification Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I, (335-343)
  24. González-Rufino E, Carrión P, Formella A, Fernández-Delgado M and Cernadas E Statistical and wavelet based texture features for fish oocytes classification Proceedings of the 5th Iberian conference on Pattern recognition and image analysis, (403-410)
  25. Saha B, Ray N and Zhang H Automating snakes for multiple objects detection Proceedings of the 10th Asian conference on Computer vision - Volume Part III, (39-51)
Contributors
  • University of Bristol
  • Swansea University
  • Teerthanker Mahaveer University, Moradabad

Reviews

Egon L. van den Broek

Texture is still not fully understood. Handbooks on both human and machine vision mention texture as an important characteristic of perceptual processes, for a range of applications. Moreover, a vast number of articles on texture, approached from either the human or machine side, have been published. However, it should be noted that direct links between, or even models of, both human and machine perception of texture are rare [1,2]. Even more surprisingly, books on texture analysis are rarely found [3,4]. So, what is texture__?__ What makes it such an odd proposition in the world of science__?__ What is the value of this handbook__?__ The abstract of the first chapter, "Introduction to Texture Analysis," starts with a dictionary-style definition of texture: "Textures are characteristic intensity (or color) variations that typically originate from roughness of object surfaces." Despite all of the work done on texture, the definitions of texture that are used are rather vague. This illustrates the complexity of the texture phenomenon and science's lack of understanding of it. This book respects and even illustrates the complexity of texture and, as an edited volume, aims to collect significant works on its various aspects. In this aim, the handbook succeeds. As such, it is regrettably not a groundbreaking work and it lacks truly new insights. It does provide various definitions of texture, all in a dictionary style. Being an edited volume, its value as a handbook is limited. Although distinguished authors and their contributions were undoubtedly chosen with care, overlap between chapters could not be avoided. This is clearly illustrated by the reference lists, as provided for each chapter. Although the reference lists are impressive when taken together, some references are missing-for example, from the Journal of the Optical Society of America . This illustrates the handbook's bias toward image processing and computer vision. Findings from other fields, such as psychology and optics, are addressed only to a limited extent. Going through the handbook, one will notice that roughly half of the chapters are reprints of papers. This is not what one would hope when consulting a handbook. Nevertheless, this collection has value, as all of the papers and chapters are excellent. The handbook starts with a gentle introduction to texture analysis, followed by a concise overview of texture synthesis. The latter, in particular, is of general interest, as it discusses 18 milestone papers. Next, the book introduces methods for texture classification, representation, and analysis, followed by three chapters on three-dimensional (3D) texture analysis and one on dynamic textures. Before the closing chapter, the handbook presents three chapters on distinct topics: an alternative approach to texture synthesis, the trace transform, and face recognition. The book ends with a compressed overview of texture features and a taxonomy of texture analysis, accompanied by an excellent reference list. One aspect of texture that the handbook hardly touches, among the various aspects of texture discussed, is color-induced texture analysis. Indeed, a vast number of applications rely solely on intensity-based textures. However, a vast number of applications also rely on color-induced texture analysis. Moreover, human vision relies on both [1,2]. This makes it hard to understand why the topic is mentioned only briefly in the handbook. Overall, the handbook is a good start for those who want to be introduced to texture analysis, but it has somewhat limited value for experts. Even for them, though, it will be convenient to be able to pick up this handbook from time to time, as it presents a collection of high-quality work in the field. It fills a gap in a niche market [3,4] and, despite the criticism I expressed, will undoubtedly be received with open arms by the community. Online Computing Reviews Service

Access critical reviews of Computing literature here

Become a reviewer for Computing Reviews.

Please enable JavaScript to view thecomments powered by Disqus.

Recommendations