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A taxonomy for texture description and identificationSeptember 1990
Publisher:
  • Springer-Verlag
  • Berlin, Heidelberg
ISBN:978-0-387-97302-9
Published:01 September 1990
Pages:
197
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Abstract

No abstract available.

Cited By

  1. Perner P Learning an ontology for visual tasks Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding, (1-16)
  2. Attig A and Perner P A comparison between Haralick's texture descriptor and the texture descriptor based on random sets for biological images Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition, (524-538)
  3. Dabbah M, Graham J, Petropoulos I, Tavakoli M and Malik R Dual-model automatic detection of nerve-fibres in corneal confocal microscopy images Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I, (300-307)
  4. Brancati N, Frucci M and Baja G Reconnecting Broken Ridges in Fingerprint Images Proceedings of the 15th International Conference on Image Analysis and Processing, (739-747)
  5. Khan J, Adhami R and Bhuiyan S (2009). A customized Gabor filter for unsupervised color image segmentation, Image and Vision Computing, 27:4, (489-501), Online publication date: 1-Mar-2009.
  6. Bergonnier S, Hild F and Roux S (2007). Local anisotropy analysis for non-smooth images, Pattern Recognition, 40:2, (544-556), Online publication date: 1-Feb-2007.
  7. Chikkerur S, Cartwright A and Govindaraju V (2007). Fingerprint enhancement using STFT analysis, Pattern Recognition, 40:1, (198-211), Online publication date: 1-Jan-2007.
  8. Michelet F, Da Costa J, Baylou P and Germain C Local orientation estimation in corrupted images Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis, (349-358)
  9. Serra J (2006). A Lattice Approach to Image Segmentation, Journal of Mathematical Imaging and Vision, 24:1, (83-130), Online publication date: 1-Jan-2006.
  10. ACM
    Lai Y, Tai W, Chang C and Liu C Synthesizing transition textures on succession patterns Proceedings of the 3rd international conference on Computer graphics and interactive techniques in Australasia and South East Asia, (273-276)
  11. Romulus T, Lavialle O, Borda M and Baylou P Flow coherence diffusion. linear and nonlinear case Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems, (316-323)
  12. Barcelos C, Ferreira M and Rodrigues M Texture image retrieval Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II, (424-433)
  13. Yun E, Hong J and Cho S Adaptive enhancing of fingerprint image with image characteristics analysis Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence, (120-131)
  14. Bigun J, Bigun T and Nilsson K (2004). Recognition by Symmetry Derivatives and the Generalized Structure Tensor, IEEE Transactions on Pattern Analysis and Machine Intelligence, 26:12, (1590-1605), Online publication date: 1-Dec-2004.
  15. Yang J, Liu L and Jiang T (2004). Efficient fingerprint matching algorithm for Integrated Circuit Cards, Journal of Computer Science and Technology, 19:4, (510-520), Online publication date: 1-Jul-2004.
  16. ACM
    Andrew T and David N Integrated Wavelet and Fourier-Mellin invariant feature in fingerprint verification system Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications, (82-88)
  17. Tico M and Kuosmanen P (2003). Fingerprint Matching Using an Orientation-Based Minutia Descriptor, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25:8, (1009-1014), Online publication date: 1-Aug-2003.
  18. Bigun J, Bigun T and Nilsson K Orientation fields filtering by derivates of a Gaussian Proceedings of the 13th Scandinavian conference on Image analysis, (19-27)
  19. Dong M and Kothari R (2003). Feature subset selection using a new definition of classifiability, Pattern Recognition Letters, 24:9-10, (1215-1225), Online publication date: 1-Jun-2003.
  20. Tico M, Onnia V and Kuosmanen P (2002). Fingerprint image enhancement based on second directional derivative of the digital image, EURASIP Journal on Advances in Signal Processing, 2002:1, (1135-1144), Online publication date: 1-Jan-2002.
  21. Zhang Q, Huang K and Yan H Fingerprint classification based on extraction and analysis of singularities and pseudoridges Proceedings of the Pan-Sydney area workshop on Visual information processing - Volume 11, (83-87)
  22. Lanterman A, Grenander U and Miller M (2000). Bayesian Segmentation via Asymptotic Partition Functions, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22:4, (337-347), Online publication date: 1-Apr-2000.
  23. Ratha N, Karu K, Chen S and Jain A (1996). A Real-Time Matching System for Large Fingerprint Databases, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18:8, (799-813), Online publication date: 1-Aug-1996.
  24. Dunn D, Higgins W and Wakeley J (2019). Texture Segmentation using 2-D Gabor Elementary Functions, IEEE Transactions on Pattern Analysis and Machine Intelligence, 16:2, (130-149), Online publication date: 1-Feb-1994.
  25. Rao A and Lohse G Towards a texture naming system Proceedings of the 4th conference on Visualization '93, (220-227)
Contributors
  • IBM Thomas J. Watson Research Center

Index Terms

  1. A taxonomy for texture description and identification

    Reviews

    Keith E. Price

    Textures, in computer vision, are the patterns and variations in an image that are smaller than the objects being analyzed, such as those caused by bricks in a wall, variations within the individual bricks, or grass in a lawn. Rao discusses a taxonomy for texture description and a technique for analysis of oriented textures. Since the book is based on the author's PhD dissertation, it has the advantages and flaws inherent in any dissertation. The majority of the book is concerned with the analysis of oriented textures. These patterns include the grain texture in wood and the flow patterns that arise in analysis of fluids, wind patterns, and so on. The goal of the research is to determine the direction of the oriented texture at different scales. To understand the need for this, consider the patterns in an image of herringbone weave material: at the highest resolution the diagonal patterns of the threads are the strongest, but at lower resolutions the vertical pattern caused by the light and dark areas of the cloth is most apparent. Thus the dominant orientation is different at different scales. From the flow directions, a high-level symbolic description of the texture or flow pattern is generated. This description is in terms of a few descriptors (saddle, spiral, and node) that describe the appearance of the pattern. The remainder of the book discusses the taxonomy of textures; much of the discussion is a review of past work in the field. First, strongly ordered textures are characterized by basic primitives arranged according to some placement rules (such as tiles on a wall or floor). Next, Rao discusses disordered textures, especially in terms of the fractal dimension. The final set of textures are compositional; these are created by combining the earlier primitive textures (strongly ordered, disordered, and weakly ordered or oriented). Using this final taxonomy, the author classifies all the textures from Brodatz's standard collection of texture images [1]. As this is a dissertation, many issues of texture analysis are ignored and others are covered in great depth. The research on the analysis of oriented patterns is important, and the taxonomy provides a means for describing texture patterns. The references to other work in the field are good, as are the discussions of the contributions of these other researchers. Unlike some author-formatted books, this volume has few typographic problems. Figure 5.2(c) is a uniform gray image rather than an image of birch wood, the second paragraph in Section 1.1.1 has several serious problems with punctuation, and the placement of quotation marks relative to commas and periods is inconsistent and usually incorrect (this error is typical of computer programmers who expect quotation marks to have a very different meaning than they do in English).

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