CUReT: Columbia-Utrecht Reflectance and Texture Database,
2006. Dataset, Texture.
MIT Texture Data,
1995. Dataset, Texture.
2006. Dataset, Texture.
Outex: New framework for empirical evaluation of
texture analysis algorithms,
2006. Dataset, Texture.
Texure Image Data,
2006. Dataset, Texture.
WWW Link. A variety of texture datasets. Includes Brodatz.
The KTH-TIPS and KTH-TIPS2 image databases,
2006. Dataset, Texture.
WWW Link. Textures under varying illumination, pose and scale. Extension of:
See also CUReT: Columbia-Utrecht Reflectance and Texture Database.
TILDA: Textile Texture Database,
1996. Dataset, Texture.
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Elsevier DOI BibRef 9602
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Springer DOI 9705
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See also Classification of Textures Using Gaussian Markov Random Fields. ), GLCM (
See also Theoretical Comparison of Texture Algorithms, A. ), Fractal Dimension (
See also Improved Fractal Geometry Based Texture Segmentation Technique. ), Gabor Convolution Energies (
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HTML Version. Strictly speaking only an online "paper," with no printed reference at this time. A means to evaluate texture algorithms with a database, results of comparing several well-known algorithms, implementations, descriptions, programs, etc. Algorithms categories include: Grey Level Cooccurrence Matrices (
See also Textural Features for Image Classification.
See also Theoretical Comparison of Texture Algorithms, A. ), Gabor Energies (
See also Gabor Filters as Texture Discriminator. ), and Gauss Markov Random Fields (
See also Classification of Textures Using Gaussian Markov Random Fields. ). BibRef 9700
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Filtering for Texture Classification: A Comparative Study,
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See also Textured Image Segmentation. ), Ring/Wedge filters, Dyadic (wavelet) Gabor Decompositions(
See also Texture Segmentation Using 2-D Gabor Elementary Functions. ), DCT, Co-Occurrence (
See also Textural Features for Image Classification. ), Autoregressive, Daubechies wavelets, Eigenfilter, etc. No one approach did best, some did better on some images, worse on others. An important comment regards separation of test and training data, do not trust results that test on training data.
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Elsevier DOI 0010
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Elsevier DOI 0110
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Elsevier DOI 0201
Ferro, C.J.S.[Christopher J.S.],
Scale and Texture in Digital Image Classification,
PhEngRS(68), No. 1, January 2002, pp. 51-64. Simulated and actual data experiments were used to determine the effects various texture scales had upon a maximum-likelihood classifier and to suggest an approach that might aid in the selection of appropriate window sizes for texture feature extraction.
WWW Link. 0201
Hansen, L.K.[Lars Kai],
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DOI Link 0211
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PhEngRS(69), No. 4, April 2003, pp. 357-368. Three texture analysis methods, all based on different mathematical tools and all tested on high-resolution aerial photograph texture samples, are compared in dif ferent classification contexts, results are presented, and details of the experimental design for their comparison are explained.
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IP(14), No. 7, July 2005, pp. 925-936.
IEEE DOI 0506
Feature fusion for image texture segmentation,
IEEE DOI 0409
Comparing Cooccurrence Probabilities and Markov Random Fields for Texture Analysis of SAR Sea Ice Imagery,
GeoRS(42), No. 1, January 2004, pp. 215-228.
IEEE Abstract. 0402
Texture segmentation comparison using grey level co-occurrence probabilities and markov random fields,
IEEE DOI 0409
Clausi, D.A.[David A.],
Preserving boundaries for image texture segmentation using grey level co-occurring probabilities,
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Elsevier DOI 0512
Texture analysis using gaussian weighted grey level co-occurrence probabilities,
IEEE DOI 0408
Chantler, M.J.[Mike J.],
Van Gool, L.J.[Luc J.],
Editorial: Special Issue on 'Texture Analysis and Synthesis',
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DOI Link 0411
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IEEE DOI 1108
See also Efficient Texture Analysis of SAR Imagery. BibRef
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IEEE DOI 1201
Texture Content Based Successive Approximations for Image Compression and Recognition,
IEEE DOI 1603
approximation theory BibRef
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PRL(34), No. 15, 2013, pp. 2007-2022.
Elsevier DOI 1309
Dataset, Texture. Survey, Texture Datasets. Texture. BibRef
An appendix to 'Texture databases: A comprehensive survey',
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Elsevier DOI 1407
Asano, C.M.[Chie Muraki],
Statistical quantification of the effects of viewing distance on texture perception,
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From BoW to CNN: Two Decades of Texture Representation for Texture Classification,
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Springer DOI 1901
The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery,
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DOI Link 1906
Problems in Distortion Corrected Texture Classification and the Impact of Scale and Interpolation,
Springer DOI 1311
SAMATS: Texture extraction explained,
PDF File. 0902
Applied to 3D building descriptions. BibRef
Texture segmentation benchmark,
IEEE DOI 0812
Bai, Y.H.[Yoon Ho],
Relative advantage of touch over vision in the exploration of texture,
IEEE DOI 0812
A Comparison of Texture Features Based on SVM and SOM,
IEEE DOI 0609
The characterization of scanning noise and quantization on texture feature analysis,
IEEE DOI 0411
In medical screening application. BibRef
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IEEE DOI 0211
Classification experiments on real-world texture,
Evaluation of Textural Feature Extraction Schemes for Neural Network-based Interpretation of Regions in Medical Images,
IEEE DOI 0108
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Bowyer, K.W.[Kevin W.],
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Activities of IAPR: TC-2, Learning, Representation and Visualization of Intelligent Pattern Recognition,
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IEEE DOI 8811
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Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Texture Models, Analysis Techniques .