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2006. Dataset, Texture.
MIT Texture Data,
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2006. Dataset, Texture.
Outex: New framework for empirical evaluation of
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2006. Dataset, Texture.
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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.
Describable Textures Dataset (DTD),
2014 Dataset, Texture.
See also Describing Textures in the Wild.
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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 (
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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,
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Hansen, L.K.[Lars Kai],
Guest Editorial: Special Issue on Statistics of Shapes and Textures,
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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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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
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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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IEEE DOI 1108
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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
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Elsevier DOI 1407
Asano, C.M.[Chie Muraki],
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From BoW to CNN: Two Decades of Texture Representation for Texture Classification,
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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
Comparison of color imaging vs. hyperspectral imaging for texture classification,
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Texture representation, Color imaging, Hyperspectral imaging, Feature selection BibRef
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
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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,
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IEEE DOI 0108
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Activities of IAPR: TC-2, Learning, Representation and Visualization of Intelligent Pattern Recognition,
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Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Texture Models, Analysis Techniques .