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2006. Dataset, Texture.
MIT Texture Data,
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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.
Describable Textures Dataset (DTD),
2014 Dataset, Texture.
See also Describing Textures in the Wild.
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Elsevier DOI BibRef 9602
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Elsevier DOI 9606
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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 (
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See also Classification of Textures Using Gaussian Markov Random Fields. ). BibRef 9700
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IEEE DOI Texture, Evaluation. Reviews the major filter approaches, noting the past problems and conflicting results for some evaluations. Laws filters (
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
Brief review of invariant texture analysis methods,
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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.
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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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Comparison of Gray-Level Reduction and Different Texture Spectrum Encoding Methods for Land-Use Classification Using a Panchromatic Ikonos Image,
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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
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',
IJCV(62), No. 1-2, April-May 2005, pp. 5-5.
DOI Link 0411
Image Processing: Dealing with Texture,
Wiley2006, ISBN: 0-470-02628-6.
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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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Visual Signal Analysis: Focus on Texture Similarity,
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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,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906
Comparison of color imaging vs. hyperspectral imaging for texture classification,
PRL(161), 2022, pp. 115-121.
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Texture representation, Color imaging, Hyperspectral imaging, Feature selection BibRef
The Impact of the Type and Spatial Resolution of a Source Image on the Effectiveness of Texture Analysis,
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DOI Link 2301
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,
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IEEE DOI 0108
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Bowyer, K.W.[Kevin W.],
Evaluation of Texture Segmentation Algorithms,
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Texture: Plus Ca Change,
Springer DOI BibRef 9200
Wang, P.S.P.[Patrick S.P.],
Activities of IAPR: TC-2, Learning, Representation and Visualization of Intelligent Pattern Recognition,
Comparison of X2 and K Statistics in Finding Signal and Picture Periodicity,
IEEE DOI 8811
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PRIP79(618-622). BibRef 7900
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RBCV-TR-90-33, Toronto, July 1990. Master's thesis, a review of various statistical texture analysis methods. BibRef 9007
Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Texture Models, Analysis Techniques .