7.10 Statistical Methods for Texture Description and Analysis

Pickett, R.M.,
Visual Analysis of Texture in the Detection and Recognition of Objects,
PPP70(289-308). BibRef 7000

Deutsch, E.S., Belknap, N.J.,
Texture Descriptions Using Neighborhood Information,
CGIP(1), No. 2, August 1972, pp. 145-168.
Elsevier DOI BibRef 7208

Troy, E.B., Deutsch, E.S., Rosenfeld, A.,
Gray-Level Manipulation Experiments for Texture Analysis,
SMC(3), No. 1, January 1973, pp. 91-98. BibRef 7301

Hayes, Jr., K.C., Shah, A.N., Rosenfeld, A.,
Texture Coarseness: Further Experiments,
SMC(4), 1974, pp. 467-472. BibRef 7400

Besag, J.,
Spatial Interaction and the Statistical Analysis of Lattice Systems,
RoyalStat(B-36), No. 2, 1974, pp. 192-236. The MRF formulation. BibRef 7400

Besag, J.,
On the Statistical Analysis of Dirty Pictures,
RoyalStat(B-48), No. 3, 1986, pp. 259-302. Energy minimization. Greedy algorithm to find minimum. BibRef 8600

Besag, J.,
Statistical Analysis of Nonlattice Data,
Statistician(24), No. 3, 1975, pp. 179-195. BibRef 7500

Larimore, W.E.,
Statistical Interaction and Statistical Analysis of Lattice Systems,
PIEEE(65), No. 6, June 1977, pp. 961-970. BibRef 7706

Mills, H.D.[Harlan D.],
On information content in patterns,
CGIP(14), No. 3, November 1980, pp. 183-202.
Elsevier DOI 0501
Sampled planar k-colored pattern. BibRef

Morganthaler, D.G., Wang, C.Y., Rosenfeld, A.,
Two Remarks on Multidimensional Texture Analysis,
PRL(1), 1982, pp. 103-105. BibRef 8200

Pietikainen, M.[Matti], Rosenfeld, A.[Azriel],
Edge Based Texture Measures,
SMC(12), 1982, pp. 585-594. BibRef 8200
Earlier: ICPR82(298-300). BibRef

Shen, H.C.[Helen C.], Wong, A.K.C.[Andrew K.C.],
Generalized Texture Representation and Metric,
CVGIP(23), No. 2, August 1983, pp. 187-206.
Elsevier DOI BibRef 8308

Shipley, T.[Thorne], van Houten, P.[Peter],
Perception of order within disorder: 1. Visual ranking of random textures,
PR(17), No. 4, 1984, pp. 465-473.
Elsevier DOI 0309
BibRef

Shipley, T.[Thorne], Shore, H.[Harold],
The human texture visual field: Fovea-to-periphery pattern recognition,
PR(23), No. 11, 1990, pp. 1215-1221.
Elsevier DOI 0401
BibRef

Flack, V.F.[Virginia Foard],
Using probabilities in analyzing two dimensional spatial patterns,
PR(18), No. 5, 1985, pp. 357-359.
Elsevier DOI 0309
Display of data for analysis. BibRef

Therrien, C.W., Quatieri, T.F., Dudgeon, D.E.,
Statistical Model-Based Algorithms for Image Analysis,
PIEEE(74), 1986, pp. 532-551.
See also Estimation-Theoretic Approach to Terrain Image Segmentation, An. BibRef 8600

Eichmann, G., Kasparis, T.,
Topologically Invariant Texture Descriptors,
CVGIP(41), No. 3, March 1988, pp. 267-281.
Elsevier DOI image property, statistics. BibRef 8803

Pachowitz, P.W.,
Integrating Low-Level Features Computation with Inductive Learning Techniques for Texture Recognition,
PRAI(4), 1990, pp. 147-165. BibRef 9000

Pachowicz, P.W.,
Local Characteristics of Binary Images and Their Application to the Automatic Control of Low-Level Robot Vision,
CVIP92(67-102). BibRef 9200

Wu, C.M.[Chung-Ming], Chen, Y.C.[Yung-Chang],
Statistical Feature Matrix for Texture Analysis,
GMIP(54), No. 5, September 1992, pp. 407-419. BibRef 9209

Muzzolini, R.[Russell], Yang, Y.H.[Yee-Hong], Pierson, R.[Roger],
Texture Characterization Using Robust Statistics,
PR(27), No. 1, January 1994, pp. 119-134.
Elsevier DOI BibRef 9401

Ganesan, L., Bhattacharyya, P.,
A statistical design of experiments approach for texture description,
PR(28), No. 1, January 1995, pp. 99-105.
Elsevier DOI 0401
BibRef

Ganesan, L., Bhattacharyya, P.,
A New Statistical Approach for Micro Texture Description,
PRL(16), No. 5, May 1995, pp. 471-478. BibRef 9505

Scharcanski, J.[Jacob], Dodson, C.T.J.,
Texture Analysis for Estimating Spatial Variability and Anisotropy in Planar Stochastic Structures,
OptEng(35), No. 8, August 1996, pp. 2302-2309. 9609
BibRef

Scharcanski, J.[Jacob],
Stochastic texture analysis for monitoring stochastic processes in industry,
PRL(26), No. 11, August 2005, pp. 1701-1709.
Elsevier DOI 0506
BibRef

Scharcanski, J.[Jacob],
A Wavelet-Based Approach for Analyzing Industrial Stochastic Textures With Applications,
SMC-A(37), No. 1, January 2007, pp. 10-22.
IEEE DOI 0701
BibRef

Lowitz, G.E.[Gabriel E.],
Can a Local Histogram Really Map Texture Information?,
PR(16), No. 2, 1983, pp. 141-147.
Elsevier DOI 9611
BibRef

Lowitz, G.E.[Gabriel E.],
Mapping the local information content of a spatial image,
PR(17), No. 5, 1984, pp. 545-550.
Elsevier DOI 0309
BibRef

Dasarathy, B.V., Holder, E.B.,
Image Characterizations Based on Joint Gray Level-Run Length Distributions,
PRL(12), No. 8, August 1991, pp. 497-502. BibRef 9108

Saito, N., Coifman, R.R.,
Local Discriminant Bases and Their Applications,
JMIV(5), No. 4, December 1995, pp. 337-358. BibRef 9512

Lee, J.H., Lee, N.I., Kim, S.D.,
A Fast and Adaptive Method to Estimate Texture Statistics by the Spatial Gray Level Dependence Matrix for Texture Image Segmentation,
PRL(13), 1992, pp. 291-303. BibRef 9200

Shen, H.C., Bie, C.Y.C.,
Feature Frequency Matrices as Texture Image Representation,
PRL(13), 1992, pp. 195-205. BibRef 9200

de Floriani, L., Jeanne, P., Nagy, G.,
Visibility-Related Image Features,
PRL(13), 1992, pp. 463-470. BibRef 9200

Chu, A., Sehgal, C.M., Greenleaf, J.F.,
Use of Gray Value Distribution of Run Lengths for Texture Analysis,
PRL(11), 1990, pp. 415-420. BibRef 9000

He, D.C., Wang, L., Guibert, J.,
Texture Feature Extraction,
PRL(6), 1987, pp. 269-273. BibRef 8700

Strickland, R.N.,
Estimation of Local Statistics for Digital Processing of Nonstationary Images,
ASSP(33), 1985, pp. 465-469. BibRef 8500

Politis, D.N.,
A Simple Information Theoretic Proof of the Maximum Entropy Property of Some Gaussian Random Fields,
IP(3), No. 6, November 1994, pp. 865-868.
IEEE DOI BibRef 9411

Popat, K.[Kris], Picard, R.W.,
Cluster-Based Probability Model and Its Application to Image and Texture Processing,
IP(6), No. 2, February 1997, pp. 268-284.
IEEE DOI 9703
BibRef
Earlier:
Cluster-Based Probability Model Applied to Image Restoration and Compression,
Vismod-253, 1993.
HTML Version. BibRef
Earlier:
Novel Cluster-Based Probability Model for Texture Synthesis, Classification, and Compression,
Vismod-234, 1993.
HTML Version. BibRef

Liu, S.C., Chang, S.,
Dimension Estimation of Discrete-Time Fractional Brownian-Motion with Applications to Image Texture Classification,
IP(6), No. 8, August 1997, pp. 1176-1184.
IEEE DOI 9708
BibRef

Tan, T.N.,
Noise Robustness of Texture Features,
IVC(15), No. 11, November 1997, pp. 815-817.
Elsevier DOI 9712
BibRef

Szirányi, T.[Tamás],
Statistical Pattern Recognition of Low Resolution Pictures,
PRL(8), 1988, pp. 221-228. BibRef 8800

Szirányi, T.[Tamáss],
Texture Recognition Using a Superfast Cellular Neural Network VLSI Chip in a Real Experimental Environment,
PRL(18), No. 11-13, November 1997, pp. 1329-1334. 9806
BibRef

Szirányi, T.[Tamás],
Noise effects in statistical subpixel pattern recognition,
CAIP93(74-81).
Springer DOI 9309
BibRef
Earlier:
Statistical subpixel pattern recognition by histograms,
ICPR92(II:705-708).
IEEE DOI 9208
BibRef

Sanna, A., Montrucchio, B., Sparavigna, A.,
A Parallel Algorithm of Texture Analysis for Liquid Crystal Investigation,
PRL(20), No. 2, February 1999, pp. 183-190. BibRef 9902

Baheerathan, S., Albregtsen, F., Danielsen, H.E.,
New texture features based on the complexity curve,
PR(32), No. 4, April 1999, pp. 605-618.
Elsevier DOI BibRef 9904

Garcia, P.[Pedro], Petrou, M.[Maria], Kamata, S.,
The Use of Boolean Model for Texture Analysis of Grey Images,
CVIU(74), No. 3, June 1999, pp. 227-235.
DOI Link BibRef 9906
Earlier: A2 and A1 only: ICPR98(Vol I: 811-813).
IEEE DOI 9808
BibRef

Agapov, I.A., Kashkin, V.B., Khlebopros, R.G.,
Identification of random fields of points,
SP:IC(13), No. 1, July 1998, pp. 21-43.
Elsevier DOI BibRef 9807

Wu, Y.N.[Ying Nian], Zhu, S.C.[Song Chun], Liu, X.W.[Xiu-Wen],
Equivalence of Julesz Ensembles and FRAME Models,
IJCV(38), No. 3, July-August 2000, pp. 247-265.
DOI Link 0006
BibRef
Earlier:
Equivalence of Julesz and Gibbs Texture Ensembles,
ICCV99(1025-1032).
IEEE DOI Award, Marr Prize, HM. BibRef

Zhu, S.C.[Song Chun], Wu, Y.N., Mumford, D.[David],
Filters, Random-Fields and Maximum-Entropy (Frame): Towards a Unified Theory for Texture Modeling,
IJCV(27), No. 2, March 1998, pp. 107-126.
DOI Link 9805
BibRef
Earlier:
FRAME: Filters, Random fields and Maximum Entropy: Towards the Unified Theory for Texture Modeling,
CVPR96(686-693).
IEEE DOI BibRef

Guo, C.E.[Cheng-En], Zhu, S.C.[Song-Chun], Wu, Y.N.[Ying Nian],
Towards a mathematical theory of primal sketch and sketchability,
ICCV03(1228-1235).
IEEE DOI 0311
Model for texture. By filters or bases. BibRef

Wu, Y.N.[Ying Nian], Zhu, S.C.[Song-Chun], Guo, C.E.[Cheng-En],
Statistical Modeling of Texture Sketch,
ECCV02(III: 240 ff.).
Springer DOI 0205
BibRef

Zhu, S.C.[Song-Chun], Guo, C.E.[Cheng-En],
Conceptualization and Modeling of Visual Patterns,
PercOrg01(xx-yy). 0106
BibRef

Zhu, S.C.[Song Chun], Wu, Y., Mumford, D.[David],
Minimax Entropy Principles and Its Application to Texture Modeling,
NeurComp(9), No. 8, 1997, pp. 1627-1660. BibRef 9700

Zhu, S.C.[Song-Chun],
Statistical modeling and conceptualization of visual patterns,
PAMI(25), No. 6, June 2003, pp. 691-712.
IEEE Abstract. 0306
A Visual pattern is equalized to a statistical ensemble and models for various patterns form a continuous spectrum. BibRef

Guo, C.E.[Cheng-En], Zhu, S.C.[Song-Chun], Wu, Y.N.[Ying Nian],
Primal sketch: Integrating structure and texture,
CVIU(106), No. 1, April 2007, pp. 5-19.
Elsevier DOI 0704
Sparse coding; Markov random fields; Image primitives; Sketch graphs; Lossy image coding BibRef

Lindsey, C.S.[Clark S.], Strömberg, M.[Michael],
Image classification using the frequencies of simple features,
PRL(21), No. 3, March 2000, pp. 265-268. 0003
BibRef

Foresti, G.L., Gentili, S.,
Noise-Robust and Invariant Object Classification by the High-Order Statistical Pattern Spectrum,
PRAI(13), No. 8, December 1999, pp. 1219. 0005
BibRef

Ojala, T.[Timo], Valkealahti, K.[Kimmo], Oja, E.[Erkki], Pietikäinen, M.[Matti],
Texture discrimination with multidimensional distributions of signed gray-level differences,
PR(34), No. 3, March 2001, pp. 727-739.
Elsevier DOI 0101

See also Image Segmentation by Texture Using Pyramid Node Linking. BibRef

Turtinen, M.[Markus], Mäenpää, T.[Topi], Pietikäinen, M.[Matti],
Texture Classification by Combining Local Binary Pattern Features and a Self-Organizing Map,
SCIA03(1162-1169).
Springer DOI 0310
BibRef

Mäenpää, T.[Topi], Pietikäinen, M.[Matti],
Multi-scale Binary Patterns for Texture Analysis,
SCIA03(885-892).
Springer DOI 0310
BibRef

Pietikainen, M., Nurmela, T., Maenpaa, T., Turtinen, M.,
View-based recognition of 3D-textured surfaces,
CIAP03(530-535).
IEEE DOI 0310
BibRef

Ojala, T.[Timo], Pietikäinen, M.[Matti], Mäenpää, T.[Topi],
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns,
PAMI(24), No. 7, July 2002, pp. 971-987.
IEEE Abstract. 0207
BibRef
Earlier:
Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns,
ECCV00(I: 404-420).
Springer DOI 0003
Find the uniform patterns for any quantization. BibRef

Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.,
Computer Vision Using Local Binary Patterns,
Springer2011. ISBN: 978-0-85729-747-1.
WWW Link. 1109
BibRef

Ahonen, T.[Timo], Pietikäinen, M.[Matti],
Pixelwise Local Binary Pattern Models of Faces Using Kernel Density Estimation,
ICB09(52-61).
Springer DOI 0906
BibRef

Ylioinas, J.[Juha], Hong, X.P.[Xiao-Peng], Pietikäinen, M.[Matti],
Constructing Local Binary Pattern Statistics by Soft Voting,
SCIA13(119-130).
Springer DOI 1311
BibRef

Ylioinas, J.[Juha], Hadid, A.[Abdenour], Guo, Y.[Yimo], Pietikäinen, M.[Matti],
Efficient Image Appearance Description Using Dense Sampling Based Local Binary Patterns,
ACCV12(III:375-388).
Springer DOI 1304
BibRef

Takala, V.[Valtteri], Ahonen, T.[Timo], Pietikäinen, M.[Matti],
Block-Based Methods for Image Retrieval Using Local Binary Patterns,
SCIA05(882-891).
Springer DOI 0506
BibRef

Zhao, G., Ahonen, T.[Timo], Matas, J.G.[Jirí G.], Pietikäinen, M.[Matti],
Rotation-Invariant Image and Video Description With Local Binary Pattern Features,
IP(21), No. 4, April 2012, pp. 1465-1477.
IEEE DOI 1204
BibRef

Ahonen, T.[Timo], Matas, J.G.[Jirí G.], He, C.[Chu], Pietikäinen, M.[Matti],
Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features,
SCIA09(61-70).
Springer DOI 0906
BibRef

Maenpaa, T.[Topi], Ojala, T.[Timo], Pietikäinen, M.[Matti], Soriano, M.[Maricor],
Robust Texture Classification by Subsets of Local Binary Patterns,
ICPR00(Vol III: 935-938).
IEEE DOI 0009
BibRef

Maenpaa, T.[Topi], Pietikäinen, M.[Matti], Ojala, T.[Timo],
Texture Classification by Multi-predicate Local Binary Pattern Operators,
ICPR00(Vol III: 939-942).
IEEE DOI 0009
BibRef

Dong, P.,
Test of a new lacunarity estimation method for image texture analysis,
JRS(21), No. 17, November 2000, pp. 3369-3373. 0102
BibRef

Johansson, J.O.[Jan-Olof],
Parameter-estimation in the auto-binomial model using the coding- and pseudo-likelihood method approached with simulated annealing and numerical optimization,
PRL(22), No. 11, September 2001, pp. 1233-1246.
Elsevier DOI 0108

See also Spatial Interaction and the Statistical Analysis of Lattice Systems. BibRef

Mukundan, R., Ong, S.H., Lee, P.A.,
Image analysis by Tchebichef moments,
IP(10), No. 9, September 2001, pp. 1357-1364.
IEEE DOI 0108
BibRef

Yap, P.T.[Pew-Thian], Paramesran, R.[Raveendran], Ong, S.H.[Seng-Huat],
Image analysis by Krawtchouk moments,
IP(12), No. 11, November 2003, pp. 1367-1377.
IEEE DOI 0311
BibRef

Yap, P.T.[Pew-Thian], Paramesran, R.[Raveendran], Ong, S.H.[Seng-Huat],
Image Analysis Using Hahn Moments,
PAMI(29), No. 11, November 2007, pp. 2057-2062.
IEEE DOI 0711
Show how Hahn moments, are a generalization of Chebyshev and Krawtchouk moments. BibRef

Rushing, J.A.[John A.], Ranganath, H.S.[Heggere S.], Hinke, T.H.[Thomas H.], Graves, S.J.[Sara J.],
Using Association Rules as Texture Features,
PAMI(23), No. 8, August 2001, pp. 845-858.
IEEE DOI 0109
Association rules as applied in other domains. Capture structural and statistical information. Capture frequently occurring local structures. Comparison with GLCM
See also Use of Gray Value Distribution of Run Lengths for Texture Analysis. (And Duda/Hart
See also Pattern Classification and Scene Analysis. ), GLRL
See also On Perceptual Analyzers Underlying Visual Texture Discrimination: Part II. Fractal
See also N-Folded Symmetries by Complex Moments in Gabor Space and Their Application to Unsupervised Texture Segmentation. Markov
See also Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow. Gabor
See also Classification of Textures Using Gaussian Markov Random Fields. BibRef

Yang, X.Y.[Xiang-Yu], Liu, J.[Jun],
Maximum entropy random fields for texture analysis,
PRL(23), No. 1-3, January 2002, pp. 93-101.
Elsevier DOI 0201
BibRef

Fwu, J.K.[Jong-Kae], Djuric, P.M.,
Unsupervised vector image segmentation by a tree structure-ICM algorithm,
MedImg(15), No. 6, December 1996, pp. 871-880.
IEEE Top Reference. 0203
Extend:
See also Spatial Interaction and the Statistical Analysis of Lattice Systems. with a tree-structured approach. BibRef

Fwu, J.K., Djuric, P.M.,
EM Algorithm for Image Segmentation Initialized by a Tree Structure Scheme,
IP(6), No. 2, February 1997, pp. 349-352.
IEEE DOI 9703
BibRef

Ferraro, M.[Mario], Boccignone, G.[Giuseppe], Caelli, T.M.[Terry M.],
Entropy-based representation of image information,
PRL(23), No. 12, October 2002, pp. 1391-1398.
Elsevier DOI 0206
BibRef
Earlier: A2, A1, A3:
Entropy Production in Colour Images,
ICPR00(Vol I: 202-205).
IEEE DOI 0009
BibRef
Earlier:
Local Structure in Images from Entropy Production,
ICIP99(I:343-347).
IEEE DOI BibRef

Kupinski, M.A.[Matthew A.], Clarkson, E.W.[Eric W.], Hoppin, J.W.[John W.], Chen, L.Y.[Li-Ying], Barrett, H.H.[Harrison H.],
Experimental determination of object statistics from noisy images,
JOSA-A(20), No. 3, March 2003, pp. 421-429.
WWW Link. 0304
BibRef

Baik, S.W.[Sung Wook], Pachowicz, P.W.,
Online model modification for adaptive texture recognition in image sequences,
SMC-A(32), No. 6, November 2002, pp. 625-639.
IEEE Top Reference. 0301
BibRef

Lahajnar, F.[Franci], Kovacic, S.[Stanislav],
Rotation-invariant texture classification,
PRL(24), No. 9-10, June 2003, pp. 1151-1161.
Elsevier DOI 0304
BibRef

Campisi, P., Neri, A., Panci, G., Scarano, G.,
Robust rotation-invariant texture classification using a model based approach,
IP(13), No. 6, June 2004, pp. 782-791.
IEEE DOI 0406
BibRef
Earlier: A1, A2, A4 only:
Model based rotation-invariant texture classification,
ICIP02(III: 117-120).
IEEE DOI 0210
BibRef

Johnson, A.P.[Aaron P.], Baker, Jr., C.L.[Curtis L.],
First- and second-order information in natural images: A Filter-Based Approach to Image Statistics,
JOSA-A(21), No. 6, June 2004, pp. 913-925.
WWW Link. 0409
BibRef

Johnson, A.P.[Aaron P.], Kingdom, F.A.[Frederick A.], Baker, Jr., C.L.[Curtis L.],
Spatiochromatic statistics of natural scenes: first- and second-order information and their correlational structure,
JOSA-A(22), No. 10, October 2005, pp. 2050-2059.
WWW Link. 0601
BibRef

Lee, K.L.[Kuen-Long], Chen, L.H.[Ling-Hwei],
An efficient computation method for the texture browsing descriptor of MPEG-7,
IVC(23), No. 5, 1 May 2005, pp. 479-489.
Elsevier DOI 0501
Characterize a texture's regularity, directionality and coarseness. BibRef

Du, F.[Feng], Shi, W.K.[Wen-Kang], Chen, L.Z.[Liang-Zhou], Deng, Y.[Yong], Zhu, Z.F.[Zhen-Fu],
Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization (PSO),
PRL(26), No. 5, April 2005, pp. 597-603.
Elsevier DOI 0501
BibRef

Puig, D.[Domènec], Garcia, M.Á.[Miguel Ángel],
Automatic texture feature selection for image pixel classification,
PR(39), No. 11, November 2006, pp. 1996-2009.
Elsevier DOI 0608
BibRef
Earlier:
Automatic Selection of Multiple Texture Feature Extraction Methods for Texture Pattern Classification,
IbPRIA05(II:215).
Springer DOI 0509
BibRef
Earlier:
Recognizing specific texture patterns by integration of multiple texture methods,
ICIP02(I: 125-128).
IEEE DOI 0210
BibRef
And: A2, A1:
Improving texture pattern recognition by integration of multiple texture feature extraction methods,
ICPR02(III: 7-10).
IEEE DOI 0211
Multiple texture methods; Multiple evaluation windows BibRef

Garcia, M.A.[Miguel Angel], Puig, D.[Domenec],
Supervised texture classification by integration of multiple texture methods and evaluation windows,
IVC(25), No. 7, 1 July 2007, pp. 1091-1106.
Elsevier DOI 0705
Supervised texture classification; Multiple texture methods; Multiple evaluation windows; Kullback J-divergence; MeasTex; LBP; Edge flow; JSEG
See also Unsupervised Segmentation of Color-Texture Regions in Images and Video. ; Fabric defect detection BibRef

Melendez, J.[Jaime], Puig, D.[Domenec], Garcia, M.A.[Miguel Angel],
On Adapting Pixel-based Classification to Unsupervised Texture Segmentation,
ICPR10(854-857).
IEEE DOI 1008
BibRef
Earlier:
Comparative Evaluation of Classical Methods, Optimized Gabor Filters and LBP for Texture Feature Selection and Classification,
CAIP07(912-920).
Springer DOI 0708
BibRef

Cantoni, V., Casanova, A., Fraschini, M., Vitulano, S.,
Equilibrium and dissipative structures role on images,
PRL(28), No. 14, 15 October 2007, pp. 1865-1872.
Elsevier DOI 0710
Entropy; Order and disorder; Structures For 1-D signals. BibRef

Chainais, P.[Pierre],
Infinitely Divisible Cascades to Model the Statistics of Natural Images,
PAMI(29), No. 12, December 2007, pp. 2105-2119.
IEEE DOI 0711
BibRef
Earlier:
Multi-Dimensional Infinitely Divisible Cascades to Model the Statistics of Natural Images,
ICIP05(III: 129-132).
IEEE DOI 0512
Model images using Infinitely Divisible Cascades. BibRef

Gabarda, S.[Salvador], Cristóbal, G.[Gabriel],
Discrimination of isotrigon textures using the Rényi entropy of Allan variances,
JOSA-A(25), No. 9, September 2008, pp. 2309-2319.
WWW Link. 0804
BibRef

Marcos, J.V.[J. Víctor], Cristóbal, G.[Gabriel],
Texture classification using discrete Tchebichef moments,
JOSA-A(30), No. 8, August 2013, pp. 1580-1591.
WWW Link. 1309
BibRef

Khellah, F.M.,
Texture Classification Using Dominant Neighborhood Structure,
IP(20), No. 11, November 2011, pp. 3270-3279.
IEEE DOI 1110
BibRef

Chandler, D.M.[Damon M.], Field, D.J.[David J.],
Estimates of the information content and dimensionality of natural scenes from proximity distributions,
JOSA-A(24), No. 4, April 2007, pp. 922-941.
WWW Link. Esitmate dimensionality of the image. BibRef 0704

Field, D.J.[David J.], Chandler, D.M.[Damon M.],
Method for estimating the relative contribution of phase and power spectra to the total information in natural-scene patches,
JOSA-A(29), No. 1, January 2012, pp. 55-67.
WWW Link. 1201
BibRef

Fathi, A.[Abdolhossein], Naghsh-Nilchi, A.R.[Ahmad Reza],
Noise tolerant local binary pattern operator for efficient texture analysis,
PRL(33), No. 9, 1 July 2012, pp. 1093-1100.
Elsevier DOI 1202
Local binary pattern; Texture pattern descriptor; Texture analysis BibRef

Aptoula, E.[Erchan],
Comparative study of moment based parameterization for morphological texture description,
JVCIR(23), No. 8, November 2012, pp. 1213-1224.
Elsevier DOI 1211
Texture analysis; Morphological Covariance; Granulometry; Parameterization; Statistical moments; Moment invariants; Fourier transform; Noise robustness BibRef

Gonzalez-Castro, V., Alegre, E., Garcia-Olalla, O., Fernandez-Robles, L., Garcia-Ordas, M.T.,
Adaptive pattern spectrum image description using Euclidean and Geodesic distance without training for texture classification,
IET-CV(6), No. 6, 2012, pp. 581-589.
DOI Link 1301
BibRef

de Mesquita Sá, Jr., J.J.[Jarbas Joaci], Backes, A.R.[André Ricardo], Cortez, P.C.[Paulo César],
A Simplified Gravitational Model for Texture Analysis,
JMIV(47), No. 1-2, September 2013, pp. 70-78.
Springer DOI 1307
BibRef
Earlier: A1, A2, Only: CAIP11(I: 26-33).
Springer DOI 1109
lacunarity method BibRef

de Mesquita Sá, Jr., J.J.[Jarbas Joaci], Backes, A.R.[André R.],
A Gravitational Model for Grayscale Texture Classification Applied to the pap-smear Database,
CIAP15(II:332-339).
Springer DOI 1511
BibRef

de Mesquita Sá, Jr., J.J.[Jarbas Joaci], Backes, A.R.[André Ricardo],
ELM based signature for texture classification,
PR(51), No. 1, 2016, pp. 395-401.
Elsevier DOI 1601
Texture classification BibRef

Wang, K.[Kai], Bichot, C.E., Zhu, C.[Chao], Li, B.L.[Bai-Lin],
Pixel to Patch Sampling Structure and Local Neighboring Intensity Relationship Patterns for Texture Classification,
SPLetters(20), No. 9, 2013, pp. 853-856.
IEEE DOI 1308
eye BibRef

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Local binary circumferential and radial derivative pattern for texture classification,
PR(67), No. 1, 2017, pp. 213-229.
Elsevier DOI 1704
Texture classification BibRef

Lee, K.[Kwanghyun], Moorthy, A.K., Lee, S.H.[Sang-Hoon], Bovik, A.C.,
3D Visual Activity Assessment Based on Natural Scene Statistics,
IP(23), No. 1, January 2014, pp. 450-465.
IEEE DOI 1402
Gaussian processes BibRef

Dolloff, J.[John], Doucette, P.[Peter],
The Sequential Generation of Gaussian Random Fields for Applications in the Geospatial Sciences,
IJGI(3), No. 2, 2014, pp. 817-852.
DOI Link 1407
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Voloshynovskiy, S.[Svyatoslav], Diephuis, M.[Maurits], Holotyak, T.[Taras], Standardo, N.[Nabil],
Physical object identification using micro-structure images,
SPIE(Newsroom), December 1, 2014
DOI Link 1501
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Pham, T.D.[Tuan D.],
The Kolmogorov-Sinai entropy in the setting of fuzzy sets for image texture analysis and classification,
PR(53), No. 1, 2016, pp. 229-237.
Elsevier DOI 1602
Chaos BibRef

Pham, T.D.,
The Semi-Variogram and Spectral Distortion Measures for Image Texture Retrieval,
IP(25), No. 4, April 2016, pp. 1556-1565.
IEEE DOI 1604
image classification BibRef

Pham, T.D.[Tuan D.],
Noise-Added Texture Analysis,
CIARP16(93-100).
Springer DOI 1703
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Guo, J.[Jie], Song, B.[Bin], Tian, F.[Fang], Qin, H.[Hao],
Texture classification with cross-covariance matrices in compressive measurement domain,
SIViP(10), No. 8, November 2016, pp. 1377-1384.
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SPLetters(24), No. 4, April 2017, pp. 402-406.
IEEE DOI 1704
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Entropies and cross-entropies of exponential families,
ICIP10(3621-3624).
IEEE DOI 1009
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Hajati, F.[Farshid], Tavakolian, M.[Mohammad], Gheisari, S.[Soheila], Gao, Y., Mian, A.S.[Ajmal Saeed],
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HMS(47), No. 6, December 2017, pp. 970-982.
IEEE DOI 1712
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Earlier: A1, A2, A3, A5, Only:
Spatiotemporal Derivative Pattern: A Dynamic Texture Descriptor for Video Matching,
ACCV14(V: 626-641).
Springer DOI 1504
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Earlier: A2, A1, A5, A3, Only:
Sparse Variation Pattern for Texture Classification,
DICTA13(1-6)
IEEE DOI 1402
Face recognition, Heuristic algorithms, Hidden Markov models, Manifolds, spatiotemporal BibRef

Tavakolian, M.[Mohammad], Hadid, A.[Abdenour],
Deep Discriminative Model for Video Classification,
ECCV18(II: 401-418).
Springer DOI 1810
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Mirhashemi, A.[Arash],
Introducing spectral moment features in analyzing the SpecTex hyperspectral texture database,
MVA(29), No. 3, April 2018, pp. 415-432.
WWW Link. 1804
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Budianto, Lun, D.P.K.[Daniel P. K.], Chan, Y.H.[Yuk-Hee],
Robust Single-Shot Fringe Projection Profilometry Based on Morphological Component Analysis,
IP(27), No. 11, November 2018, pp. 5393-5405.
IEEE DOI 1809
image representation, image texture, iterative methods, optical projectors, optimisation, statistical analysis, wavelets BibRef

Zitouni, A.[Abdelkader], Benkouider, F.[Fatiha], Chouireb, F.[Fatima], Belkheiri, M.[Mohammed],
Classification of textured images based on new information fusion methods,
IET-IPR(13), No. 9, 18 July 2019, pp. 1540-1549.
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Dong, Y., Wu, H., Li, X., Zhou, C., Wu, Q.,
Multiscale Symmetric Dense Micro-Block Difference for Texture Classification,
CirSysVideo(29), No. 12, December 2019, pp. 3583-3594.
IEEE DOI 1912
Feature extraction, Histograms, Gaussian mixture model, Encoding, Support vector machines, Task analysis, Texture classification, support vector machine BibRef

Lacombe, T.[Thomas], Favreliere, H.[Hugues], Pillet, M.[Maurice],
Modal features for image texture classification,
PRL(135), 2020, pp. 249-255.
Elsevier DOI 2006
Feature extraction, Discrete modal decomposition, Texture classification BibRef

Gao, X.[Xing], Wu, X.[Xu], Xu, P.P.[Pan-Pan], Guo, S.H.[Shi-Hui], Liao, M.H.[Ming-Hong], Wang, W.C.[Wen-Cheng],
Semi-Supervised Texture Filtering With Shallow to Deep Understanding,
IP(29), 2020, pp. 7537-7548.
IEEE DOI 2007
Texture filtering, image translation, Generative Adversarial Networks (GANs), semi-supervised BibRef

Cerkezi, L.[Llukman], Topal, C.[Cihan],
Towards more discriminative features for texture recognition,
PR(107), 2020, pp. 107473.
Elsevier DOI 2008
Local binary patterns, Texture recognition, Feature representations, Feature optimization, Mutual information BibRef

Morel, C.[Cristina], Humeau-Heurtier, A.[Anne],
Multiscale permutation entropy for two-dimensional patterns,
PRL(150), 2021, pp. 139-146.
Elsevier DOI 2109
Texture images, Normalized permutation entropy, Multi-scale analysis BibRef

Gaudêncio, A.S.[Andreia S.], Hilal, M.[Mirvana], Cardoso, J.M.[João M.], Humeau-Heurtier, A.[Anne], Vaz, P.G.[Pedro G.],
Texture analysis using two-dimensional permutation entropy and amplitude-aware permutation entropy,
PRL(159), 2022, pp. 150-156.
Elsevier DOI 2206
Bioinformatics, Entropy, Information theory, Texture, BibRef

Song, K.Y.[Kai-You], Yang, H.[Hua], Yin, Z.P.[Zhou-Ping],
Multi-Scale Boosting Feature Encoding Network for Texture Recognition,
CirSysVideo(31), No. 11, November 2021, pp. 4269-4282.
IEEE DOI 2112
Feature extraction, Encoding, Interference, Image coding, Boosting, Image recognition, Task analysis, Texture recognition, convolutional neural network BibRef

Chen, Z.[Zhile], Quan, Y.H.[Yu-Hui], Xu, R.[Ruotao], Jin, L.W.[Lian-Wen], Xu, Y.[Yong],
Enhancing texture representation with deep tracing pattern encoding,
PR(146), 2024, pp. 109959.
Elsevier DOI 2311
Texture recognition, Deep learning, Feature encoding BibRef

Pereyra, M.[Marcelo], Vargas-Mieles, L.A.[Luis A.], Zygalakis, K.C.[Konstantinos C.],
The Split Gibbs Sampler Revisited: Improvements to Its Algorithmic Structure and Augmented Target Distribution,
SIIMS(16), No. 4, 2023, pp. 2040-2071.
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WWW Link. 2312
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Zhai, W.[Wei], Cao, Y.[Yang], Zhang, J.[Jing], Xie, H.Y.[Hai-Yong], Tao, D.C.[Da-Cheng], Zha, Z.J.[Zheng-Jun],
On Exploring Multiplicity of Primitives and Attributes for Texture Recognition in the Wild,
PAMI(46), No. 1, January 2024, pp. 403-420.
IEEE DOI 2312
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Kang, S.[Sheng], Wang, Y.[Yang], Cao, Y.[Yang], Zha, Z.J.[Zheng-Jun],
Long-Range Feature Dependencies Capturing for Low-Resolution Image Classification,
MMMod22(II:3-14).
Springer DOI 2203
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Florindo, J.B.[Joao B.], Backes, A.R.[Andre R.], Neckel, A.[Acacio],
ELMP-Net: The successive application of a randomized local transform for texture classification,
PR(153), 2024, pp. 110499.
Elsevier DOI 2405
Extreme learning descriptors, Texture classification, Image descriptors, Local binary patterns BibRef

Mamidibathula, B.[Bharat], Amirneni, S.[Satakarni], Sistla, S.S.[Sai Shravani], Patnam, N.[Niharika],
Texture Classification Using Capsule Networks,
IbPRIA19(I:589-599).
Springer DOI 1910
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Zhang, L., Rusinkiewicz, S.,
Learning to Detect Features in Texture Images,
CVPR18(6325-6333)
IEEE DOI 1812
Detectors, Feature extraction, Task analysis, Pipelines, Training, Databases, Asphalt BibRef

Xue, J.[Jia], Zhang, H.[Hang], Dana, K.[Kristin],
Deep Texture Manifold for Ground Terrain Recognition,
CVPR18(558-567)
IEEE DOI
PDF File. 1812
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And: Encoding, Manifolds, Databases, Videos, Image recognition, Feature extraction, Training BibRef

Xue, J.[Jia], Wadekar, P.[Paras], Zhang, H.[Hang], Teran, L.[Leizer], Dana, K.[Kristin], Nishino, K.[Ko],
Ground Terrain Database, GTOS,
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HTML Version. Dataset, Texture. BibRef 1700

Portilla, J.[Javier], Martínez-Enríquez, E.[Eduardo],
Nested Normalizations for Decoupling Global Features,
ICIP18(2112-2116)
IEEE DOI 1809
Trajectory, Radio frequency, Couplings, Solids, Mutual information, Manifolds, Standards, global features, level sets BibRef

Saito, Y., Miyata, T.,
Recovering Texture of Denoised Image via its Statistical Analysis,
ICIP18(1767-1771)
IEEE DOI 1809
Image denoising, Noise reduction, Silicon, Noise measurement, Visualization, Minimization, AWGN, image denoising, image texture, low-rank approximation BibRef

Song, Y.[Yang], Cai, W.D.[Wei-Dong], Li, Q.[Qing], Zhang, F.[Fan], Feng, D.D.[David Dagan], Huang, H.[Heng],
Fusing subcategory probabilities for texture classification,
CVPR15(4409-4417)
IEEE DOI 1510
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Viriri, S.[Serestina],
Characterization of Medical Images Using Edge Density and Local Directional Pattern (LDP),
ICIAR15(394-401).
Springer DOI 1507
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Cimpoi, M.[Mircea], Maji, S.[Subhransu], Kokkinos, I.[Iasonas], Mohamed, S.[Sammy], Vedaldi, A.[Andrea],
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CVPR14(3606-3613)
IEEE DOI 1409
Fisher Vector
See also Describable Textures Dataset (DTD). BibRef

Quan, Y.H.[Yu-Hui], Xu, Y.[Yong], Sun, Y.P.[Yu-Ping], Luo, Y.[Yu],
Lacunarity Analysis on Image Patterns for Texture Classification,
CVPR14(160-167)
IEEE DOI 1409
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Mao, J.H.[Jun-Hua], Zhu, J.[Jun], Yuille, A.L.[Alan L.],
An Active Patch Model for Real World Texture and Appearance Classification,
ECCV14(III: 140-155).
Springer DOI 1408
BAg of words model on patches. BibRef

Zhang, X.[Xiang], Wang, S.Q.[Shi-Qi], Ma, S.W.[Si-Wei], Liu, S.H.[Shao-Hui], Gao, W.[Wen],
Entropy of primitive: A top-down methodology for evaluating the perceptual visual information,
VCIP13(1-6)
IEEE DOI 1402
entropy BibRef

Pullaperuma, P.P., Dharmaratne, A.T.,
Taxonomy of File Fragments Using Gray-Level Co-Occurrence Matrices,
DICTA13(1-7)
IEEE DOI 1402
image colour analysis Image windows. BibRef

Naman, A.T.[Aous Thabit], Taubman, D.S.[David S.],
A soft measure for identifying structure from randomness in images,
ICIP13(2939-2943)
IEEE DOI 1402
Feature Extraction BibRef

Sifre, L.[Laurent], Mallat, S.[Stephane],
Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination,
CVPR13(1233-1240)
IEEE DOI 1309
affine BibRef

Timofte, R.[Radu], Van Gool, L.J.[Luc J.],
A Training-free Classification Framework for Textures, Writers, and Materials,
BMVC12(93).
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Pourreza, H.R.[Hamid Reza], Masoudifar, M.[Mina], ManafZade, M.[MohammadMahdi],
LSP: Local similarity pattern, a new approach for rotation invariant noisy texture analysis,
ICIP11(837-840).
IEEE DOI 1201
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Yimit, A.[Adiljan], Hagihara, Y.[Yoshihiro], Miyoshi, T.[Tasuku], Hagihara, Y.[Yukari],
A New Two-Dimensional Direction Histogram Based Entropic Thresholding,
ICIG11(106-110).
IEEE DOI 1109
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Kuang, Z.H.[Zhang-Hui], Pan, G.D.[Guo-Dong], Wong, K.Y.K.[Kwan-Yee K.],
Local multiple orientations estimation using k-medoids,
ICIP10(109-112).
IEEE DOI 1009
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Qiao, X.[Xu], Chen, Y.W.[Yen-Wei],
Statistical Texture Modeling for Medical Volume Using Generalized N-Dimensional Principal Component Analysis Method and 3D Volume Morphing,
ICPR10(2488-2491).
IEEE DOI 1008
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Corani, G.[Giorgio], Giusti, A.[Alessandro], Migliore, D.[Davide], Schmidhuber, J.[Juergen],
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Zhao, H.Y.[Hai-Ying], Xu, Z.G.[Zheng-Guang], Hong, P.[Peng],
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CISP09(1-4).
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Hong, X.P.[Xiao-Peng], Chang, H.[Hong], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin], Gao, W.[Wen],
Sigma Set: A small second order statistical region descriptor,
CVPR09(1802-1809).
IEEE DOI 0906

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Barros Filho, M.N., Sobreira, F.J.A.,
Accuracy of Lacunarity Algorithms in Texture Classification of High Spatial Resolution Images from Urban Areas,
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Okada, K.[Kazunori], Periaswamy, S.[Senthil], Bi, J.B.[Jin-Bo],
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Papa, J.P.[João P.], Falcão, A.X.[Alexandre X.], Suzuki, C.T.N.[Celso T. N.], Mascarenhas, N.D.A.[Nelson D. A.],
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Montoya-Zegarra, J.A.[Javier A.], Papa, J.P.[João P.], Leite, N.J.[Neucimar J.], da Silva Torres, R.[Ricardo], Falcão, A.X.[Alexandre X.],
Rotation-Invariant Texture Recognition,
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Springer DOI 0711
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Horikawa, Y.[Yo], Ohnishi, Y.J.[Yu-Jiro],
Comparison of Combining Methods of Correlation Kernels in kPCA and kCCA for Texture Classification with Kansei Information,
SCIA07(699-708).
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de Melo, R.H.C.[Rafael H. C.], de A. Vieira, E.[Evelyn], Conci, A.[Aura],
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Andra, S.[Srinivas], Wu, Y.J.[Yong-Jun],
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ICIAR05(754-761).
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Unal, G., Sharma, G., Eschbach, R.,
Efficient classification of scanned media using spatial statistics,
ICIP04(IV: 2395-2398).
IEEE DOI 0505
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Santamaria, C., Bober, M., Szajnowski, W.,
Texture analysis using level-crossing statistics,
ICPR04(II: 712-715).
IEEE DOI 0409
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Krüger, N.[Norbert], Wörgötter, F.[Florentin],
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BMCV02(239 ff.).
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Thielscher, A.[Axel], Schuboe, A.[Anna], Neumann, H.[Heiko],
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BMCV02(99 ff.).
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Jain, A.K., Xu, X.W.[Xiao-Wei], Ho, T.K.[Tin Kam], Xiao, F.[Fan],
Uniformity testing using minimal spanning tree,
ICPR02(IV: 281-284).
IEEE DOI 0211
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Verma, B., Kulkarni, S.,
Texture Feature Extraction and Classification,
CAIP01(228 ff.).
Springer DOI 0210
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Rangarajan, A.[Anand],
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EMMCVPR01(153-168).
Springer DOI 0205
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Zhang, J., Tan, T.,
New Texture Signatures and Their Use in Rotation Invariant Texture Classification,
Texture02(157-162). 0207
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Zhang, J., Tan, T.,
Affine Invariant Texture Signatures,
ICIP01(II: 618-621).
IEEE DOI 0108
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Zhu, S.C.,
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Texture02(1-2). 0207
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Chennbhotla, C., Jepson, A.D.,
Sparse Coding in Practice,
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Griffin, L.D.,
Image analysis for using histograms of infinitestimal neighbourhoods,
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Gimel'farb, G.L.[Georgy L.],
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Tupin, F., Sigelle, M., Maitre, H.,
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Maitre, H., Bloch, I., Sigelle, M.,
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ICIP00(Vol III: 584-587).
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Chantler, M.J., McGunnigle, G.,
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ICPR00(Vol III: 931-934).
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Chantler, M.J., McGunnigle, G.,
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ICPR00(Vol III: 943-946).
IEEE DOI 0009
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McGunnigle, G., Chantler, M.J.,
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Chantler, M.J., Russell, G.T., Linnett, L.M.,
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Chetverikov, D.[Dmitry], Földvári, Z.[Zoltan],
Affine-invariant Texture Classification,
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Manduchi, R., Portilla, J.,
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Nikolova, M.,
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ICIP99(II:11-15).
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Kuan, J., Joyce, D., Lewis, P.,
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de Jager, G., Nicolls, F.[Frederick],
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Dana, K.J.[Kristin J.], Nayar, S.K.[Shree K.],
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Ye, S.J.[Shin-Ju], Shen, J.[Jun], Keskes, N.[Naamen], and Rabiller, P.[Philippe],
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Walter, F.[Fredrik], Brandtberg, T.[Tomas],
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SCIA97(xx-yy)
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Hagner, O., Joyce, S.,
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SSAB97(Photogrammetry) 9703
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CVPR93(636-637).
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Herlin, I.L., Crettez, J.P.,
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ICPR88(II: 764-766).
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ICPR94(A:444-448).
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PRIP81(583-588). BibRef

Madiraju, S.V.R., Liu, C.C.A.[Chih-Chi-Ang],
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ICIP95(II: 639-642).
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ICPR92(III:49-52).
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CAIA84(585-590). BibRef 8400

Gonzalez, R.C., Barrero, A., Moret, B., Thompson, M.G.,
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Rosenfeld, A.,
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Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Texture Discrimination and Classification .