7.12 Co-occurrence Matrix Description Methods

Chapter Contents (Back)
Co-occurrence Matrix. Texture, Co-occurrence.

Haralick, R.M., Shanmugam, K., and Dinstein, I.,
Textural Features for Image Classification,
SMC(3), No. 6, November 1973, pp. 610-621. BibRef 7311
And: CMetImAly77(141-152). Co-occurrence Matrix. Classic cooccurrence matrix computation and use. BibRef

Haralick, R.M., Shanmugam, K.,
Combined Spectral and Spatial Processing of ERTS Imagery Data,
RSE(3), No. 1, 1974, pp. 3-13. BibRef 7400

Haralick, R.M., and Dinstein, I.,
A Spatial Clustering Procedure for Multi-Image Data,
CirSys(22), No. 5, May 1975, pp. 440-450. BibRef 7505

Haralick, R.M.,
A Resolution Preserving Textural Transformation for Images,
CGPR75(51-61). BibRef 7500

Pressman, N.J.[Norman Jules],
Optical Texture Analysis for Automatic Cytology and Histology: A Markovian Approach,
Ph.D.EE, October 12, 1976. UCRL-52155, BibRef 7610 UCBLLL. Co-occurrence Matrix. Texture, Evaluation. Optical texture-spatial variation of gray levels, no general theory, but a systematic, comparative investigation of quantitative texture measures; Markovian - gray level transition probabilities (Haralick See also Textural Features for Image Classification. ); gradient; granulometric - characterize basic elements (Galloway - See also Texture Analysis Using Gray Level Run Lengths. ); transform (Fourier); using texture of a known region to characterize the region, not for segmentation; evaluation of step size (optimum) is necessary for each application. BibRef

Galloway, M.M.,
Texture Analysis Using Gray Level Run Lengths,
CGIP(4), No. 2, June 1975, pp. 172-179.
WWW Link. BibRef 7506

Davis, L.S., Mitiche, A.,
Edge Detection in Textures,
CGIP(12), No. 1, January 1980, pp. 25-39.
WWW Link. BibRef 8001
Earlier: A2, A1:
Theoretical Analysis of Edge Detection in Textures,
ICPR80(540-547). Texture segmentation: See also MITES: A Model Driven, Iterative Texture Segmentation Algorithm. BibRef

Davis, L.S., Johns, S., and Aggarwal, J.K.,
Texture Analysis Using Generalized Co-Occurrence Matrices,
PAMI(1), No. 3, July 1979, pp. 251-259. BibRef 7907
Earlier: PRIP78(313-318). BibRef
And: A1, A3 only: PRAI-78(185-189). BibRef

Davis, L.S., Clearman, M., and Aggarwal, J.K.,
An Empirical Evaluation of Generalized Cooccurrence Matrices,
PAMI(3), No. 2, March 1981, pp. 214-221. BibRef 8103
Earlier:
A Comparative Texture Classification Study Based on Generalized Co-occurrence Matrices,
IEEE Conferenceon Decision Control, Miami, December 12-14, 1979. BibRef

Davis, L.S.[Larry S.],
Polarograms: A New Tool for Image Texture Analysis,
PR(13), No. 3, 1981, pp. 219-223.
WWW Link. 0309
BibRef

Sun, C., Wee, W.G.,
Neighboring Gray Level Dependence Matrix for Texture Classification,
CVGIP(23), No. 3, September 1983, pp. 341-352.
WWW Link. BibRef 8309

Trivedi, M.M.[Mohan M.], Harlow, C.A.[Charles A.], Conners, R.W.[Richard W.], and Goh, S.[Semoon],
Object Detection Based on Gray Level Cooccurrence,
CVGIP(28), No. 2, November 1984, pp. 199-219.
WWW Link. Matching, Textures. BibRef 8411

Harlow, C.A.[Charles A.], Trivedi, M.M.[Mohan M.], and Conners, R.W.[Richard W.],
Use of Texture Operators in Image Segmentation,
OptEng(25), No. 11, November 1986, pp. 1200-1206. BibRef 8611

Gotlieb, C.C., and Kreyszig, H.E.,
Texture Descriptors Based on Co-occurrence Matrices,
CVGIP(51), No. 1, July 1990, pp. 70-86.
WWW Link. BibRef 9007

Picard, R.W., Elfadel, I.M.,
Structure of the Aura and Co-Occurrence Matrices for the Gibbs Texture Model,
JMIV(2), 1992, pp. 5-25. BibRef 9200

Elfadel, I.M.[Ibrahim M.], and Picard, R.W.[Rosalind W.],
Gibbs Random Fields, Cooccurrences, and Texture Modeling,
PAMI(16), No. 1, January 1994, pp. 24-37.
IEEE DOI BibRef 9401
And: Vismod-204, 1992.
HTML Version. BibRef

Picard, R.W., Elfadel, I.M., Pentland, A.P.,
Markov/Gibbs Texture Modeling: Aura Matrices and Temperature Effects,
CVPR91(371-377).
IEEE DOI BibRef 9100
And: Vismod-164, 1991.
HTML Version. BibRef

Picard, R.W., Elfadel, I.M.,
On the Structure of Aura and Co-Occurrence Matrices for the Gibbs Texture Model,
Vismod-160, 1991.
HTML Version. BibRef 9100

Elfadel, I.M., Picard, R.W.,
New Miscibility Measure Explains the Behavior of Grayscale Texture Synthesized By Gibbs Random Fields,
Vismod-159, 1991.
HTML Version. BibRef 9100

Elfadel, I.M.[Ibrahim M.], and Yuille, A.L.,
Mean-Field Phase Transistions and Correlation Functions for Gibbs Random Fields,
JMIV(3), 1993, pp. 167-186. BibRef 9300

Picard, R.W.,
Structured Patterns From Random Fields,
Vismod200, 1992.
HTML Version. BibRef 9200
And:
Random Field Texture Coding,
Vismod-185, 1992.
HTML Version. BibRef
Earlier:
Gibbs Random Fields: Temperature and Parameter Analysis,
Vismod177, 1992.
HTML Version. BibRef

Picard, R.W., Pentland, A.P.,
Markov/Gibbs Image Modeling: Temperature and Texture,
Vismod-175, 1991.
HTML Version. BibRef 9100

Park, D.J., Nam, K.M., Park, R.H.,
Edge-Detection in Noisy Images Based on the Cooccurrence Matrix,
PR(27), No. 6, June 1994, pp. 765-775.
WWW Link. BibRef 9406

Hong, T.H., Dyer, C.R., and Rosenfeld, A.,
Texture Classification Using Gray Level Co-Occurrence Based on Edge Maxima,
SMC(10), 1980, pp. 158-163. BibRef 8000
And: A2, A1, A3: UMD-CS-TR-738, March 1979 See also TR 759, 779, 763. BibRef

Hong, T.H., Dyer, C.R., and Rosenfeld, A.,
Texture Primitive Extraction Using an Edge-Based Approach,
SMC(10), 1980, pp. 659-675. BibRef 8000

Hong, T.H., Wu, A.Y., and Rosenfeld, A.,
Feature Value Smoothing as an Aid in Texture Analysis,
SMC(10), 1980, pp. 519-524. BibRef 8000

Cohn-Sfetou, S.[Sorin],
Topics on Generalized Convolution and Fourier Transforms: Theory and Applications in Digital Signal Processing and System Theory,
Ph.D.Thesis (EE), McMaster Univ., Hamilton, Ontario, 1976. Convolution; transform on quadratic and multiplicative abelian groups, Walsh functions. BibRef 7600

Shirvaikar, M.V.[Mukul V.], and Trivedi, M.M.[Mohan M.],
Image Clutter Characterization for Object Detection in High Clutter Images,
OptEng(31), No. 12, December 1992, pp. 2628-2639. Target Recognition. BibRef 9212
Earlier:
Studies in Robust Approaches to Object Detection in High Clutter Background,
SPIE(1468), Applications of AI IX, Orlando, April 1991, pp. 52-59. BibRef

Shirvaikar, M.V.[Mukul V.], and Trivedi, M.M.[Mohan M.],
A Novel Unsupervised Multiresolution Texture Segmentation Approach,
SPIE(2223), Characterization and Propagation of Sources and Backgrounds IV, Orlando, FL, April 6-7, 1994. Gray level cooccurrence computations. BibRef 9404

Copeland, A.C., and Trivedi, M.M.,
Texture Perception in Humans and Computers: Models and Psychophysical Experiments,
SPIE(2742), 1996, pp. 436-446. BibRef 9600

Trivedi, M.M., and Shirvaikar, M.V.,
Quantitative Characterization of Image Clutter: Problems, Progress, and Promises,
SPIE(1967), Characterization, Propagation, and Simulation of Sources and Backgrounds, Orlando, FL, April 12-13, 1993. BibRef 9304

Harlow, C.A., Trivedi, M.M., and Conners, R.W.,
Texture Operators in Segmentation,
SPIE(548), Applications of Artificial Intelligence II, Arlington, VA, April 1985, pp. 10-18. Cooccurrence operators for aerial image segmentation. BibRef 8504

Muhamad, A.K.[Anwar K.], Deravi, F.[Farzin],
Neural Networks for the Classification of Image Texture,
EngAAI(7), No. 4, 1994, pp. 381-393. Neural Networks. BibRef 9400

Oja, E., Valkealahti, K.,
Cooccurrence Map: Quantizing Multidimensional Texture Histograms,
PRL(17), No. 7, June 10 1996, pp. 723-730. 9607
BibRef

Oja, E.[Erkki], Valkealahit, K.[Kimmo],
Reduced Multidimensional Histograms in Color Texture Description,
ICPR98(Vol II: 1057-1061).
IEEE DOI 9808
BibRef

Valkealahti, K.[Kimmo], and Oja, E.[Erkki],
Reduced Multidimensional Texture Histograms,
SCIA97(xx-yy)
HTML Version. 9705
BibRef

Kovalev, V.A., Petrou, M.,
Multidimensional Cooccurrence Matrices for Object Recognition and Matching,
GMIP(58), No. 3, May 1996, pp. 187-197. 9606
BibRef

Petrou, M., Mohanna, F., Kovalev, V.A.,
3D non-linear invisible boundary detection filters,
3DPVT04(970-978).
IEEE DOI 0412
Huiman distinguish up to second order statistics. But tumors may not differ in second order. MRI analysis. BibRef

Petrou, M., Kovalev, V.A., Reichenbach, J.R.,
Three-Dimensional Nonlinear Invisible Boundary Detection,
IP(15), No. 10, October 2006, pp. 3020-3032.
IEEE DOI 0609
BibRef

Ramana, K.V., Ramamoorthy, B.,
Statistical-Methods to Compare the Texture Features of Machined Surfaces,
PR(29), No. 9, September 1996, pp. 1447-1459.
WWW Link. Machined Surfaces. Co-occurrence Matrix. Run Length Code. BibRef 9609

Parkkinen, J., Selkainaho, K., Oja, E.,
Detecting Texture Periodicity from the Cooccurrence Matrix,
PRL(11), 1990, pp. 43-50. BibRef 9000

Valkealahti, K.[Kimmo], Oja, E.[Erkki],
Reduced Multidimensional Cooccurrence Histograms in Texture Classification,
PAMI(20), No. 1, January 1998, pp. 90-94.
IEEE DOI 9803
BibRef

Valkealahti, K., Oja, E.,
Texture Classification with Single and Multiresolution Cooccurrence Maps,
PRAI(12), No. 4, June 1998, pp. 437-452. 9808
BibRef

Tang, X.,
Texture Information in Run-length Matrices,
IP(7), No. 11, November 1998, pp. 1602-1609.
IEEE DOI BibRef 9811

Lee, J.C.M., Pong, T.C., Esterline, A.,
Enhancing Object Recognition Using Regency and Cooccurrence Heuristics,
PR(31), No. 9, September 1998, pp. 1319-1336.
WWW Link. 9808
BibRef

Soh, L.K., Tsatsoulis, C.,
Texture Analysis of SAR Sea Ice Imagery Using Gray Level Co-Occurrence Matrices,
GeoRS(37), No. 2, March 1999, pp. 780.
IEEE Top Reference. BibRef 9903

Soh, L.K., Tsatsoulis, C., Gineris, D., Bertoia, C.,
ARKTOS: An Intelligent System for SAR Sea Ice Image Classification,
GeoRS(42), No. 1, January 2004, pp. 229-248.
IEEE Abstract. 0402
BibRef

Carr, J.R., Pellon de Miranda, F.,
The Semivariogram in Comparison to the Co-Occurrence Matrix for Classification of Image Texture,
GeoRS(36), No. 6, November 1998, pp. 1945.
IEEE Top Reference. BibRef 9811

Chetverikov, D.[Dmitry],
Texture analysis using feature-based pairwise interaction maps,
PR(32), No. 3, March 1999, pp. 487-502.
WWW Link. BibRef 9903
Earlier:
Texture analysis using pairwise interaction maps,
CIAP97(I: 95-102).
Springer DOI 9709
BibRef
Earlier:
Structural Filtering with Texture Feature Based Interaction Maps: Fast Algorithms and Applications,
ICPR96(II: 795-799).
IEEE DOI 9608
(Hungarian Academy of Sciences, H) BibRef

Gimel'farb, G.L.[Georgy L.],
Modeling image textures by Gibbs random fields,
PRL(20), No. 11-13, November 1999, pp. 1123-1132. 0001
BibRef

Gimel'farb, G.L.[Georgy L.],
Image Textures and Gibbs Random Fields,
KluwerSeptember 1999, ISBN 0-7923-5961-5.
WWW Link. BibRef 9909

Gimel'farb, G.L.,
Non-Markov Gibbs Texture Model with Multiple Pairwise Pixel Interactions,
ICPR96(II: 591-595).
IEEE DOI 9608
(V.M. Glushkov Institute of Cybernetics, UKR) See also Texture Modelling with Nested High-Order Markov-Gibbs Random Fields. BibRef

Gimel'farb, G.L.[Georgy L.],
Texture Modelling and Segmenting by Multiple Pairwise Pixel Interactions,
ICIP96(III: 133-136).
IEEE DOI BibRef 9600

Gimel'farb, G.L.,
Gibbs Models for Bayesian Simulation and Segmentation of Piecewise-Uniform Textures,
ICPR96(II: 760-764).
IEEE DOI 9608
(V.M. Glushkov Institute of Cybernetics, UKR) BibRef

Lafarge, F., Gimel'farb, G.L.,
Texture Representation by Geometric Objects using a Jump-Diffusion Process,
BMVC08(xx-yy).
PDF File. 0809
BibRef

Montiel, E.[Eugenia], Aguado, A.S.[Alberto S.], Nixon, M.S.[Mark S.],
Texture classification via conditional histograms,
PRL(26), No. 11, August 2005, pp. 1740-1751.
WWW Link. 0506
BibRef

Hammouche, K., Diaf, M., Postaire, J.G.,
A clustering method based on multidimensional texture analysis,
PR(39), No. 7, July 2006, pp. 1265-1277.
WWW Link. 0606
Cluster analysis; Texture; Co-occurrence matrices; Feature selection BibRef

Vadivel, A., Sural, S.[Shamik], Majumdar, A.K.,
An Integrated Color and Intensity Co-occurrence Matrix,
PRL(28), No. 8, 1 June 2007, pp. 974-983.
WWW Link. 0704
Co-occurrence matrix; HSV color space; ICICM; Image retrieval BibRef

Gelzinis, A., Verikas, A., Bacauskiene, M.,
Increasing the discrimination power of the co-occurrence matrix-based features,
PR(40), No. 9, September 2007, pp. 2367-2372.
WWW Link. 0705
Image texture; Co-occurrence matrix; Support vector machine BibRef

Mirowski, P.W.[Piotr W.], Tetzlaff, D.M.[Daniel M.],
Retrieving scale from quasi-stationary images,
PRL(29), No. 6, 15 April 2008, pp. 754-767.
WWW Link. 0803
Multi-scale; Rotation-guided; Texture characterization; Gray-Level Co-occurrence matrices; Quasi-stationary images BibRef

Partio, M.[Mari], Cramariuc, B.[Bogdan], Gabbouj, M.[Moncef],
An Ordinal Co-occurrence Matrix Framework for Texture Retrieval,
JIVP(2007), 2007, pp. xx-yy.
DOI Link 0804
BibRef

Chalumeau, T., da Fontoura Costa, L.[Luciano], Laligant, O., Meriaudeau, F.,
Complex networks: Application for Texture Characterization and Classification,
ELCVIA(7), No. 3, 2008, pp. xx-yy.
WWW Link. 0909
Networks of single pixels. BibRef

Lategahn, H., Gross, S., Stehle, T., Aach, T.,
Texture Classification by Modeling Joint Distributions of Local Patterns With Gaussian Mixtures,
IP(19), No. 6, June 2010, pp. 1548-1557.
IEEE DOI 1006
BibRef

Asha, V., Bhajantri, N.U., Nagabhushan, P.,
GLCM-based chi-square histogram distance for automatic detection of defects on patterned textures,
IJCVR(2), No. 4, 2011, pp. 302-313.
DOI Link 1202
BibRef

Gupta, M.[Mousumi], Bhaskar, D.[Debasish], Bera, R.[Rabindranath], Biswas, S.[Sambhunath],
Target detection of ISAR data by principal component transform on co-occurrence matrix,
PRL(33), No. 13, 1 October 2012, pp. 1682-1688.
Elsevier DOI 1208
ISAR; Target; Co-occurrence matrix; PCT; Covariance; Surveillence BibRef

Tsai, F.[Fuan], Lai, J.S.[Jhe-Syuan],
Feature Extraction of Hyperspectral Image Cubes Using Three-Dimensional Gray-Level Cooccurrence,
GeoRS(51), No. 6, 2013, pp. 3504-3513.
IEEE DOI 1307
feature extraction; 3D gray level cooccurrence; volumetric data set; BibRef

Song, T.C.[Tie-Cheng], Xu, L.F.[Lin-Feng], Huang, C.[Chao], Luo, B.[Bing],
Texture Representation via Joint Statistics of Local Quantized Patterns,
IEICE(E97-D), No. 1, January 2013, pp. 155-159.
WWW Link. 1402
BibRef

Song, T.C.[Tie-Cheng], Li, H.L.[Hong-Liang], Meng, F.[Fanman], Wu, Q.[Qingbo], Luo, B.[Bing],
Exploring space-frequency co-occurrences via local quantized patterns for texture representation,
PR(48), No. 8, 2015, pp. 2621-2632.
Elsevier DOI 1505
Texture classification BibRef

Wang, H.X.[Hong-Xing], Yuan, J.S.[Jun-Song], Wu, Y.[Ying],
Context-Aware Discovery of Visual Co-Occurrence Patterns,
IP(23), No. 4, April 2014, pp. 1805-1819.
IEEE DOI 1404
feature extraction BibRef

Bianconi, F.[Francesco], Fernández, A.[Antonio],
Rotation invariant co-occurrence features based on digital circles and discrete Fourier transform,
PRL(48), No. 1, 2014, pp. 34-41.
Elsevier DOI 1410
Texture classification BibRef

Huang, X.[Xin], Liu, X.B.[Xiao-Bo], Zhang, L.P.[Liang-Pei],
A Multichannel Gray Level Co-Occurrence Matrix for Multi/Hyperspectral Image Texture Representation,
RS(6), No. 9, 2014, pp. 8424-8445.
DOI Link 1410
BibRef

Verma, M.[Manisha], Raman, B.[Balasubramanian],
Center symmetric local binary co-occurrence pattern for texture, face and bio-medical image retrieval,
JVCIR(32), No. 1, 2015, pp. 224-236.
Elsevier DOI 1511
Content based image retrieval BibRef

Qi, X.[Xianbiao], Shen, L.L.[Lin-Lin], Zhao, G.Y.[Guo-Ying], Li, Q.Q.[Qing-Quan], Pietikäinen, M.[Matti],
Globally rotation invariant multi-scale co-occurrence local binary pattern,
IVC(43), No. 1, 2015, pp. 16-26.
Elsevier DOI 1512
Multi-scale co-occurrence LBP BibRef


Ren, H.[Haoyu], Li, Z.N.[Ze-Nian],
Object Detection Using Generalization and Efficiency Balanced Co-Occurrence Features,
ICCV15(46-54)
IEEE DOI 1602
Boosting See also Gender Recognition Using Complexity-Aware Local Features. See also Age Estimation Based on Complexity-Aware Features. BibRef

Ledoux, A.[Audrey], Losson, O.[Olivier], Macaire, L.[Ludovic],
Texture classification with fuzzy color co-occurrence matrices,
ICIP15(1429-1433)
IEEE DOI 1512
Co-occurrence matrices; Color image; Fuzzy set; Texture classification BibRef

Zou, Q.[Qin], Qi, X.B.[Xian-Biao], Li, Q.Q.[Qing-Quan], Wang, S.[Song],
Discriminative regional color co-occurrence descriptor,
ICIP15(696-700)
IEEE DOI 1512
co-occurrence feature BibRef

Liu, G.[Gang], Xia, G.S.[Gui-Song], Yang, W.[Wen], Zhang, L.P.[Liang-Pei],
Texture Analysis with Shape Co-occurrence Patterns,
ICPR14(1627-1632)
IEEE DOI 1412
Analytical models BibRef

Mensink, T.[Thomas], Gavves, E.[Efstratios], Snoek, C.G.M.[Cees G.M.],
COSTA: Co-Occurrence Statistics for Zero-Shot Classification,
CVPR14(2441-2448)
IEEE DOI 1409
BibRef

Alaoui, M.T.[Mohammed Talibi], Sbihi, A.[Abderrahmane],
Texture Classification Based on Co-occurrence Matrix and Neuro-Morphological Approach,
CIAP13(II:510-521).
Springer DOI 1309
BibRef

Nosaka, R.[Ryusuke], Suryanto, C.H.[Chendra Hadi], Fukui, K.[Kazuhiro],
Rotation Invariant Co-occurrence among Adjacent LBPs,
CVLBP12(I:15-25).
Springer DOI 1304
BibRef

Qi, X.B.[Xian-Biao], Xiao, R.[Rong], Li, C., Qiao, Y., Guo, J.[Jun], Tang, X.,
Pairwise Rotation Invariant Co-Occurrence Local Binary Pattern,
PAMI(36), No. 11, November 2014, pp. 2199-2213.
IEEE DOI 1410
Encoding BibRef

Qi, X.B.[Xian-Biao], Xiao, R.[Rong], Guo, J.[Jun], Zhang, L.[Lei],
Pairwise Rotation Invariant Co-Occurrence Local Binary Pattern [Conf],
ECCV12(VI: 158-171).
Springer DOI 1210
BibRef

Morioka, N.[Nobuyuki], Satoh, S.[Shin'ichi],
Compact correlation coding for visual object categorization,
ICCV11(1639-1646).
IEEE DOI 1201
Spatial relations between local features. BibRef

Costianes, P.J.[Peter J.], Plock, J.B.[Joseph B.],
Gray-level co-occurrence matrices as features in edge enhanced images,
AIPR10(1-6).
IEEE DOI 1010
BibRef

Zhang, R.[Rui], Yin, B.L.[Bao-Lin], Zhao, Q.[Qiyang], Yang, B.[Bin],
An efficient color image classification method using gradient magnitude based angle cooccurrence matrix,
ICIP10(1073-1076).
IEEE DOI 1009
BibRef

Fujiwara, T.[Takayuki], Koshimizu, H.[Hiroyasu], Hashimoto, M.[Manabu],
Application of Co-Occurrence Frequency Image,
MVA09(126-).
PDF File. 0905
BibRef

Ito, S.[Satoshi], Kubota, S.[Susumu],
Object Classification Using Heterogeneous Co-Occurrence Features,
ECCV10(II: 209-222).
Springer DOI 1009
BibRef
And: ECCV10(V: 701-714).
Springer DOI 1009
BibRef

Ni, B.B.[Bing-Bing], Yan, S.C.[Shui-Cheng], Kassim, A.A.[Ashraf A.],
Contextualizing histogram,
CVPR09(1682-1689).
IEEE DOI 0906
Incorporate context into histogram analysis. Co-occurrence features. BibRef

Cheong, M., Loke, K.S.,
An approach to texture-based image recognition by deconstructing multispectral co-occurrence matrices using Tchebichef orthogonal polynomials,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Porebski, A.[Alice], Vandenbroucke, N.[Nicolas], Macaire, L.[Ludovic],
Haralick feature extraction from LBP images for color texture classification,
IPTA08(1-8).
IEEE DOI 0811
BibRef

Patel, M.B.[Mehul B.], Rodriguez, J.J.[Jeffrey J.], Gmitro, A.F.[Arthur F.],
Effect of gray-level re-quantization on co-occurrence based texture analysis,
ICIP08(585-588).
IEEE DOI 0810
BibRef

de O. Bastos, L., Liatsis, P., Conci, A.,
Automatic texture segmentation based on k-means clustering and efficient calculation of co-occurrence features,
WSSIP08(141-144).
IEEE DOI 0806
BibRef

Winter, M., Bischof, H.,
Binary Co-occurrences of Weak Descriptors,
BMVC07(xx-yy).
PDF File. 0709
BibRef

Tsai, F.[Fuan], Chang, C.K.[Chun-Kai], Rau, J.Y.[Jian-Yeo], Lin, T.H.[Tang-Huang], Liu, G.R.[Gin-Ron],
3D Computation of Gray Level Co-occurrence in Hyperspectral Image Cubes,
EMMCVPR07(429-440).
Springer DOI 0708
BibRef

Tahir, M.A., Bouridane, A., Kurugollu, F., Amira, A.,
Accelerating the Computation of GLCM and Haralick Texture Features on Reconfigurable Hardware,
ICIP04(V: 2857-2860).
IEEE DOI 0505
BibRef

Partio, M., Cramariuc, B., Gabbouj, M.,
Block-based Ordinal Co-occurrence Matrices for Texture Similarity Evaluation,
ICIP05(I: 517-520).
IEEE DOI 0512
BibRef
Earlier:
Texture similarity evaluation using ordinal co-occurrence,
ICIP04(III: 1537-1540).
IEEE DOI 0505
BibRef

Schwartz, W.R., Pedrini, H.,
Textured Image Segmentation Based on Spatial Dependence using a Markov Random Field Model,
ICIP06(2449-2452).
IEEE DOI 0610
BibRef
Earlier:
Texture classification based on spatial dependence features using co-occurrence matrices and markov random fields,
ICIP04(I: 239-242).
IEEE DOI 0505
BibRef

Zwiggelaar, R.,
Texture based segmentation: Automatic Selection of Co-occurrence Matrices,
ICPR04(I: 588-591).
IEEE DOI 0409
BibRef

Hao, P.W.[Peng-Wei], Shi, Q.Q.[Qi-Qyun], Chen, Y.[Ying],
Co-histogram and its application in remote sensing image compression evaluation,
ICIP03(III: 177-180).
IEEE DOI 0312
BibRef

Metzler, V., Palm, C., Lehmann, T., Aach, T.,
Texture Classification of Graylevel Images by Multiscale Cross-cooccurrence Matrices,
ICPR00(Vol II: 549-552).
IEEE DOI 0009
BibRef

Andersen, J.D., Hansen, K.,
Analysis of Image Structure by Generalized Co-occurrence Matrices,
SCIA99(Image Analysis). BibRef 9900

Ojala, T., Pietikäinen, M., Kyllönen, J.,
Gray Level Cooccurrence Histograms via Learning Vector Quantization,
SCIA99(Neural Nets). BibRef 9900

Svalbe, I.D.[Imants D.], Evans, C.J.[Carolyn J.],
Localisation of Image Features Using Measures of Rank Distribution,
ICPR98(Vol I: 189-191).
IEEE DOI 9808
BibRef

Hofmann, T., Puzicha, J.,
Mixture models for co-occurrence and histogram data,
ICPR98(Vol I: 192-194).
IEEE DOI 0403
BibRef

Bello, F., Kitney, R.I.,
Co-Occurrence Based Texture Analysis Using Irregular Tessellations,
ICPR96(II: 780-784).
IEEE DOI 9608
(Imperial College of Science, UK) BibRef

Lohmann, G.,
Co-occurrence-based analysis and synthesis of textures,
ICPR94(A:449-453).
IEEE DOI 9410
BibRef

Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Structural Methods for Texture Description .


Last update:Mar 13, 2017 at 16:25:24