CUReT: Columbia-Utrecht Reflectance and Texture Database,
2006.
Dataset, Texture.
WWW Link.
MIT Texture Data,
1995.
Dataset, Texture.
HTML Version.
Texture Data,
2006.
Dataset, Texture.
WWW Link.
Outex: New framework for empirical evaluation of
texture analysis algorithms,
2006.
Dataset, Texture.
WWW Link.
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.
WWW Link.
Describable Textures Dataset (DTD),
2014
Dataset, Texture.
WWW Link.
See also Describing Textures in the Wild.
Ahuja, N.,
Schacter, B.,
Image Models,
Surveys(13), No. 4, December 1981, pp. 373-397.
BibRef
8112
And:
Comments:
Surveys(15), No. 1, March 1983, pp. 83-84.
Survey, Texture. Mostly about texture representation.
BibRef
Ahuja, N.,
Schacter, B.,
Pattern Models,
WileyNew York, 1983.
BibRef
8300
Schacter, B.,
Real Time Display of Textures,
ICPR80(789-791).
BibRef
8000
Wechsler, H.,
Texture Analysis: A Survey,
SP(2), 1980, 271-282.
Survey, Texture.
BibRef
8000
O'Toole, R.K.,
Stark, H.,
Comparative Study of Optical-Digital vs. All-Digital Techniques
in Textural Pattern Recognition,
AppOpt(19), 1980, 2496-2506.
BibRef
8000
Van Gool, L.J.,
Dewaele, P.,
Oosterlinck, A.,
Texture Analysis Anno 1983,
CVGIP(29), No. 3, 1985, pp. 336-357.
Elsevier DOI
Survey, Texture.
Texture, Survey. A recent review of texture analysis methods. Statistical and structural
methods.
Gray level difference method, filter mask texture measures, Fourier
power spectrum analysis, cooccurrence features, gray level run
lengths, autocorrelation features, methods derived from texture
models, relative extrema measures, and gray level profiles.
BibRef
8500
Haralick, R.M.,
Statistical Image Texture Analysis,
HPRIP86(247-279).
BibRef
8600
Tuceryan, M.,
Jain, A.K.,
Texture Analysis,
HPRCV92(II-1), 1993, pp. 235-276.
Texture review.
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9300
Rao, A.R.,
A Taxonomy for Texture Description and Identification,
Springer-Verlag:Berlin, 1990.
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9000
Ph.D.Thesis (EE), UMich.
The thesis as a
BibRef
BookCreates a measure to distinguish a
large number of textures.
BibRef
Tomita, F.,
Tsuji, S.,
Computer Analysis of Visual Textures,
Hingham, MA:
KluwerAcademic, August 1990.
ISBN 0-7923-9114-4.
Survey of theories and techniques for texture analysis.
WWW Link.
Survey, Texture.
BibRef
9008
Haralick, R.M.,
Statistical and Structural Approaches to Texture,
PIEEE(67), No. 5, May 1979, pp. 786-804.
BibRef
7905
Earlier:
ICPR78(45-69).
Survey, Texture.
Texture, Survey. A good review of texture.
BibRef
Julesz, B.,
Foundations of Cyclopean Perception,
The
University of Chicago Press1971.
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7100
Julesz, B.,
Visual Pattern Discrimination,
IT(8), No. 2, February 1962, pp. 84-92.
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6202
Julesz, B.,
Experiments in the Visual Perception of texture,
SciAmer(232), No. 4, April 1975, pp. 34-43.
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7504
Caelli, T.M.,
Julesz, B.,
On Perceptual Analyzers Underlying Visual
Texture Discrimination: Part I,
BioCyber(28), 1978, pp. 167-176.
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7800
Caelli, T.M.,
Julesz, B.,
Gilbert, E.N.,
On Perceptual Analyzers Underlying Visual Texture Discrimination:
Part II,
BioCyber(29), No. 4, 1978, pp. 201-214.
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Julesz, B.,
Caelli, T.M.,
On the Limits of Fourier Decompositions in Visual Texture Perception,
Perception(8), 1978, pp. 69-73.
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7800
Julesz, B.,
Gilbert, E.N.,
Victor, J.D.,
Visual Discrimination of Textures with
Identical Third-Order Statistics,
BioCyber(31), No. 3, 1979, pp. 137-140.
BibRef
7900
Julesz, B.,
Bergen, R.,
Textons, The Fundamental Elements in Preattentive Vision
and Perception of Textures,
Bell System Tech.(62), No. 6, 1983, Part II, pp. 1619-1645.
Reprinted in
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8300
RCV87(243-256).
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Julesz, B.,
Bergen, R.,
Textons, The Elements of Texture Perception, and Their Interactions,
Nature(290), 1981, pp. 91-97.
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8100
Julesz, B.,
Spatial Nonlinearities in the Instantaneous Perception of Textures
with Identical Power Spectra,
Royal(B-290), 1980, pp. 83-94.
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8000
Julesz, B.,
A Theory of Preattentive Texture Discrimination Based on First-Order
Statistics of Textons,
BioCyber(41), 1981, pp. 131-181.
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8100
Kashi, R.S.,
Papathomas, T.V.,
Gorea, A.,
Julesz, B.,
Similarities Between Texture Grouping and Motion Perception:
The Role of Color, Luminance, and Orientation,
IJIST(7), No. 2, Summer 1996, pp. 85-91.
9607
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Brodatz, P.,
Textures,
New York:
Dover1966.
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6600
BookThe standard reference for where to find natural textures.
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Longuet-Higgins, M.S.,
The Statistical Analysis of a Random Moving Surface,
Royal(A-249), February 1957, pp. 321-387.
See also Multiple Interpretations of a Pair of Images of a Surface.
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Longuet-Higgins, M.S.,
Statistical Properties of an Isotropic Random Surface,
Royal(A-250), October 1957, pp. 151-171.
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5710
Koenderink, J.J.,
van Doorn, A.J.,
Illuminance Texture Due to Surface Mesostructure,
JOSA-A(13), No. 3, March 1996, pp. 452-463.
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Stavridi, M.[Marigo],
Koenderink, J.J.,
Studies of 3D Model Textures,
ICIP96(III: 157-160).
IEEE DOI
9610
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Waksman, A.[Adlai],
Rosenfeld, A.[Azriel],
Sparse, Opaque Three-Dimensional Texture. I. Arborescent Patterns,
CVGIP(57), No. 3, May 1993, pp. 388-399.
DOI Link
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Waksman, A.[Adlai],
Rosenfeld, A.[Azriel],
Sparse, Opaque 3-Dimensional Texture, 2B: Photometry,
PR(29), No. 2, February 1996, pp. 297-313.
Elsevier DOI
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9602
And:
Erratum:
PR(29), No. 6, June 1996, pp. R1-R2.
Elsevier DOI
9606
BibRef
UMDTR3363, 1994.
WWW Link.
BibRef
Waksman, A.[Adlai],
Rosenfeld, A.[Azriel],
Sparse, Opaque 3-Dimensional Texture, 2A: Visibility,
GMIP(58), No. 2, March 1996, pp. 155-163.
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And:
UMDTR3333, 1994.
WWW Link. And
WWW Link.
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Waksman, A.[Adlai],
Rosenfeld, A.[Azriel],
Assessing the Condition of a Plant,
MVA(10), No. 1, 1997, pp. 35-41.
Springer DOI
9705
BibRef
Ohanian, P.P.[Philippe P.],
Dubes, R.C.[Richard C.],
Performance Evaluation for Four Classes of Textural Features,
PR(25), No. 8, August 1992, pp. 819-833.
Elsevier DOI
BibRef
9208
Zhu, Y.M.,
Goutte, R.,
A Comparison of Bilinear Space Spatial-Frequency Representations
for Texture-Discrimination,
PRL(16), No. 10, October 1995, pp. 1057-1068.
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9510
Smith, G.,
Burns, I.,
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PRL(18), No. 14, December 1997, pp. 1495-1501.
9806
MEASTEX.
Compared various methods. Implemented a number of them.
At least:M
MRF (
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 (
See also Gabor Filters as Texture Discriminator. ).
BibRef
Smith, G.[Guy],
Burns, I.[Ian],
Benchmarking Texture Classification Algorithms,
TR1997.
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
Randen, T.[Trygve],
Husøy, J.H.[John Håkon],
Filtering for Texture Classification: A Comparative Study,
PAMI(21), No. 4, April 1999, pp. 291-310.
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.
See also Texture Segmentation with Optimal Linear Prediction Error Filters.
BibRef
9904
Al-Janobi, A.[Abdulrahman],
Performance evaluation of cross-diagonal texture matrix method of
texture analysis,
PR(34), No. 1, January 2001, pp. 171-180.
Elsevier DOI
0010
BibRef
Sipilä, O.[Outi],
Visa, A.,
Salonen, O.,
Erkinjuntti, T.,
Katila, T.,
Experiences on data quality in automatic tissue classification,
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Elsevier DOI
0110
BibRef
Zhang, J.G.[Jian-Guo],
Tan, T.N.[Tie-Niu],
Brief review of invariant texture analysis methods,
PR(35), No. 3, March 2002, pp. 735-747.
Elsevier DOI
0201
BibRef
Ferro, C.J.S.[Christopher J.S.],
Warner, T.[Timothy],
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
BibRef
Nielsen, M.[Mads],
Hansen, L.K.[Lars Kai],
Johansen, P.[Peter],
Sporring, J.[Jon],
Guest Editorial: Special Issue on Statistics of Shapes and Textures,
JMIV(17), No. 2, September 2002, pp. 87-87.
DOI Link
0211
BibRef
Maillard, P.[Philippe],
Comparing Texture Analysis Methods through Classification,
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.
WWW Link.
0304
BibRef
Xu, B.[Bing],
Gong, P.[Peng],
Seto, E.[Edmund],
Spear, R.[Robert],
Comparison of Gray-Level Reduction and Different Texture Spectrum
Encoding Methods for Land-Use Classification Using a Panchromatic Ikonos
Image,
PhEngRS(69), No. 5, May 2003, pp. 529-536.
There was little difference in classification accuracy among the three modified TS methods,
WWW Link.
0307
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Clausi, D.A.,
An analysis of co-occurrence texture statistics as
a function of grey level quantization,
CanRS(28), No. 1, 2002, pp. 45-62.
HTML Version.
PDF File.
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0200
Clausi, D.A.,
Comparison and fusion of co-occurrence, Gabor, and MRF
texture features for classification of SAR sea ice imagery,
Atmosphere&Oceans(39), No. 4, 2001, pp. 183-194.
HTML Version.
PDF File.
See also K-means Iterative Fisher (KIF) unsupervised clustering algorithm applied to image texture segmentation.
BibRef
0100
Clausi, D.A.,
Deng, H.[Huawu],
Design-Based Texture Feature Fusion Using Gabor Filters and
Co-Occurrence Probabilities,
IP(14), No. 7, July 2005, pp. 925-936.
IEEE DOI
0506
BibRef
Earlier:
Feature fusion for image texture segmentation,
ICPR04(I: 580-583).
IEEE DOI
0409
BibRef
Clausi, D.A.,
Yue, B.[Bing],
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
BibRef
Earlier:
Texture segmentation comparison using grey level co-occurrence
probabilities and markov random fields,
ICPR04(I: 584-587).
IEEE DOI
0409
BibRef
Jobanputra, R.[Rishi],
Clausi, D.A.[David A.],
Preserving boundaries for image texture segmentation using grey level
co-occurring probabilities,
PR(39), No. 2, February 2006, pp. 234-245.
Elsevier DOI
0512
BibRef
Earlier:
Texture analysis using gaussian weighted grey level co-occurrence
probabilities,
CRV04(51-57).
IEEE DOI
0408
BibRef
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
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Petrou, M.,
Image Processing: Dealing with Texture,
Wiley2006,
ISBN: 0-470-02628-6.
WWW Link.
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Mirmehdi, M.[Majid],
Xie, X.H.[Xiang-Hua],
Suri, J.[Jasjit],
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World Scientific2008.
ISBN 978-1-84816-115-3.
Survey, Texture. Collects the basic texture approaches in one book.
Buy this book: Handbook of Texture Analysis
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0800
Kandaswamy, U.,
Schuckers, S.A.,
Adjeroh, D.A.,
Comparison of Texture Analysis Schemes Under Nonideal Conditions,
IP(20), No. 8, August 2011, pp. 2260-2275.
IEEE DOI
1108
See also Efficient Texture Analysis of SAR Imagery.
BibRef
Kandaswamy, U.,
Adjeroh, D.A.,
Schuckers, S.,
Hanbury, A.,
Robust Color Texture Features Under Varying Illumination Conditions,
SMC-B(42), No. 1, February 2012, pp. 58-68.
IEEE DOI
1201
BibRef
Kandaswamy, U.,
Texture Content Based Successive Approximations for Image Compression
and Recognition,
DICTA15(1-7)
IEEE DOI
1603
approximation theory
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Hossain, S.[Shahera],
Serikawa, S.[Seiichi],
Texture databases: A comprehensive survey,
PRL(34), No. 15, 2013, pp. 2007-2022.
Elsevier DOI
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Dataset, Texture.
Survey, Texture Datasets. Texture.
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Elsevier DOI
1407
Texture
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Li, L.[Liang],
Asano, A.[Akira],
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Human visual system issues.
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Pappas, T.N.[Thrasyvoulos N.],
Visual Signal Analysis: Focus on Texture Similarity,
SPIE(Newsroom), August 31, 2015
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1511
A plenary talk from SPIE Optics + Photonics 2015.
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Liu, L.[Li],
Chen, J.[Jie],
Fieguth, P.W.[Paul W.],
Zhao, G.Y.[Guo-Ying],
Chellappa, R.[Rama],
Pietikäinen, M.[Matti],
From BoW to CNN: Two Decades of Texture Representation for Texture
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IJCV(127), No. 1, January 2019, pp. 74-109.
Springer DOI
1901
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Kupidura, P.[Przemyslaw],
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
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Vandenbroucke, N.[Nicolas],
Comparison of color imaging vs. hyperspectral imaging for texture
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Texture representation, Color imaging, Hyperspectral imaging, Feature selection
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Kupidura, P.[Przemyslaw],
Lesisz, K.[Katarzyna],
The Impact of the Type and Spatial Resolution of a Source Image on
the Effectiveness of Texture Analysis,
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Gadermayr, M.[Michael],
Liedlgruber, M.[Michael],
Problems in Distortion Corrected Texture Classification and the Impact
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CIAP13(I:513-522).
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Hegarty, J.,
Carswell, J.D.,
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3DARCH09(xx-yy).
PDF File.
0902
Applied to 3D building descriptions.
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Mikes, S.[Stanislav],
Texture segmentation benchmark,
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IEEE DOI
0812
BibRef
Bai, Y.H.[Yoon Ho],
Park, C.[Choonseog],
Choe, Y.[Yoonsuck],
Relative advantage of touch over vision in the exploration of texture,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Chen, C.M.[Chih-Ming],
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Chen, C.C.[Chaur-Chin],
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IEEE DOI
0609
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Martin, G.,
Pattichis, M.S.,
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feature analysis,
Southwest04(152-156).
IEEE DOI
0411
In medical screening application.
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Singh, M.,
Singh, S.,
Spatial texture analysis: a comparative study,
ICPR02(I: 676-679).
IEEE DOI
0211
BibRef
Castano, R.,
Manduchi, R.,
Fox, J.,
Classification experiments on real-world texture,
EEMCV01(xx-yy).
0110
BibRef
Karkanis, S.A.,
Magoulas, G.,
Iakovidis, D.K.,
Karras, D.,
Maroulis, D.E.,
Evaluation of Textural Feature Extraction Schemes for Neural
Network-based Interpretation of Regions in Medical Images,
ICIP01(I: 281-284).
IEEE DOI
0108
See also Detection of Lesions in Endoscopic Video Using Textural Descriptors on Wavelet Domain Supported by Artificial Neural Network Architectures.
BibRef
Chang, K.I.[Kyong I.],
Bowyer, K.W.[Kevin W.],
Sivagurunath, M.[Munish],
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CVPR99(I: 294-299).
IEEE DOI
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9900
Fleck, M.M.,
Texture: Plus Ca Change,
ECCV92(151-159).
Springer DOI
BibRef
9200
Wang, P.S.P.[Patrick S.P.],
Activities of IAPR: TC-2, Learning, Representation and
Visualization of Intelligent Pattern Recognition,
ICPR96(B9M.1).
9608
(USA)
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Selkainaho, K.,
Parkkinen, J.,
Oja, E.,
Comparison of X2 and K Statistics in Finding Signal and
Picture Periodicity,
ICPR88(II: 1221-1224).
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8811
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Rosenfeld, A.,
Some Recent Developments in Texture Analysis,
PRIP79(618-622).
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7900
Kreyszig, H.E.[Herbert E.],
Descriptors for Textures,
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 .