Ahuja, N., and
Schacter, B.,
Image Models,
Surveys(13), No. 4, December 1981, pp. 373-397.
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8112
And: Comments:
Surveys(15), No. 1, March 1983, pp. 83-84.
Survey, Texture. Mostly about texture representation.
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Ahuja, N., and
Schacter, B.,
Pattern Models,
WileyNew York, 1983.
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Schacter, B.,
Real Time Display of Textures,
ICPR80(789-791).
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Wechsler, H.,
Texture Analysis: A Survey,
SP(2), 1980, 271-282.
Survey, Texture.
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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.
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8000
Van Gool, L.J.,
Dewaele, P., and
Oosterlinck, A.,
Texture Analysis Anno 1983,
CVGIP(29), No. 3, March 1985, pp. 336-357.
Survey, Texture.
Texture, Survey. A recent review of texture analysis methods.
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Haralick, R.M.,
Statistical Image Texture Analysis,
HPRIP86(247-279).
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Tuceryan, M., and
Jain, A.K.,
Texture Analysis,
HPRCV92(II-1), 1993, pp. 235-276.
Texture review.
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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
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BookCreates a measure to distinguish a
large number of textures.
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Tomita, F., and
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 Version.
Survey, Texture.
BibRef
9008
Haralick, R.M.,
Statistical and Structural Approaches to Texture,
PIEEE(67), No. 5, May 1979, pp. 786-804.
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7905
Earlier:
ICPR78(45-69).
Survey, Texture.
Texture, Survey. A good review of texture.
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Julesz, B.,
Foundations of Cyclopean Perception,
The
University of Chicago Press1971.
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Julesz, B.,
Visual Pattern Discrimination,
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Julesz, B.,
Experiments in the Visual Perception of texture,
SciAmer(232), No. 4, April 1975, pp. 34-43.
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Caelli, T.M., and
Julesz, B.,
On Perceptual Analyzers Underlying Visual
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BioCyber(28), 1978, pp. 167-176.
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Caelli, T.M.,
Julesz, B., and
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., and
Caelli, T.M.,
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Julesz, B.,
Gilbert, E.N., and
Victor, J.D.,
Visual Discrimination of Textures with
Identical Third-Order Statistics,
BioCyber(31), No. 3, 1979, pp. 137-140.
BibRef
7900
Julesz, B., and
Bergen, R.,
Textons, The Fundamental Elements in Preattentive Vision
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Bell System Tech.(62), No. 6, 1983, Part II, pp. 1619-1645.
Reprinted in
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RCV87(243-256).
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Julesz, B., and
Bergen, R.,
Textons, The Elements of Texture Perception, and Their Interactions,
Nature(290), 1981, pp. 91-97.
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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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Julesz, B.,
A Theory of Preattentive Texture Discrimination Based on First-Order
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BioCyber(41), 1981, pp. 131-181.
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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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BookThe standard reference for where to find natural textures.
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Longuet-Higgins, M.S.,
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Longuet-Higgins, M.S.,
Statistical Properties of an Isotropic Random Surface,
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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).
WWW Version.
9610
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Waksman, A.[Adlai],
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Sparse, Opaque Three-Dimensional Texture. I. Arborescent Patterns,
CVGIP(57), No. 3, May 1993, pp. 388-399.
WWW Version.
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Waksman, A.,
Rosenfeld, A.,
Sparse, Opaque 3-Dimensional Texture, 2B: Photometry,
PR(29), No. 2, February 1996, pp. 297-313.
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And: Correction:
WWW Version.
PR(29), No. 6, June 1996, pp. R1-R2.
9606
BibRef
UMDTR3363, 1994.
WWW Version.
WWW Version.
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Waksman, A.,
Rosenfeld, A.,
Sparse, Opaque 3-Dimensional Texture, 2A: Visibility,
GMIP(58), No. 2, March 1996, pp. 155-163.
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And:
UMDTR3333, 1994.
WWW Version.
WWW Version. And
WWW Version.
WWW Version.
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Waksman, A.,
Rosenfeld, A.,
Assessing the Condition of a Plant,
MVA(10), No. 1, 1997, pp. 35-41.
HTML Version.
9705
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MIT Texture Data,
1995.
Dataset, Texture.
HTML Version.
Ohanian, P.P.,
Dubes, R.C.,
Performance Evaluation for Four Classes of Textural Features,
PR(25), No. 8, August 1992, pp. 819-833.
WWW Version.
BibRef
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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.,
Measuring Texture Classification Algorithms,
PRL(18), No. 14, December 1997, pp. 1495-1501.
9806MEASTEX.
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], and
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 Abstract. IEEE Top Reference.
WWW Version.
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.
WWW Version.
0010
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Sipilä, O.[Outi],
Visa, A.,
Salonen, O.,
Erkinjuntti, T.,
Katila, T.,
Experiences on data quality in automatic tissue classification,
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HTML Version.
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.
WWW Version.
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 Version.
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.
WWW Version.
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 Version.
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 Version.
0307
BibRef
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 Version.
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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 Version.
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.
WWW Version.
0506
BibRef
Earlier:
Feature fusion for image texture segmentation,
ICPR04(I: 580-583).
WWW Version.
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. IEEE Top Reference.
0402
BibRef
Earlier:
Texture segmentation comparison using grey level co-occurrence
probabilities and markov random fields,
ICPR04(I: 584-587).
WWW Version.
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.
WWW Version.
0512
BibRef
Earlier:
Texture analysis using gaussian weighted grey level co-occurrence
probabilities,
CRV04(51-57).
IEEE Abstract. IEEE Top Reference.
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.
WWW Version.
0411
BibRef
Toyoda, T.[Takahiro],
Hasegawa, O.[Osamu],
Extension of higher order local autocorrelation features,
PR(40), No. 5, May 2007, pp. 1466-1473.
WWW Version.
0702Higher order local autocorrelation features; Texture classification;
Face recognition; Outex database; AT&T database
BibRef
Kawewong, A.,
Hasegawa, O.[Osamu],
3D Texture Classification by Using Pre-Testing Stage and Reliability
Table,
ICIP05(II: 1330-1333).
WWW Version.
0512Combine classification for texture.
BibRef
Petrou, M.,
Image Processing: Dealing with Texture,
Wiley2006,
ISBN: 0-470-02628-6.
WWW Version.
BibRef
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Outex: New framework for empirical evaluation of
texture analysis algorithms,
2006.
Dataset, Texture.
WWW Version.
Texture Data,
2006.
Dataset, Texture.
WWW Version.
CUReT: Columbia-Utrecht Reflectance and Texture Database,
2006.
Dataset, Texture.
WWW Version.
Martin, G.,
Pattichis, M.S.,
The characterization of scanning noise and quantization on texture
feature analysis,
Southwest04(152-156).
IEEE Abstract. IEEE Top Reference.
0411In medical screening application.
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Singh, M.,
Singh, S.,
Spatial texture analysis: a comparative study,
ICPR02(I: 676-679).
WWW Version.
0211
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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 Abstract. IEEE Top Reference.
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],
Evaluation of Texture Segmentation Algorithms,
CVPR99(I: 294-299).
IEEE Abstract. IEEE Top Reference.
WWW Version.
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Fleck, M.M.,
Texture: Plus Ca Change,
ECCV92(151-159).
WWW Version.
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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.,
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Picture Periodicity,
ICPR88(II: 1221-1224).
WWW Version.
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.
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9007
Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Texture Models, Analysis Techniques .