7.9.1 Texture Models, Analysis Techniques

Geusebroek, J.M.[Jan-Mark], Smeulders, A.W.M.[Arnold W.M.],
A Six-Stimulus Theory for Stochastic Texture,
IJCV(62), No. 1-2, April-May 2005, pp. 7-16.
DOI Link 0411
BibRef
Earlier:
Fragmentation in the vision of scenes,
ICCV03(130-135).
IEEE DOI 0311
The spatial scene statistics to conform to a Weibull-distribution. The parameters characterize the spatial structure of uniform textures of many different origins completely. The parameters are sensitive to illumination conditions, camera magnification and resolving power, and the texture orientation. BibRef

Geusebroek, J.M., Smeulders, A.W.M.,
A Physical Explanation for Natural Image Statistics,
Texture02(47-52). 0207
BibRef

Geusebroek, J.M.[Jan-Mark],
The Stochastic Structure of Images,
ScaleSpace05(327-338).
Springer DOI 0505
BibRef

Ben-Yosef, G.[Guy], Ben-Shahar, O.[Ohad],
Curvature-based perceptual singularities and texture saliency with early vision mechanisms,
JOSA-A(25), No. 8, August 2008, pp. 1974-1993.
WWW Link. 0804
BibRef

Sang, D.[Dinh],
Algorithms for selecting parameters of combination of acyclic adjacency graphs in the problem of texture image processing,
ELCVIA(13), No. 2, 2014, pp. xx-yy.
DOI Link 1407
Ph.D.. Thesis. BibRef

Rami, H.[Hassan], Belmerhnia, L.[Leila], El Maliani, A.D.[Ahmed Drissi], El Hassouni, M.[Mohammed],
Texture retrieval using mixtures of generalized Gaussian distribution and Cauchy-Schwarz divergence in wavelet domain,
SP:IC(42), No. 1, 2016, pp. 45-58.
Elsevier DOI 1603
Wavelet decomposition BibRef

Rami, H.[Hassan], El Maliani, A.D.[Ahmed Drissi], El Hassouni, M.[Mohammed], Aboutajdine, D.[Driss],
Texture Retrieval Using Cauchy-Schwarz Divergence and Generalized Gaussian Mixtures,
ISVC14(II: 107-116).
Springer DOI 1501
BibRef

Terzic, K.[Kasim], Krishna, S.[Sai], du Buf, J.M.H.,
Texture features for object salience,
IVC(67), No. 1, 2017, pp. 43-51.
Elsevier DOI 1710
BibRef
Earlier:
A Parametric Spectral Model for Texture-Based Salience,
GCPR15(331-342).
Springer DOI 1511
Texture BibRef

Chu, R.J.[Rui Jian], Richard, N.[Noël], Chatoux, H.[Hermine], Fernandez-Maloigne, C.[Christine], Hardeberg, J.Y.[Jon Yngve],
Hyperspectral Texture Metrology Based on Joint Probability of Spectral and Spatial Distribution,
IP(30), 2021, pp. 4341-4356.
IEEE DOI 2104
BibRef
Earlier: A1, A2, A4, A5, Only:
A Metrological Measurement of Texture in Hyperspectral Images Using Relocated Spectral Difference Occurrence Matrix,
ICIP19(3133-3137)
IEEE DOI 1910
Feature extraction, Metrology, Probability, Hyperspectral imaging, Graphical models, Distribution functions, Quantization (signal), metrology. hyperspectral, texture, non-uniformity, Kullback-Leibler BibRef

Milano, F.[Fiona], Chevrier, A.[Anik], de Crescenzo, G.[Gregory], Lavertu, M.[Marc],
Robust Segmentation-Free Algorithm for Homogeneity Quantification in Images,
IP(30), 2021, pp. 5533-5544.
IEEE DOI 2106
Image segmentation, Histograms, Nanocomposites, Gray-scale, Image color analysis, Indexes, Flowcharts, Chitosan, dispersion, statistical analysis BibRef

Akoushideh, A.[Alireza], Maybodi, B.M.N.[Babak Mazloom-Nezhad], Shahbahrami, A.[Asadollah],
Features' value range approach to enhance the throughput of texture classification,
IET-IPR(15), No. 1, 2021, pp. 28-46.
DOI Link 2106
Large dataset texture categories. BibRef

Ataky, S.T.M.[Steve Tsham Mpinda], Lameiras Koerich, A.[Alessandro],
A novel bio-inspired texture descriptor based on biodiversity and taxonomic measures,
PR(123), 2022, pp. 108382.
Elsevier DOI 2112
Texture characterization and classification, Species abundance BibRef

de Matos, J.[Jonathan], Soares-de Oliveira, L.E.[Luiz Eduardo], de Souza-Britto-Junior, A.[Alceu], Koerich, A.L.[Alessandro Lameiras],
Large-margin representation learning for texture classification,
PRL(170), 2023, pp. 39-47.
Elsevier DOI 2306
Fully convolutional networks, Large-margin classifier, Feature extraction, Texture BibRef

Zhang, Q.[Qin], Liu, J.[Jing], Sun, H.[Huali], Liu, J.[Jun],
A General Machine Learning Framework for Solving Real Applications with a Texture Perception Case Study,
ICIVC21(393-397)
IEEE DOI 2112
Deep learning, Clustering methods, Computational modeling, Manuals, Speech recognition, texture perception BibRef

Wu, C.Y.[Chen-Yun], Timm, M.[Mikayla], Maji, S.[Subhransu],
Describing Textures Using Natural Language,
ECCV20(I:52-70).
Springer DOI 2011
BibRef

Ma, D., Chen, Z., Liao, Q.,
Tree-Shaped Sampling Based Hybrid Multi-Scale Feature Extraction for Texture Classification,
ICIP18(2087-2091)
IEEE DOI 1809
Feature extraction, Histograms, Standards, Robustness, Encoding, Training, Convolutional neural networks, Feature extraction, tree-shaped sampling BibRef

Ignat, A.[Anca],
Combining Features for Texture Analysis,
CAIP15(II:220-229).
Springer DOI 1511
BibRef

Martinez, J.[Jorge], Pistonesi, S.[Silvina], Flesia, A.G.[Ana Georgina],
Inference Strategies for Texture Parameters,
CIARP15(460-467).
Springer DOI 1511
BibRef

Ribeiro, T.P.[Thiago P.], de Oliveira, A.L.N., Barcelos, C.A.Z.[Celia A. Zorzo],
Texture Characterization via Projections onto a Klein Bottle Topology,
ICIP18(2097-2101)
IEEE DOI 1809
Probability density function, Hip, Topology, Feature extraction, Visualization, Cutoff frequency, Fibonacci BibRef

Couto, L.N.[Leandro N.], Ribeiro, T.P.[Thiago P.], Backes, A.R.[André R.], Barcelos, C.A.Z.[Celia A. Zorzo],
Texture characterization via improved deterministic walks on image-generated complex network,
ICIP15(4416-4420)
IEEE DOI 1512
BibRef
Earlier: A2, A1, A3, A4:
Texture Characterization via Automatic Threshold Selection on Image-Generated Complex Network,
CIARP15(468-476).
Springer DOI 1511
Texture characterization; complex networks; deterministic walks BibRef

Couto, L.N.[Leandro N.], Backes, A.R.[Andre R.], Barcelos, C.A.Z.[Celia A.Z.],
Texture characterization via deterministic walks' direction histogram applied to a complex network-based image transformation,
PRL(97), No. 1, 2017, pp. 77-83.
Elsevier DOI 1709
Texture characterization BibRef

Xiao, S.S.[Song-Shan], Tian, Y.[Yi],
An application of Visual Computational Theory in spatial frequency domain: The simulation of dynamic radiated fringes,
IASP11(616-619).
IEEE DOI 1112
BibRef

Elkharraz, G.[Galal], Thumfart, S.[Stefan], Akay, D.[Diyar], Eitzinger, C.[Christian], Henson, B.[Brian],
Texture features corresponding to human touch feeling,
ICIP09(1341-1344).
IEEE DOI 0911
BibRef

Sun, S.[Shuli], Liu, N.[Na],
New Approach to Optimal Filtering for ARMA Signals,
CISP09(1-4).
IEEE DOI 0910
auto-regressing moving average BibRef

Unger, B.[Bertram],
Psychophysics of Virtual Texture Perception,
CMU-RI-TR-08-45, August, 2008. BibRef 0808 Ph.D.Thesis, August, 2008.
WWW Link. BibRef

Chen, J.[Jie], Shan, S.G.[Shi-Guang], Zhao, G.Y.[Guo-Ying], Chen, X.L.[Xi-Lin], Gao, W.[Wen], Pietikainen, M.[Matti],
A robust descriptor based on Weber's Law,
CVPR08(1-7).
IEEE DOI 0806
Differential and direction. BibRef

Dewangan, D., Samar, V.J., Rao, R.[Raghuveer], Paul, P.,
Factors Influencing Psycophysically Valid Taxonomies of Image Texture,
ICIP05(III: 1196-1199).
IEEE DOI 0512
BibRef

Ojala, T., Maenpaa, T., Pietikainen, M., Viertola, J., Kyllonen, J., Huovinen, S.,
Outex: New framework for empirical evaluation of texture analysis algorithms,
ICPR02(I: 701-706).
IEEE DOI 0211
BibRef

Ruiz-del-Solar, J., Jochmann, M.,
On Determining Human Description of Textures,
SCIA01(P-W4A). 0206
BibRef

Huang, Y., Chan, K., Huang, Z.,
An Adaptive Model for Texture Analysis,
ICIP00(Vol I: 276-279).
IEEE DOI 0008
BibRef

Petrou, M., Arrigo, M., Vons, H.,
On the Use the ID Boolean Model for the Description of Binary Textures,
BMVC96(Poster Session 1). 9608
University of Surrey BibRef

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

Last update:Mar 17, 2025 at 20:02:03