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Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
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“Peng et al. (2019) [89] Structured Light Laser line grid projector images, IHED deep learning model Box volume measurement Measure volumes ranging from 100 mm to 1800 mm with an accuracy of ±5.0 mm”
From Use and Potential of AI in Assisting Surveyors in Building Retrofit and Demolition—A Scoping Review (2025) · cites this paper as [89]
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(1967) · Cited by 23,719
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1994) · Cited by 15,411
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
(1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895
Markov Chain Monte Carlo in Practice
Gilks, W. R., Richardson, S. T., & Spiegelhalter, D. J. (1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,927
Vector Quantization and Signal Compression
Gersho, A., & Gray, R. M. (1992) · Springer eBooks · doi:10.1007/978-1-4615-3626-0 · Cited by 7,055
Pattern Recognition and Neural Networks
Ripley, B. D. (1996) · Cambridge University Press eBooks · doi:10.1017/cbo9780511812651 · Cited by 6,500
The Laplacian pyramid as a compact image code
Burt, P., & Adelson, E. (1983) · IEEE Transactions on Communications · doi:10.1109/tcom.1983.1095851 · Cited by 6,077
Multirate Systems and Filter Banks
Vaidyanathan, P. P. (1992) · CaltechAUTHORS (California Institute of Technology) · Cited by 5,457
Research works printed but not linked: 2
- Dana, K. and Nayar, S. 1999a. 3D textured surface modelling. In Proceedings Workshop on the Integration of Appearance and Geometric Methods in Object Recognition, pp. 46-56.
- Yuan, J. and Rao, S. 1993. Spectral estimation for random fields with applications to Markov modeling and texture classification. In Markov Random Fields: Theory and Application, R. Chellappa and A. Jain (Eds.). Academic Press.
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Items 201–300 of 1,001
Jurrus, E. R., Paiva, A. R. C., Watanabe, S., Anderson, J. R., Jones, B. W., Whitaker, R., Jørgensen, E. M., Marc, R. E., & Taşdizen, T. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.06.002 · Cited by 96
“This works well if the membranes in the image are uniform and respond well using cross-correlation (Leung and Malik, 2001; Schmid, 2001).”
BackgroundMethodInfluentialView-based recognition of real-world textures
Pietikäinen, M., Nurmela, T., Mäenpää, T., et al. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(03)00231-0 · Cited by 95
“Leung and Malik extended this approach to 3D surfaces by constructing the vocabulary of 3D textons [7] and demonstrating the applicability of the method in the classification of Columbia-Utrecht database (CUReT) textures [8] taken in different views and illuminations.”
MethodAutomated soil prediction using bag-of-features and chaotic spider monkey optimization algorithm
Kumar, S., Sharma, B., Sharma, V. K., & Poonia, R. C. (2018) · Evolutionary Intelligence · doi:10.1007/s12065-018-0186-9 · Cited by 94
Learning Saliency Maps for Object Categorization
Moosmann, F., Larlus, D., Moosmann, F., & Jurie, F. (2006) · Cited by 95
Texture Classification of PolSAR Data Based on Sparse Coding of Wavelet Polarization Textons
He, C., Li, S., Liao, Z.-X., & Liao, M. (2013) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2236338 · Cited by 94
“This concept is similar to that of Leung and Malik [14] because we subsequently cluster the WAPV vectors.”
BackgroundMethodResultPrinciples of Appearance Acquisition and Representation
Rusinkiewicz, S. M., Zickler, T., Weyrich, T., Lawrence, J., Lensch, H. P. A., Rusinkiewicz, S. M., Zickler, T. E., & Fields, R. (2013) · Cited by 93
Wei, L., Han, J., Zhou, K., Bao, H., Guo, B., & Shum, H. (2008) · ACM Transactions on Graphics · doi:10.1145/1360612.1360651 · Cited by 94
Mander, L., Li, M., Mio, W., et al. (2013) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2013.1905 · Cited by 92
“We also compared a baseline texture classification approach using histograms of local quantized image patches [25,26], which yielded an accuracy of 85.”
BackgroundResultToward Robust Material Recognition for Everyday Objects
Hu, D. P., Bo, L., & Ren, X. (2011) · British Machine Vision Conference (BMVC) · doi:10.5244/c.25.48 · Cited by 89
“The work on 3D textons [15] directly addresses material recognition using multiple aligned images of varying viewpoint and lighting conditions.”
BackgroundOpen source computer vision-based layer-wise 3D printing analysis
Petsiuk, A. L., & Pearce, J. M. (2020) · Additive manufacturing · doi:10.1016/j.addma.2020.101473 · Cited by 91
“In both plastic and metal additive manufacturing the lighting effects and mutual reflectance create a non-Lambertian environment, where similar surfaces may look significantly different under varying viewing angles [74,78], which narrows the set of possible image processing techniques.”
BackgroundMethodInfluentialLearned local Gabor patterns for face representation and recognition
Xie, S., Shan, S., Chen, X., Meng, X., & Gao, W. (2009) · Signal Processing · doi:10.1016/j.sigpro.2009.02.016 · Cited by 90
“For the purpose of classification, the textons should not only characterize each class of object to allow for the discrimination between them but also generalize well beyond the training samples [9].”
BackgroundA comparison of color features for visual concept classification
van de Sande, K. E. A., Gevers, T., & Snoek, C. G. M. (2008) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1386352.1386376 · Cited by 91
“It is also referred to as ‘textons’ [14], ‘object parts’ [6], ‘visual words’ [23] and ‘codebooks’ [10, 13].”
Spacetime Texture Representation and Recognition Based on a Spatiotemporal Orientation Analysis
Derpanis, K. G., & Wildes, R. P. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.221 · Cited by 90
RGB-D video-based individual identification of dairy cows using gait and texture analyses
Okura, F., Saya, I., Makihara, Y., Muramatsu, D., NAKADA, K., & Yagi, Y. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.104944 · Cited by 91
Harandi, M. T., Sanderson, C., Wiliem, A., & Lovell, B. C. (2012) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2012.6163005 · Cited by 89
“64% Leung-Malik [17] 97 .”
BackgroundMethodLocal Ensemble Kernel Learning for Object Category Recognition
Lin, Y., Liu, T.-L., & Fuh, C. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383084 · Cited by 89
“Like in [14, 22], the χ(2) distance is selected as the similarity measure.”
MethodZheng, X. S., Chakraborty, I., Lin, J. J.-W., & Rauschenberger, R. (2009) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1518703 · Cited by 93
“Texture was measured in terms of a texton histogram [ 18 ] that was derived by extracting texture features from the image by applying a set of linear orientation and spatial frequency filters, then clustering the resulting features into 20 levels of textons.”
MethodOn the Importance of Visual Context for Data Augmentation in Scene Understanding
Dvornik, N., Mairal, J., & Schmid, C. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2961896 · Cited by 89
“The work of [30] computes texton features [31] for each pixel of an image and defines shape filers on them.”
BackgroundTexture Classification by Modeling Joint Distributions of Local Patterns With Gaussian Mixtures
Lategahn, H., Gross, S., Stehlé, T., & Aach, T. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2042100 · Cited by 88
“An alternative approach [32], [33] uses vector quantization [ 34 ] of the D-jHSTs to reduce their size, which may be regarded as a special instance of signatures [35].”
MethodXiao, C., Deng, R., Li, B., Yu, F., Liu, M., & Song, D. X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01249-6_14 · Cited by 89
“Semantic Segmentation has received long lasting attention in the computer vision community [25].”
BackgroundMcIntosh, C., & Purdie, T. G. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2505188 · Cited by 87
“We use a 3D-extension of the Leung-Malik filter bank [29].”
MethodTowards cross-category knowledge propagation for learning visual concepts
Qi, G.-J., Aggarwal, C. C., Rui, Y., Tian, Q. C., Chang, S., & Huang, T. S. (2011) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2011.5995312 · Cited by 85
A Benchmark Dataset to Study the Representation of Food Images
Farinella, G. M., Allegra, D., & Stanco, F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16199-0_41 · Cited by 85
“Textons have been used in computer vision studies in the context of texture analysis and image classification [13, 21–23].”
MethodIthapu, V. K., Singh, V., Lindner, C., Austin, B. P., Chris Hinrichs, A., Carlsson, C. M., Bendlin, B. B., & Johnson, S. C. (2014) · Human Brain Mapping · doi:10.1002/hbm.22472 · Cited by 83
“In particular, we use texture filters referred to as textons [Leung and Malik, 2001; Malik et al., 1999] which are an ensemble of low, high and band pass spatial filters.”
BackgroundMethodNealen, A., & Alexa, M. (2003) · TUbilio (Technical University of Darmstadt) · Cited by 85
Learning Semantic and Visual Similarity for Endomicroscopy Video Retrieval
André, B., Vercauteren, T. K., Buchner, A. M., Wallace, M. B., & Ayache, N. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2012.2188301 · Cited by 83
“Whereas " HH-Sift " combines the sparse " Harris-Hessian " detector with the SIFT descriptor, the competitive " Textons " method proposed by Leung and Malik [12] is based on a dense description of local texture features.”
Sorted random projections for robust rotation-invariant texture classification
Liu, L., Fieguth, P., Clausi, D. A., et al. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.10.027 · Cited by 82
“…use of random projections (RPs), a universal, information-preserving dimensionality-reduction technique, to project the patch vector space to a compressed patch space without a loss of salient information, claiming that the performance achieved by random features can outperform patch features,…”
BackgroundMethodCorrelative multi-label multi-instance image annotation
Xue, X., Zhang, W., Zhang, J., et al. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126300 · Cited by 82
“On the other hand, all images are segmented into several regions and different kinds of local features are extracted for each region: 1) 14-dimensional color feature including mean RGB, HSV conversion, HUE histogram and SAT histogram; 2) 30-dimensional texture feature including LM-filter mean response [14] and LM-filt…”
MethodFast color/texture segmentation for outdoor robots
Blas, M. R., Agrawal, M., Sundaresan, A., et al. (2008) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2008.4651086 · Cited by 81
“Note that the problem we are interested in here is online, unsupervised segmentation, not classification based on a library.”
BackgroundImage description using joint distribution of filter bank responses
Ahonen, T., & Pietikäinen, M. (2008) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.10.012 · Cited by 80
High-rate flicker-free screen-camera communication with spatially adaptive embedding
Nguyen, V., Tang, Y., Ashok, A., Gruteser, M., Dana, K., Hu, W., Wengrowski, E., & Mandayam, N. B. (2016) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom.2016.7524512 · Cited by 79
McIntosh, C., & Purdie, T. G. (2016) · Physics in Medicine and Biology · doi:10.1088/1361-6560/62/2/415 · Cited by 79
“In order to capture features across a variety of scales we perform convolution with a 3D texture filter bank that is an extension of the Leung–Malik filter bank (Leung and Malik 2001).”
MethodHoang, N. (2019) · Advanced Engineering Informatics · doi:10.1016/j.aei.2019.04.004 · Cited by 79
Garcia‐Pineda, O., MacDonald, I. R., Zimmer, B. G., et al. (2010) · Deep Sea Research Part II Topical Studies in Oceanography · doi:10.1016/j.dsr2.2010.05.005 · Cited by 78
“The TCNNA employs a combination of edges and bar detection filters that are a subset of the Leung Malik Filter Banks (Leung and Malik, 2001).”
MethodSemantic segmentation of images exploiting DCT based features and random forest
Ravì, D., Bober, M. Z., Farinella, G. M., Guarnera, M., & Battiato, S. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.10.021 · Cited by 78
“Gray-level occurrence matrices (GLCMs) [5], Gabor Filter [6], and Local Binary Pattern (LBP) [7] are examples of popular methods to extract texture features.”
BackgroundCula, O. G., Dana, K., Murphy, F., & Rao, B. K. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-4637-2 · Cited by 75
Kernelized Supervised Dictionary Learning
Gangeh, M. J., Ghodsi, A., & Kamel, M. S. (2013) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2013.2274276 · Cited by 75
“In this section, we provide an overview on the dictionary learning and sparse representation and a brief review of recent attempts on making the approach more suitable for classification tasks.”
BackgroundLeveraging archival video for building face datasets
Ramanan, D., Baker, S., & Kakade, S. M. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4409012 · Cited by 75
“and the color histogram distances are computed with the chi-squared distance [7]”
MethodYouTubeCat: Learning to categorize wild web videos
Wang, Z., Zhao, M., Song, Y., et al. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540125 · Cited by 74
“Video content features: color histogram computed using hue and saturation in HSV color space, color motion defined as cosine distance of color histograms between two consecutive frames, skin color features as defined in [9], edge features using edges detected by Canny edge detector in regions of interest, line feature…”
MethodSelf-Supervised Material and Texture Representation Learning for Remote Sensing Tasks
Akiva, P., Purri, M., & Leotta, M. J. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00803 · Cited by 75
“Tex-tons [52,58,66] refer to the description of micro-structures in images often used to describe material and texture consistency [25, 27, 58, 101, 108].”
BackgroundMethodInfluentialTexture segmentation by multiscale aggregation of filter responses and shape elements
Galun, M., Sharon, E., Basri, R., & Brandt, A. (2003) · Cited by 74
Radon-Like features and their application to connectomics
Kumar, R., Vázquez-Reina, A., & Pfister, H. (2010) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2010.5543594 · Cited by 74
“where σ and φ are the scale and the orientation of the boundary enhancing Gaussian-Second-Derivative (GSD) [8] filter ∆G(σ, φ) (⊗ denotes convolution).”
BackgroundA 2-D wavelet decomposition-based bag-of-visual-words model for land-use scene classification
Zhao, L., Tang, P. T. P., & Huo, L. (2014) · International Journal of Remote Sensing · doi:10.1080/01431161.2014.890762 · Cited by 73
Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes
Kivinen, J., Sudderth, E. B., & Jordan, M. I. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408870 · Cited by 73
“” This approach is quite similar to texton approaches to texture recognition [13], except that SIFT descriptors aggregate filter responses over a local region rather than at a single point.”
BackgroundSupervised Saliency Map Driven Segmentation of Lesions in Dermoscopic Images
Jahanifar, M., Tajeddin, N. Z., Asl, B. M., & Gooya, A. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2839647 · Cited by 79
Lei, B., Chen, S., Ni, D., & Wang, T. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2016.00077 · Cited by 72
“K -means (Leung and Malik, 2001) and spectral clustering (Ng et al., 2002) are popular clustering methods.”
MethodComputer vision based detection and localization of potholes in asphalt pavement images
Azhar, K., Murtaza, F., Yousaf, M. H., et al. (2016) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece.2016.7726722 · Cited by 72
“Koch and Brilakis [5] presented a novel approach for automated detection of potholes using asphalt pavement surface images.”
BackgroundParameter Optimization for Automated Concrete Detection in Image Data
Zhu, Z., & Brilakis, I. (2010) · Automation in Construction · doi:10.1016/j.autcon.2010.06.008 · Cited by 71
Kim, D., Oh, S. M., & Rehg, J. M. (2007) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2007.4399610 · Cited by 71
“In addition, texture features of a region are computed using eighteen filters selected from LM filterbank [29] shown in Fig.”
MethodUnderstanding Deep Image Representations by Inverting ThemPreprint
Mahendran, A., & Vedaldi, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.0035 · Cited by 145
“Most image understanding and computer vision methods build on image representations such as textons [17], histogram of oriented gradients (SIFT [20] and HOG [4]), bag of visual words [3][27], sparse [37] and local coding [34], super vector coding [40], VLAD [10], Fisher Vectors [23], and, lately, deep neural networks,…”
BackgroundStatistical binary patterns for rotational invariant texture classification
Nguyễn, T. P., Vu, N., & Antoine Manzanera, B. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.09.029 · Cited by 70
“Our method is naturally related to filter bank approaches [6, 7, 8, 9],”
MethodRetinal vessel segmentation using multi-scale textons derived from keypoints
Zhang, L., Fisher, M. H., & Wang, W. (2015) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2015.07.006 · Cited by 70
Single Image Spectral Reconstruction for Multimedia Applications
Robles‐Kelly, A. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806223 · Cited by 70
“This is important since intensity values are traditionally used in computer vision to obtain textons [17].”
Visibility Graphs for Image Processing
Iacovacci, J., & Lacasa, L. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2891742 · Cited by 70
“We find that in the case of both IVG and IHVG Knn-filters, the IVG k-filter and the Texton filter, D appears systematically below the one for the non-filtered image, suggesting that all low-resolution images will be systematically closer (in feature space) to the high-resolution version after these specific filtering…”
MethodInfluentialMapping, navigation, and learning for off‐road traversal
Konolige, K., Agrawal, M., Blas, M. R., Bolles, R. C., Gerkey, B. P., Solà, J., & Sundaresan, A. (2008) · Journal of Field Robotics · doi:10.1002/rob.20271 · Cited by 70
Novel image markers for non-small cell lung cancer classification and survival prediction
Wang, H., Xing, F., Su, H., Stromberg, A. J., & Yang, L. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-310 · Cited by 72
“center symmetric auto-correlation (CSAC) [45], and texton features [46].”
Recognize High Resolution Faces: From Macrocosm to Microcosm
Lin, D., & Tang, X. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.243 · Cited by 69
“Since the concept texton[24][16][21] becomes popular in computer vision, it has achieved great success.”
BackgroundForan, D. J., Yang, L., Chen, W., Hu, J., Goodell, L. A., Reiss, M., Wang, F., Kurc, T., Pan, T., Sharma, A., et al. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000170 · Cited by 65
“A computational model for textons was later introduced by Leung and Malik, using cluster centers in a feature space which are generated in response to a fixed set of filter banks.(54) Textons have been successfully used to perform texture classification by a host of investigators.”
MethodInterest Point Detector and Feature Descriptor Survey
Krig, S. (2014) · Apress eBooks · doi:10.1007/978-1-4302-5930-5_6 · Cited by 74
Joint Adaptive Median Binary Patterns for texture classification
Hafiane, A., Palaniappan, K., & Seetharaman, G. S. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.02.007 · Cited by 68
“The MBP was independently shown to be one of the best performing non-parametric texture description operators [8].”
BackgroundInteractive Rendering with Bidirectional Texture Functions
Suykens, F., Berge, K. V., Lagae, A., et al. (2003) · Computer Graphics Forum · doi:10.1111/1467-8659.00694 · Cited by 68
Unifying statistical texture classification frameworks
Varma, M., & Zisserman, A. (2004) · Image and Vision Computing · doi:10.1016/j.imavis.2004.03.012 · Cited by 67
“In contrast, frequency comparison classifiers such as [3,8,14] learn distributions of texton frequencies from training images and then classify novel images by comparing their texton distribution to the learnt models.”
BackgroundMethodInfluentialSorted Consecutive Local Binary Pattern for Texture Classification
Ryu, J., Hong, S., & Yang, H. S. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2419081 · Cited by 66
Defect Detection and Classification by Training a Generic Convolutional Neural Network Encoder
Dong, X., Taylor, C., & Cootes, T. F. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.3031188 · Cited by 69
“…using seven traditional feature sets: Haar-like [23], Integral Channel Image Patches (ICIMGP) [47], Image Patches (IMGP) [48], the LM filter bank [49], the RFS filter bank [50], Scale-Invariant FeatureTransform(SIFT)[51]andSpeeded-UpRobustFeatures (SURF)[52],andthedeepfeaturesextractedfromthelast…”
MethodTexture databases – A comprehensive survey
Hossain, S., & Serikawa, S. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.02.009 · Cited by 66
3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set
Popuri, K., Cobzaş, D., Murtha, A. D., & Jägersand, M. (2011) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-011-0649-2 · Cited by 66
Fast Terrain Classification Using Variable-Length Representation for Autonomous Navigation
Angelova, A., Matthies, L. H., Helmick, D. M., et al. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383024 · Cited by 66
“Previous texture [8], [10], [16] and scene [9] recognition approaches apply a fixed, uniform representation for all classes or construct the features without regard to the existence of other classes.”
BackgroundResultRobust 3D Face Recognition Using Learned Visual Codebook
Zhong, C., Sun, Z., & Tan, T. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383279 · Cited by 66
“Based on this hypothesis, we use K - means method to cluster these Gabor filter vectors into a small set of center vectors, as Fig.2(b) shows [13].”
MethodFeature-Guided Deep Radiomics for Glioblastoma Patient Survival Prediction
Shboul, Z. A., Alam, M., Vidyaratne, L., Pei, L., Elbakary, M. I., & Iftekharuddin, K. M. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00966 · Cited by 65
“…computed from raw MRI sequences, and the features are extracted from eight texture representations of the tumor volume [Texton filters (Leung and Malik, 2001); texture-fractal characterization using both our PTPSA (Iftekharuddin et al., 2003) modeling and multi-resolution mBm (Islam et al.,…”
MethodXie, X., & Mirmehdi, M. (2008) · IMPERIAL COLLEGE PRESS eBooks · doi:10.1142/9781848161160_0013 · Cited by 64
“(42) Wigner distribution (− − −) The Wigner distribution also gives a joint representation in the spatial and spatial-frequency domain.”
BackgroundMonitoring of mineral processing systems by using textural image analysis
Kistner, M., Jemwa, G. T., & Aldrich, C. (2013) · Minerals Engineering · doi:10.1016/j.mineng.2013.05.022 · Cited by 65
A similarity measure for material appearance
Lagunas, M., Malpica, S., Serrano, A., Garcés, E., Gutiérrez, D., & Masiá, B. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3323036 · Cited by 69
Continuous rotation invariant local descriptors for texton dictionary-based texture classification
Zhang, J., Zhao, H., & Liang, J. (2012) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2012.10.004 · Cited by 64
The RWST, a comprehensive statistical description of the non-Gaussian structures in the ISM
Allys, E., Levrier, F., Zhang, S., et al. (2019) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201834975 · Cited by 64
Locally Rotation, Contrast, and Scale Invariant Descriptors for Texture Analysis
Mellor, M. P., Hong, B., & Brady, M. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1161 · Cited by 64
“Leung and Malik suggested constructing a texton dictionary by clustering filter responses [3] .”
BackgroundFine-Grained Material Classification Using Micro-geometry and Reflectance
Kampouris, C., Zafeiriou, S. P., Ghosh, A., & Malassiotis, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46454-1_47 · Cited by 65
“We perform a thorough evaluation of various feature encoding methodologies such as Bag of Visual Words (BoVW) [1], Pyramid Histogram Of visual Words (PHOW) [19], Fisher Vectors [20, 21], Vector of Locally-Aggregated Descriptors(VLAD) [22] using both SIFT [23], as well as features from Convolutional Neural Networks (CN…”
MethodInfluentialParsimonious Online Learning with Kernels via sparse projections in function space
Koppel, A., Warnell, G., Stump, E., & Ribeiro, A. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7953042 · Cited by 64
Chen, Y., Tong, X., Wang, J., et al. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015726 · Cited by 64
RGB-D Object Recognition: Features, Algorithms, and a Large Scale Benchmark
Lai, K., Bo, L., Ren, X., & Fox, D. (2012) · Advances in computer vision and pattern recognition · doi:10.1007/978-1-4471-4640-7_9 · Cited by 63
Comparison of Texture Analysis Schemes Under Nonideal Conditions
Kandaswamy, U., Schuckers, S., & Adjeroh, D. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2101612 · Cited by 63
“S. A. Schuckers is with the Biosal Laboratory, Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY 13699 USA (e-mail: sschuck@clarkson.edu).”
MethodImage surface texture analysis and classification using deep learning
Aggarwal, A., & Kumar, M. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09520-2 · Cited by 66
“Leung and Malik [26] suggested an approach to represent and recognize natural textures which are being observed under different lightening and viewing conditions using 3D textons.”
BackgroundNoise robust rotation invariant features for texture classification
Maani, R., Kalra, S., & Yang, Y. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.01.014 · Cited by 62
A generative framework for fast urban labeling using spatial and temporal context
Posner, I., Cummins, M., & Newman, P. M. (2009) · Autonomous Robots · doi:10.1007/s10514-009-9110-6 · Cited by 60
“This approach is widely used in the computer vision community [21,37,38].”
MethodA combined pose, object, and feature model for action understanding
Packer, B., Saenko, K., & Koller, D. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247824 · Cited by 62
“vector quantize the descriptors by assigning each descriptor to a “visual word” in a codebook that is obtained by performing a k-means clustering [17] over all STIP features in all training sequences.”
MethodExtracting Texels in 2.1D Natural Textures
Ahuja, N., & Todorović, S. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408926 · Cited by 62
“Finally, motivated by psychophysical research th at human brain decomposes texture into its frequency and orientation subcomponents, many studies use signal processing techniques for texture modeling [10, 4, 22].”
BackgroundBilinear CNNs for Fine-grained Visual RecognitionPreprint
Lin, T., RoyChowdhury, A., & Maji, S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.07889 · Cited by 62
“showed boosting B-CNNs offers consistent improvements on fine-grained tasks. Texture representations and second-order features. Texture representations have been widely studied for decades. Early work [35] represents the texture by computing the statistics of linear filter-bank responses (e.g., wavelets and steerable py…”
A compression‐based distance measure for texture
Campana, B. J. L., & Keogh, E. (2010) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.10093 · Cited by 61
Re-Identification in the Function Space of Feature Warps
Martinel, N., Das, A., Micheloni, C., et al. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2377748 · Cited by 60
“We also use Gabor [39], Schmid [40] and Leung-Malik (LM) [41] filter banks to extract texture features.”
MethodFace Recognition with Local Gabor Textons
Lei, Z., Li, S. Z., Chu, R., & Zhu, X. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74549-5_6 · Cited by 60
“Leung and Malik [7] reinvent and operationalize the concept of textons.”
BackgroundDeep convolutional network with locality and sparsity constraints for texture classification
Bu, X., Wu, Y., Gao, Z. F., & Jia, Y. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.02.003 · Cited by 61
Decentralized Online Learning With Kernels
Koppel, A., Paternain, S., Richard, C., et al. (2018) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2830299 · Cited by 59
“D=13), and from them generated a set of 256 textons [51].”
BackgroundAhn, S. J., Kwon, H., Yang, J., Park, M., Cha, Y. J., Suh, S. H., & Lee, J. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-65470-7 · Cited by 59
Color–texture segmentation using unsupervised graph cuts
Kim, J. S., & Hong, K.-S. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2008.09.031 · Cited by 58
Separation of Highlight Reflections on Textured Surfaces
Tan, P., Quan, L., & Lin, S. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.273 · Cited by 58
“Numerous models have been proposed for texture representation, such as Markov random fields [17], filter responses [8], and textons [5].”
MethodGeometry-Informed Material Recognition
DeGol, J., Golparvar‐Fard, M., & Hoiem, D. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.172 · Cited by 59
“Leung and Malik [21] introduced the LM-filter bank and proposed “3D Textons”, which, despite the name, are clustered 2D filter responses at each pixel without direct 3D information.”
BackgroundMethodEvaluation of LBP and Deep Texture Descriptors with a New Robustness Benchmark
Liu, L., Fieguth, P., Wang, X., Pietikäinen, M., & Hu, D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_5 · Cited by 58
“The idea of representing texture using the statistics of local features have led to the development of “textons” [3,4], the popular “Bag-ofWords (BoW)” models [5–9] and their variants such as the Fisher Vector [10].”
BackgroundMethodThe modified Beckmann–Kirchhoff scattering theory for rough surface analysis
Ragheb, H., & Hancock, E. R. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2006.10.007 · Cited by 58
“When the roughness is defined in one dimension only, the height variations z of Please cite this article as: H. Ragheb, E.R. Hancock, The modified Beckmann–Kirchhoff scattering theory for rough surface analysis, Pattern Recognition (2006), doi: 10.1016/j.patcog.2006.10.007 ARTICLE IN PRESS H. Ragheb, E.R. Hancock / Pa…”
InfluentialA smart atlas for endomicroscopy using automated video retrieval
André, B., Vercauteren, T. K., Buchner, A. M., Wallace, M. B., & Ayache, N. (2011) · Medical Image Analysis · doi:10.1016/j.media.2011.02.003 · Cited by 54
“In addition to the BoW method presented by Zhang et al. (Zhang et al., 2007) which is referred to as the HH-SIFT method combining sparse feature extraction with the BoW model, we will take as references the following methods for CBIR method comparison: the standard approach of Haralick features (Haralick, 1979) based…”
MethodInfluentialLei, Z., Fang, T., Huo, H., et al. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2166966 · Cited by 56
“Proposed by Leung and Malik in [39], textons refer to a small number of basic patches or units in images in early visual perception [38].”
BackgroundMulti-Fold Gabor, PCA, and ICA Filter Convolution Descriptor for Face Recognition
Low, C.-Y., Teoh, A. B. J., & Ng, C.-J. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2761829 · Cited by 56
Metadata
- Authors
- Leung, T., & Malik, J.
- Affiliations
- University of California, Berkeley (US)
- ORCID
- Malik, Jitendra
- Venue
- International Journal of Computer Vision
- Published
- 2001-06
- Volume
- 43
- Issue
- 1
- Pages
- 29-44
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W1484228140
- Semantic Scholar
- 90d6e7f2202f754d8588f9536e3f5b4a24701f24
- Cited by
- 1,498
- References
- 45
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 18 · 2023: 17 · 2022: 33 · 2021: 30 · 2020: 48 · 2019: 78 · 2018: 74 · 2017: 94 · 2016: 115 · 2015: 116 · 2014: 122 · 2013: 91 · 2012: 81
- FWCI
- 27.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Computer Vision (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Keywords
- material identification, natural textures, surface reflectance, surface normals, appearance modeling, Gaussian derivative filters, texture clustering, surface patches
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