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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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Items 801–900 of 1,001
Abnormal Image Detection Using Texton Method in Wireless Capsule Endoscopy Videos
Nawarathna, R. D., Oh, J., Yuan, X., et al. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13923-9_16 · Cited by 10
NII-ISM, Japan at TRECVID 2007: High Level Feature Extraction
Le, D.-D., Satoh, S. '. I., & Matsui, T. (2007) · Cited by 10
Anomaly Detection for Autonomous Inspection of Space Facilities using Camera Images
Sakai, Y., Tanaka, H., Yairi, T., et al. (2006) · SICE-ICASE International Joint Conference · doi:10.1109/sice.2006.315547 · Cited by 10
“Filter bank based methods work well in this applications [6, 7].”
MethodHuo, Y., Cai, J., Cheng, C., Raju, A., Yan, K., Landman, B. A., Xiao, J. Z., Lü, L., Liao, C., & Harrison, A. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.15691 · Cited by 10
“Traditional designs consist of three major steps: feature extraction [34], [35], [36], dictionary based feature encoding [37], [38], [39], and classification.”
MethodMulti-ring local binary patterns for rotation invariant texture classification
He, Y., & Sang, N. (2011) · Neural Computing and Applications · doi:10.1007/s00521-011-0770-8 · Cited by 9
“Leung and Malik [14] build 3D texton over a stack of images to describing textures with different illuminations and viewpoints.”
On filter banks of texture features for mobile food classification
Martinel, N., Piciarelli, C., Micheloni, C., & Foresti, G. L. (2015) · International Conference on Distributed Smart Cameras · doi:10.1145/2789116.2789132 · Cited by 9
“We have considered four of the most important texture filter banks, namely Gabor, Schmid [7], Leung-Malik [8] and Maximum Response [9].”
MethodMulti-scale neural texture classification using the GPU as a stream processing engine
Martínez‐Zarzuela, M., Pernas, F. J. D., Antón‐Rodríguez, M., et al. (2010) · Machine Vision and Applications · doi:10.1007/s00138-010-0254-3 · Cited by 9
“In the literature extensive studies have been conducted for texture analysis using model-based and structural methods [3,4], statistical methods [2,5–7] and spatialfrequency filtering methods [8].”
MethodSharma, G., & Jurie, F. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.09.007 · Cited by 9
“Leung and Malik [6], and the recent success of bag of ords representation for image retrieval (by Sivic and Zisserman [19]) nd classification (by Csurka et al.”
BackgroundInfluentialQuery Adaptive Similarity Measure for RGB-D Object Recognition
Cheng, Y., Cai, R., Zhang, C., et al. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.25 · Cited by 9
“[20] extracted SIFT [25], texton histograms [22] and spin images [19] over the color and depth frames individually, followed by a concatenation of all these features to depict an object.”
MethodBrain-wide connectivity map of mouse thermosensory cortices
Bokiniec, P., Whitmire, C. J., Leva, T. M., & Poulet, J. F. A. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac386 · Cited by 9
“Images were filtered using the Leung–Malik Filter Bank (Leung and Malik 2001) to detect noncircular cells with a size between 8 and 10 pixels (corresponding to 20–25 μ m in the downsampled image).”
MethodImage segmentation for automated taxiing of Unmanned Aircraft
Eaton, W. C., & Chen, W. (2015) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas.2015.7152268 · Cited by 9
“Textons, originally proposed in [2] and explained in great detail in [3], are visual descriptors specifically designed to describe the appearance of three-dimensional surfaces.”
BackgroundMethodFast weighted K-view-voting algorithm for image texture classification
Liu, H., Lan, Y., Jin, R., et al. (2012) · Optical Engineering · doi:10.1117/1.oe.51.2.027004 · Cited by 9
Glioblastoma Survival Prediction
Shboul, Z. A., Alam, M., Vidyaratne, L., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11726-9_45 · Cited by 9
Texture synthesis quality assessment using perceptual texture similarity
Dong, X., & Zhou, H. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105591 · Cited by 9
Combining CGAN and Mil for Hotspot Segmentation in Bone Scintigraphy
Xu, H., Geng, S., Qiao, Y., et al. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9052946 · Cited by 9
“Location features are determined by which region the patch is in: We utilize Leung-Malik filter bank [17] to obtain the texture features.”
MethodSoftness and weight from shape: Material properties inferred from local shape features
Schmidt, F., Fleming, R. W., & Valsecchi, M. (2020) · Journal of Vision · doi:10.1167/jov.20.6.2 · Cited by 9
“…that material recognition and estimation of material properties is affected by mesoscale texture (e.g., Bhushan et al., 1997; Heaps & Handel, 1999; Leung & Malik, 2001; Wiebel, Valsecchi, & Gegenfurtner, 2013; Wiebel, Valsecchi, & Gegenfurtner, 2014), as well as megascale (global) shape features…”
Background[Retracted] Virtual Reconstruction Method of Regional 3D Image Based on Visual Transmission Effect
Yin, J., & Yang, J. H. (2021) · Complexity · doi:10.1155/2021/5616826 · Cited by 9
“Leung and Malik [14] used the principle of triangulation to apply binocular vision technology to the human body on some occasions.”
RetractedMonitoring of Froth Flotation with Transfer Learning and Principal Component Models
Liu, X., & Aldrich, C. (2021) · Australian & New Zealand Control Conference (ANZCC) · doi:10.1109/anzcc53563.2021.9628313 · Cited by 9
Weakly Supervised Building Semantic Segmentation Based on Spot-Seeds and Refinement Process
Moghalles, K., Li, H.-C., & Alazeb, A. (2022) · Entropy · doi:10.3390/e24050741 · Cited by 9
“The texture histogram ht(rk) and a bank of 38 filters [35], including the Gaussian and Laplacian of Gaussian filters, edges and bar filters with three scales and six orientations, convolve the image.”
BackgroundSteel Surface Defect Classification Via Deep Learning
Tunalι, M. M., Yıldız, A., & Çakar, T. E. (2022) · International Conference on Computer Science and Engineering (UBMK) · doi:10.1109/ubmk55850.2022.9919470 · Cited by 9
“However, the literature on error detection developed with similar processes is quite limited, but it is expected that such studies will become widespread and increase their impact power in the near future [8].”
BackgroundScene Classification via Hypergraph-Based Semantic Attributes Subnetworks Identification
Choi, S., Lee, C. H., & Park, I. K. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10584-0_24 · Cited by 9
“For this, four different types of local image features are used in this experiments as in [25]: SIFT [20], LAB [17], Canny edge [4], and Texton filterbanks [16].”
MethodLocation Constrained Pixel Classifiers for Image Parsing with Regular Spatial Layout
Dang, K., & Yuan, J. (2014) · British Machine Vision Conference (BMVC) · doi:10.5244/c.28.47 · Cited by 9
“To represent each superpixel, multiple types of raw features are used such as RFS filter banks [19], dense SIFT [24] and LBP [26].”
MethodLou, Y., Andrei Irimia, A., Vela, P. A., et al. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2259625 · Cited by 9
“Consider the Chi-squared (CS) statistic [12], for example.”
Automatically Discovering Talented Musicians with Acoustic Analysis of YouTube Videos
Nichols, E., DuHadway, C., Aradhye, H. B., et al. (2012) · Proceedings · doi:10.1109/icdm.2012.83 · Cited by 9
“When identifying candidate videos we also use video features including HOG [17], CONGAS[19] and HueSaturation color histograms [11].”
MethodSegmentation using Appearance of Mesostructure Roughness
Yacoob, Y., & Davis, L. S. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-009-0224-2 · Cited by 9
“Leung and Malik (2001) (also Cula and Dana 2004; Varma and Zisserman 2002) proposed an approach for classifying materials based on 3D texture attributes, 3D textons, computed over small patches to capture local geometric and photometric properties of monochromatic images.”
BackgroundTexel Based Regular and Near-Regular Texture Characterization
Gui, Y., Chen, M., Ma, L., et al. (2011) · International Conference on Multimedia and Signal Processing (CMSP) · doi:10.1109/cmsp.2011.171 · Cited by 9
“Indeed, such a characterization for regular and near-regular textures is significantly useful to improve the performance in shapefrom-texture [6], [7], texture synthesis and editing [8], [9], texture segmentation [10], texture classification [11] and recognition tasks [12], among others.”
BackgroundThe remote-sensing image segmentation using textons in the Normalized Cuts framework
Sun, F. J., & He, J. (2009) · International Conference on Mechatronics and Automation · doi:10.1109/icma.2009.5244991 · Cited by 9
Dendritic tree extraction from noisy maximum intensity projection images in C. elegans
Greenblum, A., Sznitman, R., Fua, P., Arratia, P. E., Oren‐Suissa, M., Podbilewicz, B., & Sznitman, J. (2014) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-13-74 · Cited by 9
“MR8 is directly derived from the Root Filter Set (RFS) [36,38] that computes 38 texture filters using a mixture of anisotropic edge and bar filters, as well as two rotationally symmetric filters, i.e., Gaussian and Laplacian of Gaussian (LoG) filters with different parameters.”
MethodResultInfluentialA Graph Cuts Based Interactive Image Segmentation Method
Liu, J., & Wang, H. (2011) · JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY · doi:10.3724/sp.j.1146.2007.00075 · Cited by 9
Medical Image Retrieval Using Multi-Texton Assignment
Tang, Q., Yang, J., & Xia, X. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-0017-z · Cited by 9
“[24] proposed a computational method of textons based 108 J Digit Imaging (2018) 31:107–116”
MethodTwo-stage human hair segmentation in the wild using deep shape prior
Yan, Y., Duffner, S., Naturel, X., Berthelier, A., García, C., Blanc, C., & Château, T. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.06.014 · Cited by 9
“As a result, many approaches are based on the Bag of Words model (Leung and Malik, 2001) to obtain spatially invariant features for texture representation (Liu et al., 2018).”
Robust Subject-invariant Feature Learning for Ocular Biometrics in Visible Spectrum
Reddy, N., Rattani, A., & Derakhshani, R. R. (2019) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas46853.2019.9185993 · Cited by 9
“Besides using grayscale images, we also evaluated the model performance by training on illumination invariant images using the self-quotient image (SQI) and edge feature map obtained by Laplacian of Gaussian (LoG) filter from LM-Filter bank [14]1.”
MethodSimultaneous surface texture classification and illumination tilt angle prediction
Lladó, X., Oliver, A., Petrou, M., et al. (2003) · British Machine Vision Conference (BMVC) · doi:10.5244/c.17.77 · Cited by 9
“Leung and Malik [5] developed a texture classification scheme that identifies 3D “textons” in the Columbia-Utrecht database for the purpose of illumination and viewpoint invariant classification.”
BackgroundTexton Correlation for Recognition
Leung, T. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24670-1_16 · Cited by 9
“D objects [11]; (2) faces [1,2,3,6,7]; and (3) natural materials [4,10,14].”
BackgroundInfluentialSelf-citationEstimating Lighting Direction and Classifying Textures
Chantler, M. J., McGunnigle, G., Penirschke, A., et al. (2002) · British Machine Vision Conference (BMVC) · doi:10.5244/c.16.72 · Cited by 9
“texture classification scheme that identifies 3D ’textons’ in the Columbia-Utrecht database for the purposes of illumination and viewpoint invariant classification [9, 10].”
BackgroundBiomassID: A biomass type identification system for mobile devices
Ionescu, R. T., Popescu, A. L., Popescu, M., & Popescu, D. (2015) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2015.03.002 · Cited by 9
“One of the early approaches of building a vocabulary of features is that of Leung and Malik (2001).”
BackgroundMethodInfluentialGeldenhuys, C. M., & Niesler, T. (2025) · Bioacoustics · doi:10.1080/09524622.2025.2487099 · Cited by 12
“This pre-trained model can then be fine-tuned on a domain-specific source set, through transfer learning.”
Have a SNAK. Encoding Spatial Information with the Spatial Non-alignment Kernel
Ionescu, R. T., & Popescu, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23231-7_9 · Cited by 8
“The vector quantization process is done by k-means clustering [13], and visual words are stored in a randomized forest of k-d trees [15] to reduce search cost.”
MethodTexture Superpixels Merging by Color-texture Histograms for Color Image Segmentation
Sima, H., & Guo, P. (2014) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2014.07.011 · Cited by 8
“To evaluate the efficiency of the segmentation algorithm in this paper, we employ two test databases: Berkeley segmentation dataset (BSD)[51] and MIT Vistex textures[46].”
BackgroundMethodInfluentialBreast density characterization using texton distributions
Petroudi, S., & Brady, M. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091240 · Cited by 8
Constructing and applying higher order textons: Estimating breast cancer risk
Li, X.-Z., Williams, S., & Bottema, M. J. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.09.033 · Cited by 8
“The motivation and theoretical basis for standard textons (firstorder textons) appears in Leung and Malik [17].”
BackgroundLearning Tree-structured Quantizers for Image Categorization
Krapac, J., Verbeek, J. J., & Jurie, F. (2011) · British Machine Vision Conference (BMVC) · doi:10.5244/c.25.47 · Cited by 8
Pothole Detection Method in Asphalt Pavement
Kim, Y.-R., Kim, T.-H., & Ryu, S.-K. (2014) · Journal of the Institute of Electronics and Information Engineers · doi:10.5573/ieie.2014.51.10.248 · Cited by 8
Zhang, Z., Li, D., Liu, S., et al. (2017) · Remote Sensing · doi:10.3390/rs10010008 · Cited by 8
“We compared the performance of the proposed method TCLBP + DML with two state-of-the-art methods, bag of words (BoW) [45] and multiple random projections (MRP) [46].”
MethodSelvathi, D., & Selvaraj, H. (2018) · International Conference on Systems Engineering · doi:10.1109/icseng.2018.8638244 · Cited by 8
“[8] analysed textures using filter bank of 48 filters for recognizing the materials using three-dimensional textons.”
MethodImage retrieval based on ASIFT features in a Hadoop clustered system
Huang, Y., & Wu, H. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2019.0229 · Cited by 8
Describing Textures Using Natural Language
Wu, C., Timm, M., & Maji, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58452-8_4 · Cited by 9
GLAVNet: Global-Local Audio-Visual Cues for Fine-Grained Material Recognition
Shi, F., Guo, J. J., Zhang, H., Yang, S., Wang, X., & Guo, Y. (2021) · 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/cvpr46437.2021.01420 · Cited by 8
“Early methods usually leverage filter bank responses to extract features from rich textures of materials [49, 28, 50].”
BackgroundDensity-preserving quantization with application to graph downsampling
Alamgir, M., Lugosi, G., & Ulrike Von Luxburg, A. (2014) · Cited by 8
Clustering-Based Descriptors for Fingerprint Indexing and Fast Retrieval
He, S., Zhang, C., & Hao, P. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12307-8_33 · Cited by 8
“This approach is very much like the bag-of-words model for visual categorization [15][ 16 ].”
MethodImage Specific Feature Similarities
Omer, I., & Werman, M. (2006) · Lecture notes in computer science · doi:10.1007/11744047_25 · Cited by 8
“The texture features used are the LeungMalik filter bank [12] (a total of 48 filters).”
MethodChambers, D. R., Gassaway, J., Goodin, C. T., et al. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2194372 · Cited by 8
Robust local ternary patterns for texture categorization
Han, X., Xu, G., & Chen, Y. (2013) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2013.6747059 · Cited by 8
“…spent a lot of effort on image categorization [1] , and then leads to very powerful intermediate representation of images such as color statistics and the bag-of-word (BoW) model [2] [3] [4], which contributes to the acceptable recognition performances for different applications in computer vision.”
BackgroundComputer-aided detection of colitis on computed tomography using a visual codebook
Wei, Z., Zhang, W., Liu, J., et al. (2013) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2013.6556432 · Cited by 8
“The visual codebook has proven its effectiveness in object recognition and texture classification [4-6].”
MethodImprovements in image categorization using codebook ensembles
Luo, H., Wei, H., & Hu, F. (2011) · Image and Vision Computing · doi:10.1016/j.imavis.2011.08.005 · Cited by 8
A unified account of tilt illusions, association fields, and contour detection based on elastica
Keemink, S., & van Rossum, M. C. W. (2015) · Vision Research · doi:10.1016/j.visres.2015.05.021 · Cited by 8
“However, the energy of the smoothest curve between a given center and flanker pair is approximated by Leung and Malik (2001) and Sharon et al. (1997) Esðhc; hf ;uf Þ 4ðb 2 c þ b 2 f bcbf Þ; ð3Þ While Eq.”
MethodLazy texture selection based on active learning
Xia, T., Wu, Q., Chen, C., & Yu, Y. (2009) · The Visual Computer · doi:10.1007/s00371-009-0359-8 · Cited by 8
“There has been extensive work on texture descriptors in the image processing and computer vision literature [17, 24].”
BackgroundResultHigh-level feature extraction using SVM with walk-based graph kernel
Vert, J., Matsui, T., Satoh, S., et al. (2009) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2009.4959785 · Cited by 8
“12–23) are the responses to a small filter bank of orientation and spatialfrequency selective linear filters[3].”
BackgroundTexture classification via morphological scale-space: Tex-Mex features
Southam, P., & Harvey, R. W. (2009) · Journal of Electronic Imaging · doi:10.1117/1.3258441 · Cited by 8
Chen, Q., Wang, G., & Tan, C. L. (2013) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2013.6607545 · Cited by 8
“To extract texture information, each image is convolved with the Leung-Mailk filter bank [22].”
MethodLocal part model for action recognition
Shi, F., Laganière, R., & Petriu, E. M. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2015.11.010 · Cited by 8
Sky detection by effective context inference
Shang, Y., Li, G., Luan, Z., Zhou, X., & Guo, G. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.12.126 · Cited by 8
“In this paper we choose the LM filter bank proposed by Leung and Malik [10].”
MethodIonescu, R. T., Ionescu, A. L., Mothe, J., & Popescu, D. (2016) · Artificial Intelligence Review · doi:10.1007/s10462-016-9532-4 · Cited by 9
“The vector quantization process is done by k-means clustering (Leung and Malik 2001), and visual words are stored in a randomized forest of k–d trees (Philbin et al.”
MethodA CRF that combines touch and vision for haptic mapping
Shenoi, A. A., Bhattacharjee, T., & Kemp, C. C. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759353 · Cited by 8
Material Classification under Natural Illumination Using Reflectance Maps
Georgoulis, S., Vanweddingen, V., Proesmans, M., & Gool, L. V. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.34 · Cited by 8
“Reflectance, usually described as a BRDF [51], has been used by various methods [13, 29, 35, 39, 46, 27] to tackle a variety of problems.”
BackgroundMethodInfluentialPoint Light Source Position Estimation From RGB-D Images by Learning Surface Attributes
Karaoğlu, S., Liu, Y., Gevers, T., et al. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2731619 · Cited by 8
“material property [5]) information.”
Identification of HEp-2 specimen images with mitotic cell patterns
Gupta, K., Bhavsar, A. V., & Sao, A. K. (2020) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2020.07.003 · Cited by 8
A new approach for retinal vessel differentiation using binary particle swarm optimization
Irshad, S., Yin, X., & Zhang, Y. (2021) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2020.1870001 · Cited by 8
“To extract features from filters, filters from Leung-Malik filter bank (Leung and Malik 2001), Schmid filter bank (Schmid 2001), and Maximum Response filter bank (Geusebroek et al.”
BackgroundMethodClassification with invariant scattering representations
Bruna, J., & Mallat, S. (2011) · doi:10.1109/ivmspw.2011.5970362 · Cited by 8
“Error rate for the CUREt database Scatt+PCA Scatt+SVM Textons [7] MRFs [13] 0.”
BackgroundMethodInfluentialGeorgiadis, G., Chiuso, A., & Soatto, S. (2013) · DCC · doi:10.1109/dcc.2013.30 · Cited by 8
“We therefore filter the synthesized and original textures by a filter bank [10]; then, we calculate the histograms of the filter responses and used the χ2 distance to measure the distance between each filter response of the two textures.”
MethodDong, J., & Chantler, M. J. (2004) · IEEE International Conference on Computer and Information Technology (CIT) · doi:10.1109/cit.2004.1357294 · Cited by 8
“The difference due to varying illumination presents challenges in both computer vision and computer graphics [1,2,3,4].”
BackgroundHierarchical Integration of Local 3D Features for Probabilistic Pose Recovery
Detry, R. J., & Piater, J. (2007) · ORBi (University of Liège) · Cited by 8
Leung-Malik Features and Adaboost Perform Classification of Alzheimer’s Disease Stages
Basheera, S., & Ram, M. S. S. (2022) · IETE Journal of Research · doi:10.1080/03772063.2022.2154284 · Cited by 7
“An image’s texton features are extracted using a variety of filter banks, and the benefits of employing the use of a filter bank are successfully made [31].”
Multiple Structure Tracing in 3D Electron Micrographs
Jagadeesh, V., Vu, N., & Manjunath, B. S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23623-5_77 · Cited by 7
“The idea is to learn edge textons [10] to improve structure specific edge detection.”
BackgroundOnline unsupervised cumulative learning for life-long robot operation
Gatsoulis, Y., Burbridge, C., & McGinnity, T. M. (2011) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2011.6181678 · Cited by 7
“There is an extensive body of existing research into object recognition, and one popular approach is using the compact and generic Bag-of-Words representation of visual perceptions (BoW) [8], [11].”
The analysis of image feature robustness using cometcloud
Qi, X., Kim, H., Xing, F., et al. (2012) · Journal of Pathology Informatics · doi:10.4103/2153-3539.101782 · Cited by 7
“The texton is a model based texture method.[21] Previous studies[22,23] had shown that some texture features can capture the underlying variations that exist in normal and cancer tissues.”
BackgroundMethodInfluentialParking space detection from video by augmenting training dataset
Yu, W. B., & Chen, T. (2009) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2009.5414333 · Cited by 7
“More specifically, we follow the texton-based model developed in the context of texture recognition[3].”
MethodInfluentialAutomatic detection of malignant prostatic gland units in cross-sectional microscopic images
Xia, T., Yu, Y., & Hua, J. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5650763 · Cited by 7
“An important class of features we use are based on textons [1, 2], whose spatial distribution serves as a repre-”
MethodInfluentialObject Tracking Podium on Region Covariance for Recognition and Classification
Hossain, A., & Sanyal, G. (2012) · Cited by 7
Enriched dermoscopic-structure-based cad system for melanoma diagnosis
López-Labraca, J., Fernández‐Torres, M., González-Díaz, I., et al. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4879-3 · Cited by 7
“approach is based on the use of textons [23] as information units describing complex textural patterns.”
MethodChoi, J. Y. (2017) · The Visual Computer · doi:10.1007/s00371-017-1429-y · Cited by 7
“The randomized kd-tree matching [25,48] was”
MethodVisual Scene Prediction for Blind People Based on Object Recognition
Jabnoun, H., Benzarti, F., & Amiri, H. (2017) · International Conference on Computer Graphics, Imaging and Visualization (CGIV) · doi:10.1109/cgiv.2017.19 · Cited by 7
“Each feature in the object image is mapped to a certain visual words [13], so that the object is represented through the clustering process the set of patches.”
BackgroundYin, X., Irshad, S., & Zhang, Y. (2019) · Journal of Innovative Optical Health Sciences · doi:10.1142/s1793545819500214 · Cited by 7
Retinal vessel segmentation using Gabor Filter and Textons
Zhang, L., Fisher, M. H., & Wang, W. (2014) · UEA Digital Repository (University of East Anglia) · Cited by 7
Neural Photometry-Guided Visual Attribute Transfer
Rodriguez-Pardo, C., & Garcés, E. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3133081 · Cited by 7
“Ourmethod is agnostic to the capture setup [58], [59], [60], and it may work for any kind of input data (e.g., BTFs[34], [35]) as long as the photometric images are pixel-wise aligned with each other and to the visual attribute.”
BackgroundMethodInfluentialContent-Based Retrieval in Endomicroscopy: Toward an Efficient Smart Atlas for Clinical Diagnosis
André, B., Vercauteren, T. K., & Ayache, N. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28460-1_2 · Cited by 7
“…on the different pCLE databases, the classification performances of our “Dense-Sift” method with respect to several state-of-the-art CBIR methods, namely the statistics-based “Haralick” method [8], the dense “Textons” method [9], the sparse “HH-Sift” method [2], and the “NBNN” classifier [10].”
A framework of perceptual features for the characterisation of 3D textured images
Paulhac, L., Makris, P., Ramel, J.-Y., et al. (2013) · Signal Image and Video Processing · doi:10.1007/s11760-013-0438-1 · Cited by 7
“…related to frequency or statistical analysis of the image, whereas the other four features are computed using information regarding the repetitive patterns that potentially define the texture (these patterns are most often called “textons” [53] and are usually analysed using geometrical methods).”
BackgroundEstimating Floor Regions in Cluttered Indoor Scenes from First Person Camera View
Aggarwal, S., Namboodiri, A., & Jawahar, C. V. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.733 · Cited by 7
“• Texture Cues (96-dim) : mean absolute response and histogram of maximum responses using 48 Leung-Malik(LM) filter bank [15].”
MethodDong, J., Sun, G., & Chen, G. (2008) · Image and Vision Computing · doi:10.1016/j.imavis.2008.02.002 · Cited by 7
Probabilistic salient object contour detection based on superpixels
Jiang, H., Wu, Y., & Yuan, Z. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738632 · Cited by 7
“We apply the filter bank, including 15 filters, proposed in [8] to describe the texture of a superpixel.”
MethodView-based recognition of 3D-textured surfaces
Pietikäinen, M., Nurmela, T., Mäenpää, T., & Turtinen, M. (2004) · International Conference on Image Analysis and Processing · doi:10.1109/iciap.2003.1234104 · Cited by 7
“Leung and Malik extended this approach to 3D surfaces by constructing the vocabulary of 3D textons [ 9 ] and demonstrating the applicability of the method in the classification of Columbia-Utrecht database (CUReT) textures [6] taken in different views and illuminations.”
MethodPredicate logic based image grammars for complex pattern recognition
Shet, V. D., Singh, M., Bahlmann, C., & Ramesh, V. (2009) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2009.5204328 · Cited by 11
A Hybrid Scheme for Online Detection and Classification of Textural Fabric Defects
Behravan, M., Boostani, R., Tajeripour, F., et al. (2009) · International Conference on Machine Vision · doi:10.1109/icmv.2009.53 · Cited by 7
“It was reported that these structures of textures can be represented closely by its responses to an appropriate filter bank [11].”
BackgroundMethodInfluentialSyntactic image parsing using ontology and semantic descriptions
Nwogu, I., Govindaraju, V., & Brown, C. (2010) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2010.5543723 · Cited by 7
“Transformation Semantic feature L*a*b* color perceptually uniform color YCbCr color better captures skin tones [20] HSV color bright man-made structures [16] LM fiters 4 oriented edge responses [12] 4π ∗ Area÷ Perimeter(2) global shape measure”
BackgroundRobust Texture Recognition Using Credal Classifiers
Giorgio Corani, A., Giusti, A., Migliore, D., et al. (2010) · British Machine Vision Conference (BMVC) · doi:10.5244/c.24.78 · Cited by 7
“In particular, several different descriptors have been proposed, including Haralick features [6], Gabor filters [12] and histograms of texture elements (textons) [7, 8, 14].”
MethodCutting and Pasting Irregularly Shaped Patches for Texture Synthesis
Dong, F., Lin, H. B., & Clapworthy, G. (2005) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2005.00825.x · Cited by 7
Diversity in Ensembles of Codebooks for Visual Concept Detection
Piras, L., Tronci, R., & Giacinto, G. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41184-7_41 · Cited by 7
“These kind of methods have been proposed in the literature under different names such as ‘textons’ [13], ‘object parts’ [9] and ‘codebooks’ [10], but the main idea is more or less the same: the use of vector quantization techniques on local descriptors extracted in some particular areas of an image.”
A Stochastic Statistical Approach for Tracking Human Activity
Hossain, M. A., & Sanyal, G. (2013) · International Journal of Information Technology Modeling and Computing · doi:10.5121/ijitmc.2013.1304 · Cited by 7
“Besides the tracking based on histogram-application is found in texture portrayal [8, 9] and pronouncement their association [10] in the domain of image-processing.”
MethodHan, K. K., Muthukumar, B., & Golparvar‐Fard, M. (2016) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784479827.089 · Cited by 7
Metric learning based automatic segmentation of patterned species
Shukla, A., & Anand, S. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7533107 · Cited by 7
“The texture features are extracted using a filter bank [19] of 48 filters at different scales and orientations.”
Method
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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