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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 901–1,000 of 1,001
Using material classification methods for steel surface defect inspection
Pan, S., Hung, T.-Y., & Chia, L.-T. (2016) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie.2016.7744862 · Cited by 7
Landmark-Based Alzheimer’s Disease Diagnosis Using Longitudinal Structural MR Images
Zhang, J., Liu, M., An, L., Gao, Y., & Shen, D. G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-61188-4_4 · Cited by 7
Joyseeree, R., Müller, H., & Depeursinge, A. (2018) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2018.01.005 · Cited by 7
Computational Texture and Patterns
Dana, K. (2018) · Synthesis lectures on computer vision · doi:10.2200/s00819ed1v01y201712cov014 · Cited by 12
Evaluating Deep Convolutional Neural Networks as Texture Feature Extractors
Scabini, L. F. D. S., Condori, R. H. M., Ribas, L. C., & Bruno, O. M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30645-8_18 · Cited by 7
Wang, Y., Shi, H., Zhuang, Y., et al. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3023645 · Cited by 7
“Later on, various specifically designed textural patterns or filters (Gaussian, Laplacian-of-Gaussian, etc) are developed to capture particular object patterns [20], [21].”
BackgroundAn Automatic Random Walker Algorithm for Segmentation of Ground Glass Opacity Pulmonary Nodules
Li, X., Li, B., Yin, H., et al. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/6727957 · Cited by 7
“Gabor transformation [41] is another commonly used texture feature extraction method.”
MethodA Short Survey on Optical Material Recognition
Weinmann, M., & Klein, R. (2015) · Eurographics · doi:10.2312/mam.20151202 · Cited by 7
Pothole Detection Based on the Features of Intensity and Motion
Jo, Y., Ryu, S.-K., & Kim, Y.-R. (2016) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2595-03 · Cited by 7
Color texture recognition by color information fusion using the non-extensive entropy
Susan, S., & Hanmandlu, M. (2017) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-017-0502-z · Cited by 7
“Multidim Syst Sign Process (2018) 29:1269–1284 1271 (Leung and Malik 2001) in its classification performance (98.8457% on 64,800 samples of twenty five monochrome Brodatz textures), was proposed by the authors in Susan and Hanmandlu (2013b) for high accuracy texture classification. The utility of this feature set for…”
BackgroundMethodInfluentialHall, B. H., Chen, W., Reiß, M., et al. (2007) · Springer eBooks · doi:10.1007/978-3-540-75759-7_35 · Cited by 7
“The DAB staining map is characterized using textons, a texture feature composed of a vocabulary of consensus filter responses derived through cluster analysis [9, 10].”
MethodEstimating Distributions of Surface Parameters for Classification Purposes
Zhang, M., Levina, E., Djurdjanović, D., et al. (2008) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.2844588 · Cited by 7
Semantic Image Labelling as a Label Puzzle Game
Kontschieder, P., Bulò, S. R., Donoser, M., Pelillo, M., & Bischof, H. (2011) · British Machine Vision Conference (BMVC) · doi:10.5244/c.25.111 · Cited by 7
“For instance, [31] uses an adapted version of joint boosting [35] to train unary classifiers integrating textons [23] and shape filters.”
MethodDistributional-based texture classification using non-parametric statistics
Pothos, V. K., Theoharatos, C., Zygouris, E., & Economou, G. (2007) · Pattern Analysis and Applications · doi:10.1007/s10044-007-0083-9 · Cited by 7
Long, L. R., Antani, S., Jerónimo, J., et al. (2006) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2006.154 · Cited by 7
“A second approach is to apply texton [ 11 ] filtering to model texture in the cervicography.”
MethodResolution-Enhanced Photometric Stereo
Tan, P., Lin, S., & Quan, L. (2006) · Lecture notes in computer science · doi:10.1007/11744078_5 · Cited by 7
“Consistency is evaluated in terms of surface integrability and simplicity of surface description with respect to 3D textons, which is motivated by the minimum description length principle [9] and the observation that a surface is generally composed of only a small number of perceptually distinct local structures [10].”
BackgroundTracing the boundaries of materials in transparent vessels using computer visionPreprint
Eppel, S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1501.04691 · Cited by 7
de Croon, G. (2008) · Research Publications (Maastricht University) · doi:10.26481/dis.20080626gc · Cited by 7
Striver: an image descriptor for fingerprint liveness detection
Li, J., Wang, Y., & Zhang, E. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03466-w · Cited by 6
“We apply the Leung-Malik filter bank [70] to analysis spatial feature.”
Amirkhani, D., Allili, M. S., & Lapointe, J. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3591407 · Cited by 6
“Before sending the FPN features to the classification and regression sub-heads, they are first refined by integrating the Leung-Malik (LM) filter bank [54], known for capturing linear local patterns.”
Multi-level Local Binary Pattern Analysis for Texture Characterization
Suguna, R., & Anandhakumar, P. (2011) · Communications in computer and information science · doi:10.1007/978-3-642-22555-0_39 · Cited by 6
“Another frequently used approach in texture description is using distributions of quantized filter responses to characterize the texture (Leung and Malik), (Varma and Zisserman) [ 11 ][12].”
MethodDirectional Gaussian Filter-based LBP Descriptor For Textural Image Classification
Sun, X., Wang, J., Chen, R., et al. (2011) · Procedia Engineering · doi:10.1016/j.proeng.2011.08.330 · Cited by 6
“In recent ten years, statistical methods have gone through flourishing developments, and the representative approaches include filter bank-based methods [1-3] that use the response vector of filter banks as texture feature, patch-based algorithms [4, 5] that utilize raw pixel values in image patches for classification…”
MethodBoosting video classification using cross-video signals
Sargin, M. E., & Aradhye, H. B. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5946854 · Cited by 6
“• Texture: Texton histogram is computed using the method of [10].”
MethodPeripheral pooling is tuned to the localization task
Eberhardt, S., Zetzsche, C., & Schill, K. (2016) · Journal of Vision · doi:10.1167/16.2.14 · Cited by 6
Vision Based Horizon Detection for UAV Navigation
Timotheatos, S., Piperakis, S., Argyros, A., et al. (2018) · Mechanisms and machine science · doi:10.1007/978-3-030-00232-9_19 · Cited by 6
Learning Multi-scale Representations for Material Classification
Li, W. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11752-2_65 · Cited by 6
“For multi-scale approaches we consider: Texton [18], Multiscale LBP (MLBP) [23].”
BackgroundMethodPlatero, C. A., & Tobar, M. C. (2015) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2015.04.005 · Cited by 7
Vehicle detection under varying poses using Conditional Random Fields
Zhang, X., & Zheng, N. (2010) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2010.5624980 · Cited by 6
“Some vehicles are running on the road, and others are parked at the street.”
BackgroundPurposive Hidden-Object-Game: Embedding Human Computation in Popular Game
Feng, J., Ni, Y., Dong, J., et al. (2012) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2012.2198801 · Cited by 6
“To represent the texture features, we employ the filter bank proposed in [14].”
MethodAutomatic target image detection for morphing
Vyas, J. P., Joshi, M. V., & Raval, M. S. (2015) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2014.12.005 · Cited by 6
Categorization of Multiple Objects in a Scene Using a Biased Sampling Strategy
Yang, L., Zheng, N., Chen, M., Yang, Y., & Yang, J. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0629-9 · Cited by 6
“The current patch samplers can be classified into three groups: dense (Leung and Malik 2001), random (Nowak et al. 2006), and sparse (based on various saliency measures) (Itti et al. 1998; Lowe 2004; Mikolajczyk and Schmid 2004; Zhang et al. 2007; Parikh et al. 2008; Moosmann et al. 2008; Achanta…”
Combining Multi-Scale Dissimilarities for Image Classification
Li, Y., Duin, R. P. W., & Loog, M. (2016) · Cited by 6
Bunting, P., He, W., Reyer Zwiggelaar, A., et al. (2009) · Lecture notes in geoinformation and cartography · doi:10.1007/978-3-540-93962-7_2 · Cited by 6
“For this study the Leung-Malik filter bank (Leung and Malik, 2001), consisting of 48 filters, including 8 Laplacian of Gaussian, 4 Gaussian smoothing filters and 6 Gaussian first and second order derivative filters at 3 scales, was used.”
BackgroundMethodPopulation density estimation using textons
Javed, Y., Khan, M. M., & Chanussot, J. (2012) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2012.6351062 · Cited by 6
Textons for 3D Binary Data with Applications to Classifying Cancellous Bone
Martin, B. L., & Bottema, M. J. (2015) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2015.7371312 · Cited by 6
“3D textons refer to textons obtained by clustering a concatenation of local representation vectors obtained from a collection of images of the same scene under different lighting conditions [15].”
BackgroundMethodEVALUATION OF TEXTURE ENERGIES FOR CLASSIFICATION OF FACADE IMAGES
Drauschke, M., & Mayer, H. (2010) · Cited by 6
Texture Estimation for Abnormal Tissue Segmentation in Brain MRI
Reza, S. M. S., Islam, A., & odu.edu, K. M. K. (2016) · Springer series in computational neuroscience · doi:10.1007/978-1-4939-3995-4_21 · Cited by 6
Monocular Visual-IMU Odometry: A Comparative Evaluation of the Detector-Descriptor Based Methods
Dong, X., Dong, X., & Dong, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46604-0_6 · Cited by 6
“…of LIOP [34] is better than that it obtained in Section 5.1 but is still worse than those of the other descriptors in most cases; (5) LM [18] and LSSD [32] perform well when combined with the Blob&Corner detector [11]; (6) IMGP [33] performs properly when used with the FAST [27] or DoG…”
BackgroundMethodInfluentialUnseen object categorization using multiple visual cues
Ramesh, B., & Xiang, C. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.12.003 · Cited by 6
LBP-and-ScatNet-based combined features for efficient texture classification
Nguyen, V., Vu, N., Phan, H., et al. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4824-5 · Cited by 6
“…(LBP) method [1] and its variants, 2) the rep-11 resentation based on co-occurrence matrix [2],(3) the filter-based approaches such as works in [3,4], 4) the wavelet transform method as works in [5–7], 5) the texton dictionary-based [8–10], 6) the use of bidirectional features [11, 12], and so on.”
MethodSimple texture descriptors for classifying monochrome planetary rover terrains
Shukla, D. K., & Skonieczny, K. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989647 · Cited by 6
A Handcrafted Normalized-Convolution Network for Texture Classification
Vu, N., Nguyen, V., & Gosselin, P.-H. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.149 · Cited by 6
“They can be: 1) local binary pattern (LBP) method [32] and its variants; 2) the representation based on co-occurrence matrix [16]; 3) the filter-based approaches such as works in [2, 30]; 4) the wavelet transform method as works in [28, 20, 4]; 5) the texton dictionary-based [22, 43, 44]; 6) the use of bidirectional f…”
MethodComputer-Aided Theragnosis Based on Tumor Volumetric Information in Breast Cancer
Gangeh, M. J., Liu, S., Tadayyon, H., et al. (2018) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2018.2839714 · Cited by 6
“Malik [25].”
BackgroundA deep neural network and rule-based technique for fire risk identification in video frames
Zhang, L., & Verma, B. (2018) · Pattern Analysis and Applications · doi:10.1007/s10044-018-0756-6 · Cited by 7
“In previous studies, different types of filters have been proposed for generating representative features for objects, including Leung and Malik (LM) with 48 filters [28], Schmid (S) set with 13 filters [29], MR8 set with 38 filters [30], Gabor filter set [31], tree-structured wavelets [32], sub-band filters [33], etc.”
MethodMicro-parenchymal patterns for breast cancer risk assessment
Pertuz, S. D., Sassi, A., Karivaara-Mäkelä, M., Holli-Helenius, K., Lääperi, A.-L., Rinta-Kiikka, I. C., Arponen, O., & Kämäräinen, J. (2019) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ab42f4 · Cited by 6
Scene Classification Based on Local Binary Pattern and Improved Bag of Visual Words
Montazer, G. A., Giveki, D., & Soltanshahi, M. A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19258-1_21 · Cited by 6
Vessel Delineation in Retinal Images using Leung-Malik filters and Two Levels Hierarchical Learning.
Varnousfaderani, E. S., Yousefi, S., Bowd, C., et al. (2015) · PubMed Central · Cited by 6
3D-Difference Theoretic Texture Features for dynamic face recognition
Susan, S., & Chakre, R. (2016) · International Conference on Computational Techniques in Information and Communication Technologies · doi:10.1109/icctict.2016.7514583 · Cited by 6
“Texture cues [8-9, 12-15] are particularly suited to the face recognition task since they represent patterns associated with the structures in a human face.”
BackgroundImage-based monument classification using bag-of-word architecture
Ghildiyal, B., Singh, A., & Bhadauria, H. S. (2017) · International Conference on Advances in Computing, Communication & Automation (ICACCA) · doi:10.1109/icaccaf.2017.8344728 · Cited by 6
“In this method, the centers of the clusters are the codewords[10].”
MethodApplying Scattering Operators for Face Recognition: A Comparative Study
Chang, K.-Y., Lin, C.-F., Chen, C., et al. (2014) · Cited by 6
An Embedded Low Power High Efficient Object Tracker for Surveillance Systems
Diaz, I., Heijligers, M. J. M., Kleihorst, R. P., et al. (2007) · ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC) · doi:10.1109/icdsc.2007.4357546 · Cited by 6
Kandaswamy, U., Rotman, Z., Watt, D., Schillebeeckx, I., Cavalli, V., & Klyachko, V. A. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.12.011 · Cited by 6
“…such as changes in viewing direction, deformations, and changes in ambient illumination is often solved by obtaining invariant descriptors of the image, such as, for instance, illumination-or rotation-invariant features (Kandaswamy et al., 2011; Leung and Malik, 2001; Varma and Zisserman, 2005).”
BackgroundMethodAvni, U., Goldberger, J., & Greenspan, H. K. (2008) · CLEF (Working Notes) · Cited by 6
Learning object-specific DAGs for multi-label material recognition
Xie, X., Yang, L., & Zheng, W. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.11.018 · Cited by 6
Object Recognition with the Bag of Visual Words Model
Ionescu, R. T., & Popescu, M. (2016) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-30367-3_5 · Cited by 6
A unified framework for exploiting color coefficients for salient object detection
Naqvi, S. S., Mirza, J., & Bashir, T. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.091 · Cited by 6
On the use of marginal statistics of subband images
Gluckman (2003) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238381 · Cited by 6
“Recently, good results on texture classification have been obtained using “textons”, which are clusters in the joint statistical space of filter outputs [16] [17] [18].”
BackgroundClassifying Surface Texture while Simultaneously Estimating Illumination Direction
Chantler, M. J., Petrou, M., Penirsche, A., et al. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000046590.98379.19 · Cited by 6
“Leung and Malik (1999, 2001), Cula and Dana (2001), and Varma and Zisserman (2002a, 2002b) all developed classification schemes using filter banks and 3D ‘textons’ for the purposes of illumination and viewpoint invariant classification.”
MethodUnsupervised Texture Classification: Automatically Discover and Classify Texture Patterns
Qin, L., Wang, W., Huang, Q., et al. (2006) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2006.1146 · Cited by 6
Maximum Response Filters for Texture Analysis
Caenen, G., & Gool, L. V. (2005) · IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvpr.2004.393 · Cited by 6
“It is the explicit goal of texture analysis to extract (part of) these dependencies and the neighborhood systems from example images.”
BackgroundTexture recognition under varying imaging geometries
Lladó, X. (2004) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 6
Saha, S., & Gokhale, T. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00070 · Cited by 9
“Shift invariance can be improved through dense sampling [28] with dilated convolu-tions [48] with susceptibility to griding artifacts.”
Generating vocabulary for global feature representation towards commerce image retrieval
Chen, Z., Duan, L., Wang, C., et al. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6115607 · Cited by 5
“Leung at al.[7] tried dense sampling in images, applying a bank of Gabor-like filters to each patch, followed by vector quantization to generate codebook to recognize textures.”
MethodTexture detection in noisy images by combining several local parameters
Науменко, А. В., Krivenko, S., Ponomarenko, N. N., et al. (2015) · International Scientific-Practical Conference Problems of Infocommunications Science and Technology · doi:10.1109/infocommst.2015.7357321 · Cited by 5
“Textural features are widely used in image segmentation [3-5], material classification [6, 7], identification of surface types in remote sensing (RS) images [8], etc.”
BackgroundSupertexton-based segmentation in early Drosophila oogenesis
Nava, R., & Kybic, J. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351284 · Cited by 5
“In order to build a supertexton dictionary, we used a reduce set of the Leung-Malik [12] filter bank by keeping only the maximum responses of first and second Derivatives of Gaussian across six orientations {0◦, 30◦, 60◦, 90◦, 120◦, 150◦} and at three dyadic scales { √ 2, 2, 2 √ 2}.”
MethodSingle-Trial Decoding from Local Field Potential Using Bag of Word Representation
Parto-Dezfouli, M., & Daliri, M. R. (2019) · Brain Topography · doi:10.1007/s10548-019-00726-8 · Cited by 5
Automatic Generation of Multidestination Routes for Autonomous Wheelchairs
Mori, Y., & Nagao, K. (2020) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2020.p1121 · Cited by 5
Multi-View Learning for Material Classification
Sumon, B. U., Muselet, D., Xu, S., & Trémeau, A. (2022) · Journal of Imaging · doi:10.3390/jimaging8070186 · Cited by 6
“Then, local texture features aggregation methods, such as the bags-of-textons [18], have been introduced, which are designed to compute global texture features.”
MethodSemantic segmentation of textured mosaics
Cote, M., Dash, A., & Albu, A. B. (2023) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-023-00613-0 · Cited by 7
“Learning-based descriptors are built on the concept of dictionaries of visual patterns, pioneered by Leung and Malik [22]: a repeated pattern or texton is described by features clustered in the same visual word in the feature space.”
BackgroundAutomatic Detection of Defects on Periodically Patterned Textures
Asha, V., Bhajantri, N. U., & Nagabhushan, P. N. (2011) · Journal of Intelligent Systems · doi:10.1515/jisys.2011.015 · Cited by 5
“The first method is based on chisquare measure which is considered to be a measure of dissimilarity between two histograms [7] and has been widely used in various applications such as texture and object categories classification, shape matching, shape classification and boundary detection.”
MethodSun, X., Wang, J., She, M. F. H., et al. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.07.001 · Cited by 5
“Some representative feature extraction methods include the Local Binary Patterns (LBP) [10], texton-based approaches [11,12,7,13], filter bank based methods [14–17] and bag-of-keypoints [18,8].”
MethodGreiner, T., Rao, S. G., & Das, S. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.04.027 · Cited by 5
Wang, H., Zhou, H., & Finn, A. M. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907788 · Cited by 5
“Successful applications have been reported that use K-means to construct visual dictionaries for image-based object recognition [10], video-based activity recognition [8], [16], etc.”
MethodGraph Cuts Segmentation by Using Local Texture Features of Multiresolution Analysis
FUKUDA, K., Takiguchi, T., & Ariki, Y. (2009) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e92.d.1453 · Cited by 5
“There have been many studies on texture analysis [7], [8].”
BackgroundMethodInfluentialLarge Data Clustering Using Quadratic Programming: A Comprehensive Quantitative Analysis
Chakeri, A., & Hall, L. (2015) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2015.151 · Cited by 5
“In [29], the definition of textons as cluster centers of high-dimensional points of filter responses has been provided.”
BackgroundMethodYamazaki, K. (2015) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2015.7418919 · Cited by 6
“This approach is similar to a filter bank [8] [25] [31], the most frequent application of which is texture classification [14].”
BackgroundMethodClassification of Flowers based on Whorl Region
Guru, D. S., Kumar, Y. H. S., & Manjunath, S. (2011) · doi:10.13140/2.1.3528.3204 · Cited by 5
Detection and segmentation of underwater archaeological sites surveyed with stereo-vision platforms
Skinner, K. A., & Johnson‐Roberson, M. (2015) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.23919/oceans.2015.7401959 · Cited by 5
“Next we use a supervised texture classification method to classify each superpixel as either a rock or non-rock segment using the texture classification method presented by Varma and Zisserman [18], which is based on the rotationally invariant Maximum Response-8 (MR8) filter bank [19] and textons, which are defined by…”
MethodTexture Classification based on First Order Local Ternary Direction Patterns
Rao, M. S., Kumar, V. V., & Prasad, M. K. (2017) · International Journal of Image Graphics and Signal Processing · doi:10.5815/ijigsp.2017.02.06 · Cited by 5
“The other approaches used vector quantization [38, 39, 40] to reduce the dimensionality.”
MethodPollen recognition in optical microscopy by matching multifocal image sequences
Filipovych, R., Daood, A. I., Ribeiro, E., et al. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7899950 · Cited by 5
“where 2 is the chi-square histogram distance [11].”
BackgroundMethodImplicit representation of inscribed volumes
Sahbaei, P., Mould, D., & Wyvill, B. (2018) · doi:10.1145/3229147.3229164 · Cited by 5
“D texton method [8].”
SiameseDenseU-Net-based Semantic Segmentation of Urban Remote Sensing Images
Dong, R., Bai, L., & Li, F. (2020) · Mathematical Problems in Engineering · doi:10.1155/2020/1515630 · Cited by 5
“,e original pixel intensity, a simple combination of raw values, and various types of statistical information describing the local image texture [25, 26] are typical choices for input features.”
BackgroundModeling Extent-of-Texture Information for Ground Terrain Recognition
Ghose, S., Chowdhury, P. N., Roy, P. P., & Pal, U. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412703 · Cited by 5
“[8] T. Leung and J. Malik, “Representing and recognizing the visual appearance of materials using three-dimensional textons,” IJCV, 2001.”
BackgroundXiang, Z., Wu, H., & Zhou, D. (2021) · IET Image Processing · doi:10.1049/ipr2.12391 · Cited by 6
Image texture classification using textons
Javed, Y., & Khan, M. M. (2011) · International Conference on Emerging Technologies · doi:10.1109/icet.2011.6048474 · Cited by 5
“Recently, textons have been proposed for image texture representation [9][ 3 ].”
BackgroundUnsupervised video object segmentation by spatiotemporal graphical model
Guo, L., Cheng, T., Huang, Y., et al. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-3100-9 · Cited by 5
Texton-Based Fabric Defect Detection and Recognition
Behravan, M., Tajeripour, F., Azimifar, Z., & Boostani, R. (2011) · Cited by 5
A novel LBP method for invariant texture classification
Ahmadvand, A., Ahmadvand, R., Hajiali, M. T., et al. (2015) · International Conference on Knowledge-Based Engineering and Innovation · doi:10.1109/kbei.2015.7436037 · Cited by 5
Perumal, T. S. R., & Dhanasekaran, R. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7428-4 · Cited by 5
RetractedArtery/vein classification of retinal vessels using classifiers fusion
Yin, X.-X., Irshad, S., & Zhang, Y. (2019) · Health Information Science and Systems · doi:10.1007/s13755-019-0090-4 · Cited by 5
Effectiveness of GAN-based Synthetic Samples Generation of Minority Patterns in HEp-2 Cell Images
Gupta, K., Thapar, D., Bhavsar, A. V., et al. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175636 · Cited by 5
Acquisition and representation of material appearance for editing and rendering
Rusinkiewicz, S. M., & Lawrence, J. (2006) · Cited by 5
Rotation-invariant texture classification using circular Gabor wavelets
Yin, Q., Kim, J., & Shen, L. (2009) · Optical Engineering · doi:10.1117/1.3070640 · Cited by 5
Robots Móviles con Orugas Historia, Modelado, Localización y Control
González, R., Rodríguez, F., & Guzmán, J. L. (2014) · Revista Iberoamericana de Automática e Informática Industrial RIAI · doi:10.1016/j.riai.2014.11.001 · Cited by 5
“En este caso, se est´a trabajando en el uso de descriptores del terreno basados en textones ( textons ) (Leung and Malik, 2001) y en algoritmos de aprendizaje (e.g. Support Vector Machine ).”
BackgroundAutomatic system recognition of Lebanese license plates
Akoum, A. H., Daya, B., & Chauvet, P. E. (2010) · International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) · doi:10.1109/bicta.2010.5645286 · Cited by 5
“The options for the local coding of the images are to Rowle [5, 6] who developed an approach, used in the recognition of the objects, called Scale Invariant Feature (SIFT) which consists of a new family of local descriptors of an object and the filters [ 7 ] or the labels of intensities [8].”
MethodIndoor Place Categorization Using Co-occurrences of LBPs in Gray and Depth Images from RGB-D Sensors
Jung, H., Mozos, Ó. M., Iwashita, Y., & Kurazume, R. (2014) · International Conference on Emerging Security Technologies · doi:10.1109/est.2014.23 · Cited by 5
The visual appearance and surface texture of materials according to the old masters
Parraman, C. E. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2041216 · Cited by 5
“[16] Leonardo created paintings without lines or boundaries, or sfumato, which softened the overall appearance of a scene.”
BackgroundMethodSurface texture recognition by surface rendering
Llado, X., Petrou, M., & Marti, J. (2005) · Optical Engineering · doi:10.1117/1.1869994 · Cited by 5
Generic video coding with abstraction and detail completion
Yuan, Z., Xiong, H., Song, L., et al. (2009) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2009.4959730 · Cited by 5
“The human visual perception from Gestalt psychology suggests that there is a strong bias toward forming global percept by grouping local elements [8].”
BackgroundDimension amnesic pyramid match kernel
Liu, Y., Wang, X.-L., & Zha, H. (2008) · National Conference on Artificial Intelligence · Cited by 5
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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