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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 401–500 of 1,001
A Two Level Approach for Scene Recognition
Lü, L., Toyama, K., & Hager, G. D. (2005) · 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.2005.51 · Cited by 38
“[16, 5, 27].”
Jemwa, G. T., & Aldrich, C. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.104 · Cited by 41
Toward A Discriminative Codebook: Codeword Selection across Multi-resolution
Wang, L. (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.2007.383374 · Cited by 37
“Codeword selection The simplest way may be to remove the clusters with very few descriptors [9].”
BackgroundA Feature Descriptor Based on Local Normalized Difference for Real-World Texture Classification
Zhang, W., Zhang, W., Liu, K., & Gu, J. J. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2760102 · Cited by 37
“use their responses to represent texture patterns [13], [14].”
BackgroundSky Segmentation Approach to obstacle avoidance
de Croon, G., Wagter, C. D., Remes, B. D. W., et al. (2011) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2011.5747529 · Cited by 37
“This computationally rather expensive method extracts many different features from the image, including LeungMalik filter bank responses [33] and information on the vanishing points of the projection.”
MethodTexton theory revisited: A bag-of-words approach to combine textons
Álvarez, S., & Vanrell, M. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.04.032 · Cited by 36
“The computational representation we propose perfectly matches current bag-of-words models coming from the object recognition field [21] and also used in texture representation [15,1,17,16].”
BackgroundMethodPrabhu, D. S., Bezerra, H. G., Kolluru, C., et al. (2019) · Journal of Biomedical Optics · doi:10.1117/1.jbo.24.10.106002 · Cited by 36
Classifying Surface Texture while Simultaneously Estimating Illumination Direction
Chantler, M. J., Petrou, M., Penirsche, A., et al. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-4636-3 · Cited by 36
“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.”
MethodObjectness to improve the bag of visual words model
Ionescu, R. T., & Popescu, M. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025655 · Cited by 35
“The vector quantization process is done by k-means clustering [17], and visual words are stored in a randomized forest of k-d trees [6] to reduce search cost.”
MethodSteerable Wavelet Machines (SWM): Learning Moving Frames for Texture Classification
Depeursinge, A., Püspöki, Z., Ward, J. P., & Unser, M. A. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2655438 · Cited by 36
“X Z | h ° 2 Ω ¢ | = 1 3) n h d Ω n ØØØØ Ω = 0 = 0 for n = 0,..., N (vanishing moments).”
BackgroundKrig, S. (2014) · Apress eBooks · doi:10.1007/978-1-4302-5930-5_2 · Cited by 39
Interest Point Detector and Feature Descriptor Survey
Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_6 · Cited by 37
A psychophysically validated metric for bidirectional texture data reduction
Filip, J., Chantler, M. J., Green, P. R., et al. (2008) · ACM Transactions on Graphics · doi:10.1145/1409060.1409091 · Cited by 35
Gabor Filter Analysis for Texture Segmentation
Sandler, R., & Lindenbaum, M. (2006) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2006.86 · Cited by 35
“4 Texture recognition test For this test we implemented the texture recognition algorithm proposed by Varma and Zisserman [28] and partially based on earlier work by Leung and Malik [17].”
MethodHigh Throughput Analysis of Breast Cancer Specimens on the Grid
Yang, L., Chen, W., Meer, P., Salaru, G., Feldman, M. D., & Foran, D. J. (2007) · Springer eBooks · doi:10.1007/978-3-540-75757-3_75 · Cited by 33
“We used the 49×49 LM filter bank [8] composed of 48 filters: eight LOG filter responses with σ = 1, √ 2, 2, 2 √ 2, 3, 3 √ 2, 6, 6 √ 2,”
Statistical Estimation of Histogram Variation for Texture Classification
Broadhurst, R. E. (2005) · Cited by 35
Computational grading of hepatocellular carcinoma using multifractal feature description
Atupelage, C., Nagahashi, H., Yamaguchi, M., et al. (2012) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2012.10.001 · Cited by 34
Zhang, L., Ye, X., Lambrou, T., Duan, W., Allinson, N., & Dudley, N. J. (2016) · Physics in Medicine & Biology · doi:10.1088/0031-9155/61/3/1095 · Cited by 34
The Effect of Illuminant Rotation on Texture Filters: Lissajous’s Ellipses
Chantler, M. J., Schmidt, M., Petrou, M., et al. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47977-5_19 · Cited by 34
“Leung and Malik developed a texture classification scheme that identifies 3D ’textons’ in the Columbia-Utrecht database for the purposes of illumination and viewpoint invariant classification [10, 11 ].”
BackgroundLiu, W., Yu, Z., Lu, L., Wen, Y., Li, H., & Zou, Y. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.04.014 · Cited by 33
“The χ 2 distance is proposed in [40] for texture similarity measure.”
BackgroundBidirectional Texture Function Compression Based on Multi‐Level Vector Quantization
Havran, V., Filip, J., & Myszkowski, K. (2010) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2009.01585.x · Cited by 33
Song, J., Xiao, L., Molaei, M., & Lian, Z. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.03.031 · Cited by 31
A perceptually validated model for surface depth hallucination
Glencross, M., Ward, G. J., Melendez, F., et al. (2008) · ACM Transactions on Graphics · doi:10.1145/1360612.1360658 · Cited by 33
Convolutional Fisher Kernels for RGB-D Object Recognition
Cheng, Y., Cai, R., Zhao, X., & Huang, K. (2015) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2015.23 · Cited by 33
Automated Monitoring of Operation-level Construction Progress Using 4D BIM and Daily Site Photologs
Han, K. K., & Golparvar‐Fard, M. (2014) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784413517.106 · Cited by 33
“The work utilizes Leung and Malik (2001) filter bank which contains 48 filters– 36 oriented filters at 6 orientations, 3 scales, and 2 filters, in addition to 8 derivative filters and 4 low-pass Gaussian filters.”
Detecting Specular Surfaces on Natural Images
DelPozo, A., & Savarese, S. (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.2007.383215 · Cited by 33
“Texture recognition techniques [17, 7] based on models build out of histograms of textons have been shown to be a powerful discriminative tool.”
BackgroundLow-level fusion of color, texture and depth for robust road scene understanding
Scharwächter, T., & Franke, U. (2015) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2015.7225750 · Cited by 32
“We encode texture by applying a set of eight filter kernels, a subset from the filter bank presented in [19].”
MethodGao, S., Cheng, Y., & Song, C. (2013) · Infrared Physics & Technology · doi:10.1016/j.infrared.2013.05.010 · Cited by 32
Texton analysis for mass classification in mammograms
Li, Y., Chen, H., Rohde, G. K., et al. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.10.008 · Cited by 32
Finger vein verification based on a personalized best patches map
Dong, L., Yang, G., Yin, Y., et al. (2014) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2014.6996234 · Cited by 32
“…has good resistance to occlusions, geometric deformations, rotation and illumination variations [15], which has been successfully applied in texture analysis and visual classification [15] [16] [17] [18] [19], the BoW method is an intuitively good candidate for finger vein feature extraction.”
MethodEndomicroscopic image retrieval and classification using invariant visual features
André, B., Vercauteren, T. K., Aymeric Perchant, B., Buchner, A. M., Wallace, M. B., & Ayache, N. (2009) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2009.5193055 · Cited by 32
“For performance comparison, the following methods are taken as references: the sparse scale invariant SIFT method [4], the statistical approach of Haralick features [6, 7] and the texture retrieval method of Textons [8].”
MethodEstimation of visible spectrum faces from polarimetric thermal faces
Riggan, B. S., Short, N. J., Hu, S., & Kwon, H. (2016) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2016.7791170 · Cited by 32
“In fact, there has been an interest in understanding image representations for a long time [14,15].”
BackgroundSemantic Prior Analysis for Salient Object Detection
Nguyen, T., Nguyen, K.-D., & Do, T.-T. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2894284 · Cited by 32
“, the normalized area/perimeter/aspect ratio of the superpixel bounding box, the variances/means of color histograms (RGB, Lab, HSV), Textons [50], Local Binary Patterns [52], etc.”
BackgroundA novel approach for modeling positive vectors with inverted Dirichlet-based hidden Markov models
Nasfi, R., Amayri, M., & Bouguila, N. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105335 · Cited by 32
Associative memory optimized method on deep neural networks for image classification
Yang, G., & Ding, F. (2020) · Information Sciences · doi:10.1016/j.ins.2020.05.038 · Cited by 34
Vegetation Extraction from UAV-based Aerial Images through Deep Learning
Behera, T., Bakshi, S., & Sa, P. K. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107094 · Cited by 30
A new algorithm for image recognition and classification based on improved Bag of Features algorithm
Li, K., Wang, F., & Zhang, L. (2015) · Optik · doi:10.1016/j.ijleo.2015.08.219 · Cited by 31
A probabilistic representation for efficient large scale visual recognition tasks
Bhattacharya, S., Sukthankar, R., Jin, R., & Shah, M. A. (2011) · doi:10.1109/cvpr.2011.5995746 · Cited by 31
“Earlier approaches [4, 11, 22] have demonstrated the utility of constructing representations based on local features in images [16] and video [5, 9], analogous to words in text documents, enabling researchers to apply algorithms from text retrieval and classification to computer vision.”
BackgroundLearning completed discriminative local features for texture classification
Zhang, Z., Liu, S., Mei, X., Xiao, B., & Zheng, L. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.02.021 · Cited by 31
Supervoxel based method for multi-atlas segmentation of brain MR images
Huo, J., Wu, Q. M. J., Cao, J., & Wang, G. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.04.001 · Cited by 31
Efficiently Combining Contour and Texture Cues for Object Recognition
Shotton, J., Blake, A., & Cipolla, R. (2008) · British Machine Vision Conference (BMVC) · doi:10.5244/c.22.7 · Cited by 31
“Texture-layout filters (TLFs) are based on localized patterns of textons [18, 30], a compact discrete image representation.”
BackgroundGarcia‐Pineda, O., MacDonald, I. R., & Zimmer, B. G. (2008) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2008.4779960 · Cited by 31
“The LMFB is a multi-scale, multi-orientation filter bank with 36 edge and line detector filters [1].”
MethodInfluentialExtracting Semantic Information from Visual Data: A Survey
Liu, Q., Li, R., Hu, H., & Gu, D. (2016) · Robotics · doi:10.3390/robotics5010008 · Cited by 31
“Subsequently, the vectors are clustered by vector quantisation algorithms, e.g., K-means clustering [84].”
MethodA biased sampling strategy for object categorization
Yang, L., Zheng, N., Yang, J., Chen, M., & Chen, H. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459349 · Cited by 31
“Dense sampling [ 11 ] processes every pixel at every scale and captures most information, but it is memory and computation intensive.”
BackgroundMethodNonnegative Matrix Factorization with Earth Mover's Distance metric
Sandler, R., & Lindenbaum, M. (2009) · 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.2009.5206834 · Cited by 31
“Note the high local variability of the textures. tion methods (e.g., [ 14 ]) effective.”
BackgroundSupervised Dictionary Learning and Sparse Representation-A ReviewPreprint
Gangeh, M. J., Farahat, A., Ghodsi, A., & Kamel, M. S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.05928 · Cited by 31
“Texton-based approach The texton-based approach [20, 23, 50–53], is one of the earliest methods that was proposed to compute the histogram of dictionary elements, called textons, to model a texture image based on patches extracted.”
MethodGenerative Modeling of Audible Shapes for Object Perception
Zhang, Z., Wu, J., Li, Q., Huang, Z., Traer, J. A., McDermott, J. H., Tenenbaum, J. B., & Freeman, W. T. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.141 · Cited by 31
“An important topic in understanding physical object properties is material recognition, which has been another longstanding research problem in computer vision [24, 25, 3].”
BackgroundThe University of Amsterdam’s Concept Detection System at ImageCLEF 2009
van de Sande, K. E. A., Gevers, T., & Smeulders, A. W. M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15751-6_32 · Cited by 30
“To avoid using all visual features in an image, while incorporating translation invariance and a robustness to noise, we follow the well known codebook approach, see e.g. [8] [6] [23] [20] [19].”
MethodNonparametric kernel estimators for image classification
Póczos, B., Xiong, L., Sutherland, D. J., et al. (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.2012.6248028 · Cited by 30
Lakani, S. R., Rodríguez-Sánchez, A. J., & Piater, J. (2018) · Autonomous Robots · doi:10.1007/s10514-018-9787-5 · Cited by 30
“Given the training patches Y = { y 1 , . . . , y n } and their histogram of surface normals, we construct a dictionary (Leung and Malik 2001; Fei-Fei and Perona 2005; Lazebnik et al. 2006) from them.”
MethodWei, L., Han, J., Zhou, K., Bao, H., Guo, B., & Shum, H. (2008) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/1399504.1360651 · Cited by 33
Multimodal fusion recognition for digital twin
Zhou, T., Zhang, X., Kang, B., & Chen, M. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.10.009 · Cited by 32
A Survey of Recent Advances in Texture Representation.
Liu, L., Chen, J., Fieguth, P., Zhao, G., Chellappa, R., & Pietikäinen, M. (2018) · Cited by 29
Intensity Rank Estimation of Facial Expressions Based on a Single Image
Chang, K.-Y., Chen, C., & Hung, Y. (2013) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2013.538 · Cited by 29
“[21] have proved that complex Gabor function for ψ is effective for texture analysis.”
BackgroundLevel-of-detail representation of bidirectional texture functions for real-time rendering
Ma, W., Chao, S.-H., Tseng, Y.-T., et al. (2005) · ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D) · doi:10.1145/1053427.1053458 · Cited by 29
Subspace learning-based dimensionality reduction in building recognition
Li, J., & Allinson, N. M. (2009) · Neurocomputing · doi:10.1016/j.neucom.2009.08.016 · Cited by 28
Generalized Local Binary Patterns for Texture Classification
Li, L., Fieguth, P., & Kuang, G. (2011) · British Machine Vision Conference (BMVC) · doi:10.5244/c.25.123 · Cited by 29
“It is generally agreed that the local descriptors play a much important role, therefore have received considerable attention during the last decade and numerous local descriptors have been proposed [2, 3, 5, 6, 7, 8, 9].”
BackgroundVocabulary hierarchy optimization for effective and transferable retrieval
Ji, R., Xie, X., Yao, H., et al. (2009) · 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.2009.5206680 · Cited by 29
Supervised and unsupervised segmentation using superpixels, model estimation, and graph cut
Borovec, J., Švihlík, J., Kybic, J., et al. (2017) · Journal of Electronic Imaging · doi:10.1117/1.jei.26.6.061610 · Cited by 29
High-Precision Localization Using Ground Texture
Zhang, L., Finkelstein, A., & Rusinkiewicz, S. M. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794106 · Cited by 29
“Indeed computerbased modeling and recognition of textures traditionally proceeded along statistical lines [3, 4].”
BackgroundObject scale selection of hierarchical image segmentation with deep seeds
Al‐Huda, Z., Peng, B., Yang, Y. T., et al. (2020) · IET Image Processing · doi:10.1049/ipr2.12020 · Cited by 28
“For texture histograms, the image is convolved with a bank of 38 filters [29], “which consists of an edge and a bar filters at six orientations and three scales, a Gaussian and a Laplacian of Gaussian fil-ters”.”
BackgroundMethodUsing textons to rank crystallization droplets by the likely presence of crystals
Ng, J. T., Dekker, C., Kroemer, M., Osborne, M. A., & von Delft, F. (2014) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s1399004714017581 · Cited by 28
“In contrast to GLCM or spectral methods, textures can also be modelled by the joint distribution of filter response cluster centres, otherwise known as textons (Leung & Malik, 2001).”
BackgroundMethodLearning Dictionaries of Discriminative Image Patches
Dahl, A. B., & Larsen, R. (2011) · British Machine Vision Conference (BMVC) · doi:10.5244/c.25.77 · Cited by 28
“The proposed method is based on modeling an image using small image patches, which has similarities to sparse image coding [3, 14] and textons [7, 9, 17].”
MethodBuilding descriptive and discriminative visual codebook for large-scale image applications
Tian, Q. C., Zhang, S. S., Zhou, W., Ji, R., Ni, B., & Sebe, N. (2010) · Multimedia Tools and Applications · doi:10.1007/s11042-010-0636-6 · Cited by 26
Dong, X., Zhou, H., & Dong, J. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3019185 · Cited by 27
Al‐Huda, Z., Peng, B., Yang, Y. Z., Algburi, R. N. A., Ahmad, M., Khurshid, F., & Moghalles, K. (2021) · Neural Computing and Applications · doi:10.1007/s00521-020-05669-x · Cited by 27
Xiong, H., Pan, Z., Ye, X., & Chen, C. W. (2012) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2012.2221271 · Cited by 27
“filter bank [40], Cula and Dana filter bank [41], and Schmid filter bank [42] are among the most studied and best known recent texture analysis techniques.”
BackgroundMultimodal medical image retrieval
Kalpathy–Cramer, J., & Hersh, W. (2010) · Multimedia Information Retrieval · doi:10.1145/1743384.1743415 · Cited by 27
“Textons [22] are a popular and effective class of algorithms for texture categorization.”
BackgroundTexture classification with a dictionary of basic image features
Crosier, M., & Griffin, L. D. (2008) · 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.2008.4587663 · Cited by 27
“One approach which has proved particularly successful for this task involves representing images statistically as distributions of local features [1-10].”
BackgroundMethodGabor Layers Enhance Network Robustness
Pérez, J. C., Alfarra, M., Jeanneret, G., Bibi, A., Thabet, A., Ghanem, B., & Arbeláez, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58545-7_26 · Cited by 30
Representations and Techniques for 3D Object Recognition & Scene Interpretation
Hoiem, D., & Savarese, S. (2011) · Cited by 28
Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery
Hu, F., Xia, G.-S., Hu, J., Zhong, Y., & Xu, K. (2016) · Remote Sensing · doi:10.3390/rs8070555 · Cited by 26
“The maximum response filters (MR) [73] and the Schmid filters (S) [74], which are specially designed for texture recognition, are investigated; the results in Figure 9a show that a majority of learned filters perform far better (over 10 percent accuracy) than these two specially-designed filters.”
MethodColor Based Tracing in Real-Life Surveillance Data
Metternich, M. J., Worring, M., & Smeulders, A. W. M. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14298-7_2 · Cited by 26
“To obtain fixed-length feature vectors per image, the bag-of-words model is used [24], which is also known as ’textons’ [18], ’object parts’ [8] and ’codebooks’ [13, 17].”
MethodParsimonious Topic Models with Salient Word Discovery
Soleimani, H., & Miller, D. J. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2014.2345378 · Cited by 26
Fusing Sorted Random Projections for Robust Texture and Material Classification
Liu, L., Fieguth, P., Hu, D., et al. (2014) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2014.2359098 · Cited by 26
“…approach that first detects certain salient regions in a given image and then applies local descriptors, such as SIFT, RIFT, and SPIN to describe the selected regions [11], [23], [26]; the other is a dense approach that extracts local features pixel-by-pixel over the input image [10], [12]–[16].”
BackgroundTerrain classification using Laser Range Finder
Walas, K., & Nowicki, M. (2014) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2014.6943273 · Cited by 26
“Based on this assumption descriptor similar to textons [26] is calculated.”
Tan, P., Lin, S., & Quan, L. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70789 · Cited by 26
“In [12], 3D texton primitives are represented in terms of co-occurring responses to a bank of Gaussian derivative filters of different orientations and spatial frequencies, but for our context of photometric stereo, we instead describe these structural primitives simply as a concatenated vector of…”
Joint Shape and Texture Based X-Ray Cargo Image Classification
Zhang, J., Zhang, L., Zhao, Z., et al. (2014) · IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2014.48 · Cited by 26
“The main reason is that most sampling points fall into the plain gray region, which gives poor discriminality.”
BackgroundTexture image classification with discriminative neural networks
Song, Y., Li, Q., Feng, D. D., Zou, J. J., & Cai, W. (2016) · Computational Visual Media · doi:10.1007/s41095-016-0060-6 · Cited by 26
“It is however difficult to achieve accurate classification due to the large intra-class variation and low inter-class distinction [1, 2].”
BackgroundHybrid deep learning for Reflectance Confocal Microscopy skin images
Kaur, P., Dana, K., Cula, G. O., et al. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7899844 · Cited by 26
“Texture recognition methods have traditionally relied on multiscale, multi-orientation, gradient-like features called textons that are paralleled in the human visual system [7], [8], [9], [10], [11],”
BackgroundMethodRepresentations and Techniques for 3D Object Recognition and Scene Interpretation
Hoiem, D., & Savarese, S. (2011) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00370ed1v01y201107aim015 · Cited by 26
Improving texture categorization with biologically-inspired filtering
Vu, N., Nguyễn, T. P., & García, C. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.04.006 · Cited by 25
“Presented more recently for texture description are Gabor filters [21], the filter bank of Leung and Lalik [22], the MR8 [4], the filter bank of Crosier and Griffin [10], the morphological approaches of Hanbury et al.”
MethodArbeláez, P., Han, B.-G., Typke, D., Lim, J. J., Glaeser, R. M., & Malik, J. (2011) · Journal of Structural Biology · doi:10.1016/j.jsb.2011.05.017 · Cited by 25
“In computer vision, an operational version of textons was proposed by Leung and Malik (2001), who convoluted an image with a bank of Gaussian filters to generate a vector of filter responses at each image location.”
BackgroundSelf-citationDeep U-Net Regression and Hand-Crafted Feature Fusion for Accurate Blood Vessel Segmentation
Kassim, Y. M., Glinskii, O. V., Glinsky, V. V., et al. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803084 · Cited by 25
Al‐Huda, Z., Zhai, D., Yang, Y. Z., & Algburi, R. N. A. (2021) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001421540264 · Cited by 25
Learning Features for Tissue Classification with the Classification Restricted Boltzmann Machine
van Tulder, G., & de Bruijne, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13972-2_5 · Cited by 25
“On the airway dataset, an RBF SVM trained with Leung-Malik filters gives a test accuracy of 90.5%, against 91.3% for an RBF SVM trained on the raw intensity values.”
MethodInfluentialADHD classification using bag of words approach on network features
Solmaz, B., Dey, S., Rao, A. R., & Shah, M. A. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.911598 · Cited by 24
A distinct and compact texture descriptor
Quan, Y., Xu, Y., & Sun, Y. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.02.004 · Cited by 25
“In the past, a number of approaches [1, 2, 3, 4, 5, 6, 7, 8, 9] for texture description have been proposed to achieve robustness against environmental changes.”
BackgroundFast object detection based on color histograms and local binary patterns
Lee, K., Lee, C., Kim, S., et al. (2012) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2012.6412323 · Cited by 25
“Color histograms were also used for texture recognition [3] and matching [4].”
MethodKampouris, C., Mariolis, I. G., Peleka, G., Skartados, E., Kargakos, A., Triantafyllou, D., & Malassiotis, S. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487307 · Cited by 26
“Texture recognition was addressed extensively in the literature, and state-of-the-art techniques [16], [17], [18], [19] make use of”
BackgroundAutomated Recognition of Building Façades for Creation of As-Is Mock-Up 3D Models
Oskouie, P., Becerik-Gerber, B., & Soibelman, L. (2017) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000711 · Cited by 25
Unsupervised texture classification: Automatically discover and classify texture patterns
Qin, L., Zheng, Q., Jiang, S., Huang, Q., & Gao, W. (2007) · Image and Vision Computing · doi:10.1016/j.imavis.2007.08.003 · Cited by 25
“Many texture classification methods [1, 2, 3, 4] have been reported in literatures.”
BackgroundCombination of different texture features for mammographic breast density classification
Liasis, G., Pattichis, C. S., & Petroudi, S. (2012) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2012.6399758 · Cited by 25
“Textons are defined under the operational definition of clustered filter responses [26] and provide a statistical and structural unifying approach”
BackgroundMethod1 Parsimonious Topic Models with Salient Word DiscoveryPreprint
Soleimani, H., & Miller, D. J. (2016) · Cited by 25
Gangeh, M. J., Naini, A. S., Diu, M., Tadayyon, H., Kamel, M. S., & Czarnota, G. J. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2312254 · Cited by 24
“Texton-based approach learns a dictionary of texture atoms (textons) and subsequently represent each ROI by a histogram of these textons [41], [42], [43], [44].”
Contextual object category recognition for RGB-D scene labeling
Ali, H., Faisal Shafait, B., Giannakidou, E., Vakali, A. I., Figueroa, N., Varvadoukas, T., & Mavridis, N. (2013) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2013.10.001 · Cited by 24
“We combine the VFH with a 539-D texton histogram feature vector which uses oriented Gaussian filter responses to retrieve texture information [41].”
MethodDavid, D. S., & Jayachandran, A. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6265-1 · Cited by 24
“Medical images are always present in overlapping of gray-scale intensities for different tissues [17, 19, 20].”
BackgroundFusing learned representations from Riesz Filters and Deep CNN for lung tissue classification
Joyseeree, R., Otálora, S., Müller, H., & Depeursinge, A. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.06.006 · Cited by 23
“Grey–Level Co–occurrence Matrices (GLCM) (Haralick et al., 1973), Histograms Of Gradients (HOG) (Dalal and Triggs, 2005) used in the Scale– Invariant Feature Transform (SIFT) (Lowe, 2004), non–separable and sep-41 arable wavelets (Jeng-Shyang and Jing-Wein, 1999), Run–Length Encoding (RLE) (Xu et al., 2004), and orien…”
BackgroundDetecting mitotic cells in HEp-2 images as anomalies via one class classifier
Gupta, K., Bhavsar, A. V., & Sao, A. K. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103328 · Cited by 24
Carpenter, H. J., Ghayesh, M. H., Zander, A. C., Giovanni, G. D., Li, J., & Psaltis, P. J. (2022) · Tomography · doi:10.3390/tomography8030108 · Cited by 24
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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