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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 701–800 of 1,001
A Novel Approach to Build Image Ontology Using Texton
Minu, R. I., & Thyagharajan, K. K. (2012) · Advances in intelligent systems and computing · doi:10.1007/978-3-642-32063-7_35 · Cited by 13
Multiple Classifier Systems in Texton-Based Approach for the Classification of CT Images of Lung
Gangeh, M. J., Sørensen, L., Shaker, S. B., Kamel, M. S., & de Bruijne, M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-18421-5_15 · Cited by 13
“, filter banks [8, 15, 16], raw pixel representation [6], and Markov random field (MRF) representation, where the centra l pixel in a neighborhood is modeled using the neighboring pixels [6].”
BackgroundMethodSeeing the World Behind the Image: Spatial Layout for 3D Scene Understanding
Hoiem, D., Efros, A. A., & Kanade, T. (2007) · Cited by 13
Lu, Y., Kang, T., Zhang, H., & Lim, M. (2015) · IET Computer Vision · doi:10.1049/iet-cvi.2014.0249 · Cited by 13
The Visual N1 Is Sensitive to Deviations from Natural Texture Appearance
Balas, B., & Conlin, C. (2015) · PLoS ONE · doi:10.1371/journal.pone.0136471 · Cited by 13
“Some examples of image descriptors that meet these criteria and have been applied to texture recognition problems of various types include dipole or “needle” statistics [7], moments of the pixel intensity histogram [8], “textons” that are derived from cluster analyses of natural images [9] multi-scale orientation hist…”
BackgroundLin, G., & Zou, X. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.08.192 · Cited by 13
Raghavendra, U., Gudigar, A., Ciaccio, E. J., et al. (2019) · Expert Systems · doi:10.1111/exsy.12474 · Cited by 13
Liu, M., Lian, C., & Shen, D. G. (2019) · Intelligent systems reference library · doi:10.1007/978-3-030-32606-7_8 · Cited by 13
A FRAMEWORK FOR ANALYZING TEXTURE DESCRIPTORS
Ahonen, T., & Pietikäinen, M. (2008) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0001077305070512 · Cited by 13
“( Leung and Malik, 2001 ), (Varma and Zisserman, 2005).”
MethodInfluentialVehicle Categorization: Parts for Speed and Accuracy
Nowak, E., & Jurie, F. (2006) · IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS) · doi:10.1109/vspets.2005.1570926 · Cited by 13
“Leug and Malik [7] use a (cid:2)lter bank convolution to describe regularly sampled pixels, and k-means to compute the parts.”
MethodBag of Visual Words: A Soft Clustering Based Exposition
Garg, V., Vempati, S., & Jawahar, C. V. (2011) · National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) · doi:10.1109/ncvpripg.2011.15 · Cited by 13
“Bag of Words representation, motivated from field of documents search, was first used for the problem of texture recognition [6].”
MethodLillholm, M., & Griffin, L. D. (2008) · Image and Vision Computing · doi:10.1016/j.imavis.2008.08.003 · Cited by 13
“We adopt the general notion that an alphabet can be found through a partitioning [29,26,31,35, 54,60,15,17] of response space into categories of qualitatively similar structures.”
MethodEvent Recognition and Classification in Sports Video
Ellappan, V., & Rajkumar, R. (2017) · International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM) · doi:10.1109/icrtccm.2017.57 · Cited by 13
Robotic hand grasping of objects classified by using support vector machine and bag of visual words
Ergene, M. C., & Durdu, A. (2017) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap.2017.8090228 · Cited by 14
Shape Recipes: Scene Representations that Refer to the Image
Freeman, W. T., & Torralba, A. B. (2002) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 13
A versatile segmentation procedure
Vanzella, W., & Torre, V. (2006) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2005.859077 · Cited by 13
Lin, J., Gaurav Sharma, A., & Pappas, T. N. (2023) · e-Prime - Advances in Electrical Engineering Electronics and Energy · doi:10.1016/j.prime.2022.100092 · Cited by 14
“We can aggregate textons from Ω and establish a universal texton codebook TΩ [27].”
MethodSegmentation of structural features in cheese micrographs using pixel statistics
Impoco, G., Tuminello, L., Fucà, N., et al. (2011) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2011.09.013 · Cited by 12
“Several sets of filters have been proposed in the literature to compute features from greylevel images (Leung and Malik, 2001; Schmid et al., 2001; Geusebroek et al., 2003).”
BackgroundMulti-scale local pattern co-occurrence matrix for textural image classification
Sun, X., Wang, J., Chen, R., et al. (2012) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2012.6252374 · Cited by 12
Object classification based on visual and extended features for video surveillance application
Islam, M. K., Jahan, F., Min, J.-H., et al. (2011) · Cited by 12
Hu, R., Queen, C. M., & Zouridakis, G. (2013) · doi:10.1109/pht.2013.6461311 · Cited by 12
“In previous studies, different sampling strategies have been used: 1) Dense (grid) sampling: grid sampling [12] selects patches on a regular grid of a fixed size within the lesion.”
MethodDomain-Specific Image Captioning
Mason, R., & Eugene Charniak, D. (2014) · Conference on Computational Natural Language Learning · doi:10.3115/v1/w14-1602 · Cited by 12
“T EXTURE : Textons (Leung and Malik, 2001) are computed by convolving images with Gabor filters at multiple orientations and scales, then sampling the outputs at random locations.”
MethodTexture classification using block intensity and gradient difference (BIGD) descriptor
Hu, Y., Wang, Z., & AlRegib, G. (2020) · Signal Processing Image Communication · doi:10.1016/j.image.2019.115770 · Cited by 13
Matsuzaki, S., Miura, J., & Masuzawa, H. (2022) · Advanced Robotics · doi:10.1080/01691864.2022.2109427 · Cited by 15
New edge-adaptive image interpolation using anisotropic Gaussian filters
Han, J., Suryanto, Kim, J., et al. (2012) · Digital Signal Processing · doi:10.1016/j.dsp.2012.07.016 · Cited by 12
“In the proposed method, the edge information of the LR image, which is the edge orientation and energy, is first estimated using the modified Leung–Malik (LM) filter bank [12].”
MethodMulti-scale directional-filtering-based method for follicular lymphoma grading
Bozkurt, A., Suhre, A., & Çetin, A. E. (2014) · Signal Image and Video Processing · doi:10.1007/s11760-014-0681-0 · Cited by 12
“We compare the proposed feature extraction scheme with various multi-scale directional feature extraction algorithms, such as curvelets [4], contourlets [5] steerable pyramids [26], complex wavelets [14], Gabor filters [22], and texton filterbanks [17,18,24].”
MethodA Unifying Framework for LBP and Related Methods
Bianconi, F., & Fernández, A. (2013) · Studies in computational intelligence · doi:10.1007/978-3-642-39289-4_2 · Cited by 12
“In the first case features are extracted from the output of oriented linear filters [52, 44, 83, 69].”
A Novel Stochastic Tracking Approach on Human Movement Analysis
Hossain, M. A., Al-Turki, S. M., & Sanyal, G. (2014) · International Journal of Computer Applications · doi:10.5120/15089-3488 · Cited by 12
“The integral-image for getting the soul of speediness in estimation process is observed [8] in Haar-like-features extraction system.”
MethodShape Index Descriptors Applied to Texture-Based Galaxy Analysis
Pedersen, K. S., Stensbo-Smidt, K., Zirm, A., et al. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.303 · Cited by 12
“We follow the line of filter-based approaches [25, 31, 32] to texture analysis.”
MethodTexture Classification Based on Texton Features
Babu, U. R., Kumar, V., & Sujatha, B. K. (2012) · International Journal of Image Graphics and Signal Processing · doi:10.5815/ijigsp.2012.08.05 · Cited by 12
“The percentage of classification rates of the proposed method and crashes methods [7, 13] are listed in table 3.”
BackgroundMethodNon-contact terrain classification for autonomous mobile robot
Kim, J., Kim, D.-G., Lee, J.-H., et al. (2009) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2009.5420568 · Cited by 12
“Leung and Malik defined a concept of texton in operational manner [ 9 ].”
BackgroundLupșor‐Platon, M., Badea, R., Nedevschi, S., et al. (2006) · IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR) · doi:10.1109/aqtr.2006.254685 · Cited by 12
“- the autocorrelation index, characterizing the granularity of the texture [15] - the analysis of the structure of gray levels and the obtain ment of some information like those referring to the textural surface roughness, using fractal-based methods [16] - the analysis of the pattern of the intensity values at differ…”
MethodDominant orientation patch matching for HMAX
Lu, Y., Zhang, H., Kang, T., & Lim, M. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.01.069 · Cited by 12
“Bag-of-features [11] is effective for resolving this issue; however, the amount of structural information still falls short.”
BackgroundYamazaki, K. (2016) · Autonomous Robots · doi:10.1007/s10514-016-9559-z · Cited by 12
“This approach is similar to the use of a filter bank (Geusebroek et al. 2003; Schmid 2001), a method applied most frequently to texture classification (Galun et al. 2003; Leung and Malik 2001; Martin et al. 2004).”
MethodBozkurt, A., Gale, T., Köse, K., Alessi‐Fox, C., Brooks, D. H., Rajadhyaksha, M. M., & Dy, J. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.108 · Cited by 14
“use Leung-Malik (LM) filter bank [16] based texton features to model the textural appearance of individual RCM slices.”
MethodWeakly Supervised Dictionary Learning
You, Z., Raich, R., Fern, X. Z., & Kim, J. (2018) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2807422 · Cited by 12
Monocular Visual-IMU Odometry: A Comparative Evaluation of Detector–Descriptor-Based Methods
Dong, X., Dong, X., Dong, J., & Zhou, H. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2919003 · Cited by 12
“…performance to these results when used with FAST [15] and performs better than that it does on Routes 1, 2 and 3; (3) HOG [26], SURF [20] and LM [28] perform properly while LSSD [29] only produces proper performance when used with B&C [23], DoG [19] or FAST [15]; (4) IMGP [27] yields its best…”
BackgroundMethodInfluentialOnline data summaries for semantic mapping and anomaly detection with autonomous underwater vehicles
Kaeli, J. W., & Singh, H. (2015) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.1109/oceans-genova.2015.7271577 · Cited by 12
“While this method has been extremely useful in generating texton dictionaries for texture analysis [13], [14], the fact that the cluster centers are not guaranteed to occur at a data point makes mapping back to a single representative image for each class difficult.”
BackgroundMethodLearning efficient codes for 3D face recognition
Zhong, C., Sun, Z., & Tan, T. (2008) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2008.4712158 · Cited by 12
Visual summaries for low-bandwidth semantic mapping with autonomous underwater vehicles
Kaeli, J. W., Leonard, J. J., & Singh, H. (2014) · IEEE/OES Autonomous Underwater Vehicles · doi:10.1109/auv.2014.7054429 · Cited by 12
“While this method has been extremely useful in generating texton dictionaries for texture analysis [13], [14], the fact that the cluster centers are not guaranteed to occur at a data point makes mapping back to a single representative image for each class difficult.”
MethodKumar, N., Rajwade, A. V., Chandran, S., & Awate, S. P. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8297065 · Cited by 12
Smith, M. L., Farooq, A. R., Smith, L. N., & Midha, P. S. (2000) · Sensor Review · doi:10.1108/02602280010351028 · Cited by 12
A novel approach for texture shape recovery
Wang, J., & Dana (2003) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238650 · Cited by 12
Hybrid textons: modeling surfaces with reflectance and geometry
Wang, J., & Dana, K. (2004) · 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.2004.1315056 · Cited by 12
Multi-modal panoramic 3D outdoor datasets for place categorization
Jung, H., Oto, Y., Mozos, O. M., Iwashita, Y., & Kurazume, R. (2016) · 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759669 · Cited by 16
“In this work we compared three feature descriptors that can be applied to 3D and multimodal sensor cues: local binary patterns (LBP) [17], spin images [18], and textons [19].”
MethodGabor Filter Parameters Optimization for Texture Classification Based on Genetic Algorithm
Afshang, M., Helfroush, M. S., & Zahernia, A. (2009) · International Conference on Machine Vision · doi:10.1109/icmv.2009.50 · Cited by 11
“Since texture classification is changed to an interesting subject and considering that classification algorithm is affected by parameters such as choice of filter bank and rotational invariance property [2, 3, 4], scientists have proposed different feature extraction methods.”
BackgroundNiu, M., & Lo, C. (2022) · Virtual Reality · doi:10.1007/s10055-021-00613-3 · Cited by 12
“There are also many studies on the influence of external factors such as illumination on material perception (Leung and Malik 2001; Vangorp et al. 2017; Lagunas et al. 2019).”
BackgroundA new gaussian mixture conditional random field model for indoor image labeling
Wang, X., Zhang, X., Clarke, I., et al. (2009) · doi:10.1145/1631040.1631049 · Cited by 11
“The features are RGB and Lab color features and LM filter features [7].”
Optic Disc Segmentation by Balloon Snake with Texture from Color Fundus Image
Sun, J., Luan, F., & Wu, H. (2015) · International Journal of Biomedical Imaging · doi:10.1155/2015/528626 · Cited by 11
Anwer, R. M., Khan, F. S., van de Weijer, J., & Laaksonen, J. T. (2017) · International Conference on Multimedia Retrieval · doi:10.1145/3078971.3079001 · Cited by 11
“Most texture description approaches are based on orderless distribution of local features leading to the development of several successful image classication models, including histograms of vector quantized lter responses [28], textons theory [29], bag-of-words [10] and later the Fisher Vector [40].”
BackgroundDeep learning and texture-based semantic label fusion for brain tumor segmentation
Alam, M., Vidyaratne, L., Shboul, Z. A., et al. (2018) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2292930 · Cited by 11
“Then spatially varying texture features such as PTPSA [27], multifractal Brownian motion (mBm) [16], regular texture (texton) [28] and intensity are extracted from each preprocessed MRI modality.”
MethodDeep Learning Based Brain Tumor Segmentation: Recent Updates
Patil, R. B., Ansingkar, N. P., & Deshmukh, P. D. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-1122-4_42 · Cited by 11
Anaam, A., Al–antari, M. A., & Gofuku, A. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105191 · Cited by 11
Texton-based analysis of paintings
van der Maaten, L. J. P., & Postma, E. O. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.863082 · Cited by 12
Scenery Character Detection with Environmental Context
Kunishige, Y., Feng, Y., & Uchida, S. (2011) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2011.212 · Cited by 11
“In this paper, by referring very recent trials [3], [4], the following twelve features (C1-12) are extracted for each CC: the ratio between areas of the CC and the entire image, the ratio between width of the CC and the entire image, the aspect ratio of the CC, contour roughness, the number of…”
BackgroundFrequential versus spatial colour textons for breast TMA classification
Fernández-Carrobles, M. M., Bueno, G., Déniz, Ó., Salido, J., García‐Rojo, M., & Gonzández-López, L. (2014) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2014.11.009 · Cited by 13
A Review on Texture Analysis Methods in Biomedical Image Processing
Ahmadvand, A., & Daliri, M. R. (2016) · OMICS Journal of Radiology · doi:10.4172/2167-7964.1000e136 · Cited by 11
“The other important groups of texture analysis methods which have been considered in biomedical image analysis applications are filter bank based methods [13].”
BackgroundGhodrati, S., Kandi, S. G., & Mohseni, M. (2018) · Journal of the Optical Society of America A · doi:10.1364/josaa.35.000712 · Cited by 11
Textural features selection for image classification by Bayesian method
Vovan, T., Che‐Ngoc, H., & Trang, T. N. (2017) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/fskd.2017.8393365 · Cited by 11
“In literature, we can find two main approaches that interested by many researchers: signal processing approach [1]–[3] and statistical approach [4]–[6].”
MacNeil, J. M. L., Ushizima, D. M., Panerai, F., Mansour, N. N., Barnard, H. S., & Parkinson, D. Y. (2019) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11429 · Cited by 12
“A traditional approach would be to use an over-complete representation such as directional Gaussian derivate filters for multiple orientations, frequencies and scales used in Textons [20], but this is likely to be redundant for three-dimensional (3D) textile analysis.”
MethodUnconstrained texture classification using efficient jet texton learning
Roy, S. K., Ghosh, D. K., Dubey, S. R., et al. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105910 · Cited by 11
“Leung and Malik [19] have proposed a method where the training images are enrolled and a vector of 48 dimensional response is generated for each class using a filtered operation.”
MethodA set of statistical radial binary patterns for tree species identification based on bark images
Boudra, S., Yahiaoui, I., & Behloul, A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08874-x · Cited by 11
Kim, G., Hebert, M., & Park, S.-K. (2007) · Springer eBooks · doi:10.1007/978-3-540-76729-9_20 · Cited by 11
“The texture descriptor is a 32 dimensional texture histogram based on Leung-Malik filter banks [ 11 ].”
MethodRegion Contextual Visual Words for scene categorization
Liu, C., Xu, D., & Feng, S. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.03.037 · Cited by 11
Multiband image segmentation and object recognition using texture filter banks
Kang, Y., Kidono, K., Naito, T., et al. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761332 · Cited by 11
Texture CLassification Using Compressed Sensing
Liu, L., & Fieguth, P. (2010) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2010.16 · Cited by 11
“Among the most popular dense descriptors are the use of large support filter banks to extract texture features at multiple scales and orientations [3] [4] [5].”
BackgroundHypotheses for Image Features, Icons and Textons
Griffin, L. D., & Lillholm, M. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-006-6355-9 · Cited by 11
A Multiclassifier Approach for Lung Nodule Classification
Pereira, C., Alexandre, L. A., Mendonça, A. M., & Campilho, A. (2006) · Lecture notes in computer science · doi:10.1007/11867661_55 · Cited by 11
“Leung and Malik filter set is a multi-scale, multi-orientation filter bank with 48 filters [14].”
BackgroundLinear Discriminant Analysis for Signatures
Huh, S., & Lee, D. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2090047 · Cited by 11
Addressing the ImageClef 2009 Challenge Using a Patch-based Visual Words Representation
Avni, U., Goldberger, J., & Greenspan, H. K. (2009) · Cited by 11
SD-SEM: sparse-dense correspondence for 3D reconstruction of microscopic samples
Baghaie, A., Tafti, A. P., Owen, H. A., et al. (2017) · Micron · doi:10.1016/j.micron.2017.03.009 · Cited by 11
Vision-based entrance detection in outdoor scenes
Talebi, M., Vafaei, A., & Monadjemi, A. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5846-3 · Cited by 11
“We apply a subset of the filter bank designed by Leung and Malik (LM filters) [18] and generate the filters using publicly available code [14] with the following parameters: 19 × 19 pixel support, scale for oriented and blob filters, and 6 orientations.”
Zhang, Z., Li, D., & Liu, S. (2018) · IEEE Access · doi:10.1109/access.2018.2874994 · Cited by 11
“As for BoW, we take a 9 × 9 neighborhood around each pixel as a patch, and stretch each patch into an 81-dimensional vector.”
MethodChen, Y., Tong, X., Wang, J., et al. (2004) · ACM SIGGRAPH Conference Papers (SIGGRAPH) · doi:10.1145/1186562.1015726 · Cited by 11
“Leung and Malik introduced 3D textons for analyzing surface mesostructures [Leung and Malik 2001], and building on this work, Tong et al. derived an algorithm for synthesizing a BTF on arbitrary surfaces [Tong et al. 2002].”
MethodKnowledge Based Perceptual Anchoring
Daoutis, M. (2013) · Künstliche Intell. · doi:10.1007/s13218-013-0249-0 · Cited by 11
“One simple method is to perform K -means clustering over the dataset (or a subset of a dataset) [95] (see § 3.”
MethodOn the smoothing of multinomial estimates using Liouville mixture models and applications
Bouguila, N. (2011) · Pattern Analysis and Applications · doi:10.1007/s10044-011-0236-8 · Cited by 10
“bag of words representation) [5], images representation via visual words [6], texture classification using textons [7] or cooccurrence matrices [8], and protein classification [9].”
MethodKernels for Visual Words Histograms
Ionescu, R. T., & Popescu, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41181-6_9 · Cited by 10
“The vector quantization process is done by k-means clustering [9], and visual words are stored in a randomized forest of k-d trees [11] to reduce search cost.”
MethodAnnotation of Online Shopping Images without Labeled Training Examples
Mason, R., & Eugene Charniak, D. (2013) · Cited by 10
A CAD System for the Acquisition and Classification of Breast TMA in Pathology.
Fernández-Carrobles, M. M., Bueno, G., Déniz, Ó., et al. (2015) · PubMed · Cited by 10
Improving Robot Mobility by Combining Downward-Looking and Frontal Cameras
González, R., Rituerto, A., & Guerrero, J. J. (2016) · Robotics · doi:10.3390/robotics5040025 · Cited by 10
“The most typical ways to implement global descriptors are using textons [21] and Gist [22,23].”
BackgroundMethodLinear-Time Learning on Distributions with Approximate Kernel Embeddings
Sutherland, D. J., Oliva, J. B., Póczos, B., et al. (2016) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v30i1.10308 · Cited by 10
“Learning on distributions In computer vision, the popular “bag of words” model (Leung and Malik 2001) represents a distribution by quantizing it onto codewords (usually by k-means on points from all sets), then compares those histograms with some kernel (often exponentiated χ(2)).”
BackgroundA Comprehensive Taxonomy of Dynamic Texture Representation
Nguyễn, T. P., & Nguyen, T. T. (2021) · ACM Computing Surveys · doi:10.1145/3487892 · Cited by 10
Extended complete local binary pattern for texture classification
Zeng, Q., Jianhua, A., Xiao-ya, S., & Hong, S. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11776-1 · Cited by 10
“Leung et al. studied the identification of texture images acquired under different imaging angles and illumination conditions, and they proposed the LM filter algorithm [16].”
MethodPrototype Network for Predicting Occluded Picking Position Based on Lychee Phenotypic Features
Li, Y., Liao, J., Wang, J., Luo, Y., & Lan, Y. (2023) · Agronomy · doi:10.3390/agronomy13092435 · Cited by 10
“The architecture presented in this paper draws inspiration from Prototype Generation, with the goal of creating an encoder that predicts a set of k prototype masks covering the entire image [30,31].”
Achievements and Challenges in Recognizing and Reconstructing Civil Infrastructure
Brilakis, I., Dai, F., & Radopoulou, S. C. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34091-8_7 · Cited by 10
“During this step, three spot filters of the Leung-Malik (LM) filter bank [68] and one”
MethodProstate cancer diagnosis using quantitative phase imaging and machine learning algorithms
Nguyen, T. H., Sridharan, S., Macias, V., et al. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2080321 · Cited by 10
“Each input SLIM images is convolved with a filter bank, here, a modified version of the Leung-Malik filter bank proposed in [18].”
MethodEstimating parameters of an illumination model for the synthesis of specular surface textures
Dong, J., & Chantler, M. J. (2004) · IEEE International Conference on Computer and Information Technology (CIT) · doi:10.1109/cit.2004.1357279 · Cited by 10
“The synthesized representation maps are finally relit using the illumination model to produce new images under arbitrary illumination directions.”
MethodToward parts-based scene understanding with pixel-support parts-sparse pictorial structures
Corso, J. J. (2012) · Pattern Recognition Letters · doi:10.1016/j.patrec.2012.04.004 · Cited by 10
“The texton maps use a 61-channel filter bank [Varma and Zisserman, 2005] of combined Leung-Malik [Leung and Malik, 2001] and Schmid filters [Schmid, 2001], followed by a k-Means process with 64 cluster centers.”
MethodLearning Long-range Terrain Perception for Autonomous Mobile Robots
Wang, M., Zhou, J. C., Tu, J., et al. (2010) · International Journal of Advanced Robotic Systems · doi:10.5772/7245 · Cited by 10
BoFF: A bag of fuzzy deep features for texture recognition
Florindo, J. B., & Esmi, E. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119627 · Cited by 12
Kennel, P., Fiorio, C., & Borne, F. (2015) · Pattern Analysis and Applications · doi:10.1007/s10044-015-0491-1 · Cited by 10
Multi-class Image Classification Based on Fast Stochastic Gradient Boosting
Li, L., Wu, Y., & Ye, M. (2014) · Informatica · Cited by 10
Learning to Count Cells: Applications to lens-free imaging of large fields
Flaccavento, G., Lempitsky, V., Pope, I., et al. (2011) · ORCA Online Research @Cardiff (Cardiff University) · Cited by 10
Moment based wavelet filter design for fingerprint classification
Saini, M. K., Saini, J. S., & Sharma, S. (2013) · doi:10.1109/icspcom.2013.6719795 · Cited by 10
“It has traditionally been thought that many filters at different orientations and scales are necessary to extract rich fe atures [5].”
BackgroundPerceptual texture similarity estimation
Dong, X. (2014) · Cited by 10
Griffin, L. D. (2005) · Network Computation in Neural Systems · doi:10.1080/09548980500289874 · Cited by 10
Quantitative MR Image Analysis for Brain Tumor
Shboul, Z. A., Reza, S. M. S., & Iftekharuddin, K. M. (2017) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-3-319-68195-5_2 · Cited by 10
“The stochastic multiresolution texture (mBm, PTPSA, and GmBm), regular texture (texton) [13] and intensity features are extracted from each preprocessed MRI modality.”
MethodFace recognition under varying illuminations with multi-scale gradient maximum response
Faraji, M. R., & Qi, X. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.04.062 · Cited by 10
Iterative screen optimization maximizes the efficiency of macromolecular crystallization
Jones, H. G., Wrapp, D., Gilman, M. S. A., et al. (2019) · Acta Crystallographica Section F Structural Biology Communications · doi:10.1107/s2053230x18017338 · Cited by 10
Material Recognition for Efficient Acquisition of Geometry and Reflectance
Weinmann, M., & Klein, R. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16199-0_23 · Cited by 10
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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