Relevant bibliographies by topics / Local binary patterns (LBP)

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Academic literature on the topic 'Local binary patterns (LBP)'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 June 2025

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Journal articles on the topic "Local binary patterns (LBP)"

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Vijaya Kumar, Vakulabharanam, Vishnu Murthy. G, and A. Obulesu. "A Grammar for Representing Uniform Local Binary Patterns (ULBP)." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 1 (2014): 5329–36. http://dx.doi.org/10.24297/ijct.v14i1.2129.

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One of the recently invented theoretically simple and efficient operators that is playing a significant role in image processing, pattern recognition and other domains is the Local Binary Pattern (LBP). LBP measures the local texture features on a given neighbourhood efficiently. The Uniform LBP (ULBP) that is derived on LBP is treated as the fundamental unit of the textures. There 58 ULBPs out of 256 LBPs on a 3 x3 neighbourhood. Further the textures on average contain 75% to 90% of the patterns as ULBP only. One disadvantage is representing all 58 ULBS. To say whether a given LBP is uniform or not one should march the given LBP with 58 Patterns. Very few research scholars derived grammar to represent patterns that arises from 2-D images. The present paper addresses the above problem by generating a simple array grammar that represents all ULBP on a 3 x 3 neighbourhood or with 8 neighbours.The present paper tests the proposed Array Grammar model of ULBP (AG-ULBP) with various LBP patterns to prove its accuracy.
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Sharma, Richa, and Madan Lal. "Comparative Analysis of Texture Classification Using Local Binary Pattern and Its Variants." International Journal of Information System Modeling and Design 8, no. 2 (2017): 45–56. http://dx.doi.org/10.4018/ijismd.2017040103.

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Texture classification is an important issue in digital image processing and the Local Binary pattern (LBP) is a very powerful method used for analysing textures. LBP has gained significant popularity in texture analysis world. However, LBP method is very sensitive to noise and unable to capture the macrostructure information of the image. To address its limitation, some variants of LBP have been defined. In this chapter, the texture classification performance of LBP has been compared with the five latest high-performance LBP variants, like Centre symmetric Local Binary Pattern (CS-LBP), Orthogonal Combination of Local Binary Patterns (OC LBP), Rotation Invariant Local Binary Pattern (RLBP), Dominant Rotated Local Binary Pattern (DRLBP) and Median rotated extended local binary pattern (MRELBP). This was by using the standard images Outex_TC_0010 dataset. From the experimental results it is concluded that DRLBP and MRELBP are the best methods for texture classification.
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Petranek, Karel, Pavel Janecka, and Jan Vanek. "Using local binary patterns for object detection in images." Global Journal of Computer Science 5, no. 1 (2015): 07. http://dx.doi.org/10.18844/gjcs.v5i1.24.

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<p>The article discusses a texture operator called Local Binary Patterns (LBP) and its applications in image processing and object detection. We provide a description of the algorithm for computing LBP together with a rationale for using LBP as a feature for object detection and image recognition. Based on the algorithm we show that LBP features have a low computational overhead compared to more complicated image features such as the commonly used SIFT or SURF features or neural network based approaches because they exploit the use of extremely fast bitwise and integer operations of the CPU. We demonstrate that LBP is robust to changes in brightness, contrast, image rotation, image scale. We develop two enhancements for LBP that improve its resistance to camera noise and enhance the discriminative power of LBP when it is used as a feature for machine learning algorithms. We present the results on a challenging real-world object detection task.</p><p> </p><p>Keywords: computer vision, object detection, local binary patterns.</p>
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Rassem, Taha H., and Bee Ee Khoo. "Completed Local Ternary Pattern for Rotation Invariant Texture Classification." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/373254.

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Despite the fact that the two texture descriptors, the completed modeling of Local Binary Pattern (CLBP) and the Completed Local Binary Count (CLBC), have achieved a remarkable accuracy for invariant rotation texture classification, they inherit some Local Binary Pattern (LBP) drawbacks. The LBP is sensitive to noise, and different patterns of LBP may be classified into the same class that reduces its discriminating property. Although, the Local Ternary Pattern (LTP) is proposed to be more robust to noise than LBP, however, the latter’s weakness may appear with the LTP as well as with LBP. In this paper, a novel completed modeling of the Local Ternary Pattern (LTP) operator is proposed to overcome both LBP drawbacks, and an associated completed Local Ternary Pattern (CLTP) scheme is developed for rotation invariant texture classification. The experimental results using four different texture databases show that the proposed CLTP achieved an impressive classification accuracy as compared to the CLBP and CLBC descriptors.
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Nagaraju, C., D. Sharadamani, C. Maheswari, and D. Vishnu Vardhan. "Evaluation of LBP-Based Facial Emotions Recognition Techniques to Make Consistent Decisions." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 06 (2015): 1556008. http://dx.doi.org/10.1142/s021800141556008x.

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Decision making is one of the smouldering problems in day to day works. Human emotions play crucial role in decision-making systems. While person is in high emotion he cannot make proper decision. Robust local binary pattern (RLBP) operator is more powerful to recognize the emotions and extends the features of local binary pattern (LBP). However, there are some precincts like discriminating bright faces against dark features and vice versa and intra-class variances increase. The RLBP solves this problem by finding minimum of LBP codes and their complements. However, it miss the mark for different local structures a similar feature is obtained, weak contrast local patterns and similar strong contrast local patterns. Hence, the discriminative robust local binary pattern (DRLBP) method is proposed to retain the contrast information of image patterns next to considering both edge and texture information. Nevertheless, LBP family methods are highly sensitive to noise. To trounce these drawbacks this paper extends fuzzy rule-based DRLBP which is more robust to noise, low contrasted, uneven lighting conditions, variations in expressions and rotation variant images.
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Shakoor, Mohammad Hossein, and Reza Boostani. "Extended Mapping Local Binary Pattern Operator for Texture Classification." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 06 (2017): 1750019. http://dx.doi.org/10.1142/s0218001417500197.

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In this paper, an Extended Mapping Local Binary Pattern (EMLBP) method is proposed that is used for texture feature extraction. In this method, by extending nonuniform patterns a new mapping technique is suggested that extracts more discriminative features from textures. This new mapping is tested for some LBP operators such as CLBP, LBP, and LTP to improve the classification rate of them. The proposed approach is used for coding nonuniform patterns into more than one feature. The proposed method is rotation invariant and has all the positive points of previous approaches. By concatenating and joining two or more histograms significant improvement can be made for rotation invariant texture classification. The implementation of proposed mapping on Outex, UIUC and CUReT datasets shows that proposed method can improve the rate of classifications. Furthermore, the introduced mapping can increase the performance of any rotation invariant LBP, especially for large neighborhood. The most accurate result of the proposed technique has been obtained for CLBP. It is higher than that of some state-of-the-art LBP versions such as multiresolution CLBP and CLBC, DLBP, VZ_MR8, VZ_Joint, LTP, and LBPV.
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Oppedal, Ketil, Trygve Eftestøl, Kjersti Engan, Mona K. Beyer, and Dag Aarsland. "Classifying Dementia Using Local Binary Patterns from Different Regions in Magnetic Resonance Images." International Journal of Biomedical Imaging 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/572567.

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Dementia is an evolving challenge in society, and no disease-modifying treatment exists. Diagnosis can be demanding and MR imaging may aid as a noninvasive method to increase prediction accuracy. We explored the use of 2D local binary pattern (LBP) extracted from FLAIR and T1 MR images of the brain combined with a Random Forest classifier in an attempt to discern patients with Alzheimer's disease (AD), Lewy body dementia (LBD), and normal controls (NC). Analysis was conducted in areas with white matter lesions (WML) and all of white matter (WM). Results from 10-fold nested cross validation are reported as mean accuracy, precision, and recall with standard deviation in brackets. The best result we achieved was in the two-class problem NC versus AD + LBD with total accuracy of 0.98 (0.04). In the three-class problem AD versus LBD versus NC and the two-class problem AD versus LBD, we achieved 0.87 (0.08) and 0.74 (0.16), respectively. The performance using 3DT1 images was notably better than when using FLAIR images. The results from the WM region gave similar results as in the WML region. Our study demonstrates that LBP texture analysis in brain MR images can be successfully used for computer based dementia diagnosis.
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Darapureddy, Nagadevi, Nagaprakash Karatapu, and Tirumala Krishna Battula. "Local Derivative Vector Pattern: Hybrid Pattern for Content-Based Medical Image Retrieval." Review of Computer Engineering Studies 7, no. 4 (2020): 79–86. http://dx.doi.org/10.18280/rces.070401.

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This paper examines a hybrid pattern i.e. Local derivative Vector pattern and comparasion of this pattern over other different patterns for content-based medical image retrieval. In recent years Pattern-based texture analysis has significant popularity for a variety of tasks like image recognition, image and texture classification, and object detection, etc. In literature, different patterns exist for texture analysis. This paper aims at forming a hybrid pattern compared in terms of precision, recall and F1-score with different patterns like Local Binary Pattern (LBP), Local Derivative Pattern (LDP), Completed Local Binary Pattern (CLBP), Local Tetra Pattern (LTrP), Local Vector Pattern (LVP) and Local Anisotropic Pattern (LAP) which were applied on medical images for image retrieval. The proposed method is evaluated on different modalities of medical images. The results of the proposed hybrid pattern show biased performance compared to the state-of-the-art. So this can further extended with other pattern to form a hybrid pattern.
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Prakasa, Esa. "Texture Feature Extraction by Using Local Binary Pattern." Jurnal INKOM 9, no. 2 (2016): 45. http://dx.doi.org/10.14203/j.inkom.420.

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Local Binary Pattern (LBP) is a method that used to describe texture characteristics of the surfaces. By applying LBP, texture pattern probability can be summarised into a histogram. LBP values need to be determined for all of the image pixels. Texture regularity might be determined based on the distribution shape of the LBP histogram. The implementation results of LBP on two texture types - synthetic and natural textures - shows that extracted texture feature can be used as input for pattern classification. Euclidean distance method is applied to classify the texture pattern obtained from LBPcomputation.
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Satria, M. Adhi, Kurniawan Nur Ramadhani, and Anditya Arifianto. "Pengenalan Huruf Isyarat Tangan Menggunakan Ekstraksi Ciri Local Binary Pattern." Indonesian Journal on Computing (Indo-JC) 3, no. 1 (2018): 75. http://dx.doi.org/10.21108/indojc.2018.3.1.215.

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<p>Pada penelitian ini dibangun sistem pengenalan huruf isyarat tangan menggunakan metode ekstraksi ciri Local Binary Patterns (LBP). Metode LBP memiliki kehandalan dalam melakukan analisis tekstur, mengatasi penskalaan dan citra yang kabur. Untuk algoritma klasifikasi, digunakan metode k-Nearest Neighbour (KNN) dan Support Vector Machine (SVM). Parameter LBP terbaik didapatkan untuk nilai R=10 dan P=16 menggunakan SVM dengan kernel Gaussian. Performansi terbaik dalam penelitian ini didapatkan untuk nilai F1-Score 99,84%.</p>
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Dissertations / Theses on the topic "Local binary patterns (LBP)"

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Zhu, Chao. "Effective and efficient visual description based on local binary patterns and gradient distribution for object recognition." Phd thesis, Ecole Centrale de Lyon, 2012. http://tel.archives-ouvertes.fr/tel-00755644.

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Cette thèse est consacrée au problème de la reconnaissance visuelle des objets basé sur l'ordinateur, qui est devenue un sujet de recherche très populaire et important ces dernières années grâce à ses nombreuses applications comme l'indexation et la recherche d'image et de vidéo , le contrôle d'accès de sécurité, la surveillance vidéo, etc. Malgré beaucoup d'efforts et de progrès qui ont été fait pendant les dernières années, il reste un problème ouvert et est encore considéré comme l'un des problèmes les plus difficiles dans la communauté de vision par ordinateur, principalement en raison des similarités entre les classes et des variations intra-classe comme occlusion, clutter de fond, les changements de point de vue, pose, l'échelle et l'éclairage. Les approches populaires d'aujourd'hui pour la reconnaissance des objets sont basé sur les descripteurs et les classiffieurs, ce qui généralement extrait des descripteurs visuelles dans les images et les vidéos d'abord, et puis effectue la classification en utilisant des algorithmes d'apprentissage automatique sur la base des caractéristiques extraites. Ainsi, il est important de concevoir une bonne description visuelle, qui devrait être à la fois discriminatoire et efficace à calcul, tout en possédant certaines propriétés de robustesse contre les variations mentionnées précédemment. Dans ce contexte, l'objectif de cette thèse est de proposer des contributions novatrices pour la tâche de la reconnaissance visuelle des objets, en particulier de présenter plusieurs nouveaux descripteurs visuelles qui représentent effectivement et efficacement le contenu visuel d'image et de vidéo pour la reconnaissance des objets. Les descripteurs proposés ont l'intention de capturer l'information visuelle sous aspects différents. Tout d'abord, nous proposons six caractéristiques LBP couleurs de multi-échelle pour traiter les défauts principaux du LBP original, c'est-à-dire, le déffcit d'information de couleur et la sensibilité aux variations des conditions d'éclairage non-monotoniques. En étendant le LBP original à la forme de multi-échelle dans les différents espaces de couleur, les caractéristiques proposées non seulement ont plus de puissance discriminante par l'obtention de plus d'information locale, mais possèdent également certaines propriétés d'invariance aux différentes variations des conditions d'éclairage. En plus, leurs performances sont encore améliorées en appliquant une stratégie de l'image division grossière à fine pour calculer les caractéristiques proposées dans les blocs d'image afin de coder l'information spatiale des structures de texture. Les caractéristiques proposées capturent la distribution mondiale de l'information de texture dans les images. Deuxièmement, nous proposons une nouvelle méthode pour réduire la dimensionnalité du LBP appelée la combinaison orthogonale de LBP (OC-LBP). Elle est adoptée pour construire un nouveau descripteur local basé sur la distribution en suivant une manière similaire à SIFT. Notre objectif est de construire un descripteur local plus efficace en remplaçant l'information de gradient coûteux par des patterns de texture locales dans le régime du SIFT. Comme l'extension de notre première contribution, nous étendons également le descripteur OC-LBP aux différents espaces de couleur et proposons six descripteurs OC-LBP couleurs pour améliorer la puissance discriminante et la propriété d'invariance photométrique du descripteur basé sur l'intensité. Les descripteurs proposés capturent la distribution locale de l'information de texture dans les images. Troisièmement, nous introduisons DAISY, un nouveau descripteur local rapide basé sur la distribution de gradient, dans le domaine de la reconnaissance visuelle des objets. [...]
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Doshi, Niraj P. "Multi-dimensional local binary pattern texture descriptors and their application for medical image analysis." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/17332.

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Texture can be broadly stated as spatial variation of image intensities. Texture analysis and classification is a well researched area for its importance to many computer vision applications. Consequently, much research has focussed on deriving powerful and efficient texture descriptors. Local binary patterns (LBP) and its variants are simple yet powerful texture descriptors. LBP features describe the texture neighbourhood of a pixel using simple comparison operators, and are often calculated based on varying neighbourhood radii to provide multi-resolution texture descriptions. A comprehensive evaluation of different LBP variants on a common benchmark dataset is missing in the literature. This thesis presents the performance for different LBP variants on texture classification and retrieval tasks. The results show that multi-scale local binary pattern variance (LBPV) gives the best performance over eight benchmarked datasets. Furthermore, improvements to the Dominant LBP (D-LBP) by ranking dominant patterns over complete training set and Compound LBP (CM-LBP) by considering 16 bits binary codes are suggested which are shown to outperform their original counterparts. The main contribution of the thesis is the introduction of multi-dimensional LBP features, which preserve the relationships between different scales by building a multi-dimensional histogram. The results on benchmarked classification and retrieval datasets clearly show that the multi-dimensional LBP (MD-LBP) improves the results compared to conventional multi-scale LBP. The same principle is applied to LBPV (MD-LBPV), again leading to improved performance. The proposed variants result in relatively large feature lengths which is addressed using three different feature length reduction techniques. Principle component analysis (PCA) is shown to give the best performance when the feature length is reduced to match that of conventional multi-scale LBP. The proposed multi-dimensional LBP variants are applied for medical image analysis application. The first application is nailfold capillary (NC) image classification. Performance of MD-LBPV on NC images is highest, whereas for second application, HEp-2 cell classification, performance of MD-LBP is highest. It is observed that the proposed texture descriptors gives improved texture classification accuracy.
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Cavalcante, Tarique da Silveira. "Método de superfícies ativas usando local binary patterns (LBP) aplicado na segmentação de lobos pulmonares em imagens de tomografia computadorizada do tórax." reponame:Repositório Institucional da UFC, 2016. http://www.repositorio.ufc.br/handle/riufc/19549.

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CAVALCANTE, T. S. Método de superfícies ativas usando local binary patterns (LBP) aplicado na segmentação de lobos pulmonares em imagens de tomografia computadorizada do tórax. 2016. 181 f. Tese (Doutorado em Engenharia de Teleinformática) - Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2016.<br>Submitted by Marlene Sousa (mmarlene@ufc.br) on 2016-09-13T17:02:51Z No. of bitstreams: 1 2016_Tese_tscavalcante.pdf: 12172675 bytes, checksum: a4f69559319313702095b478de3a549d (MD5)<br>Approved for entry into archive by Marlene Sousa (mmarlene@ufc.br) on 2016-09-13T17:09:11Z (GMT) No. of bitstreams: 1 2016_Tese_tscavalcante.pdf: 12172675 bytes, checksum: a4f69559319313702095b478de3a549d (MD5)<br>Made available in DSpace on 2016-09-13T17:09:11Z (GMT). No. of bitstreams: 1 2016_Tese_tscavalcante.pdf: 12172675 bytes, checksum: a4f69559319313702095b478de3a549d (MD5) Previous issue date: 2016-03-21<br>In several applications involving medical image analysis, the process of image segmentation, be it automatic or manual, is a present task. An accurate segmentation provides information for inspection of anatomical structures, to identify diseases and monitoring of its progress, and even for surgical planning and simulation. Thus, the role of image segmentation is essential in any medical image analysis system. Among the segmentation techniques in the literature, the active models technique is one of the most popular approaches of the last two decades and has been widely used in medical image segmentation, achieving considerable success. Active models that are applied on three-dimensional applications are called Active Surfaces Methods (ASM), which has been widely used in the segmentation of 3D objects, evolving under the influence of their energy to converge to the desired surface. So, knowing how essential surface extraction is to obtain an accurate segmentation, this thesis conducts a study on ASM, identifying its advantages and limitations, and proposes a new ASM to the segmentation of pulmonary lobes on CT images. The new ASM has as contributions internal forces for unstructured meshes, external energies based on LBP texture and Hessian matrix, and automatic initialization for each lobe. In order to validate this proposal, a comparative study of the performance of the internal forces in synthetic images, along with the comparison of the segmentation of lung lobes obtained by the proposed method with the segmentation of a gold standard carried out by an expert medical board will be conducted. The results show that the internal forces performs well, providing synthetic images segmentation with average distance of less than 1 voxel and adjustment measures of 0.95. The automatic initialization has also achieved significant results, with overall hit rate of 94%. Finally, the rates obtained for pulmonary lobe segmentation allows validation of the proposed method with average distance values of 1.93 mm and rates of size and form adjustment of 0.98 and 0.89, respectively. Thus, it is concluded that the obtained metric is sufficient to validate the lobar segmentation obtained in this thesis<br>Em diversas aplica¸c˜oes de an´alise de imagens m´edicas, o processo de segmenta¸c˜ao de imagens, seja autom´atico ou manual, ´e uma tarefa presente. Uma segmenta¸c˜ao correta fornece informa¸c˜oes para inspe¸c˜ao de estruturas anatˆomicas, para a identifica¸c˜ao de doen¸cas e acompanhamento de seu progresso e at´e mesmo para o planejamento cir´urgico e simula¸c˜ao. Logo, o papel da segmenta¸c˜ao de imagens ´e essencial em qualquer sistema de an´alise de imagens m´edicas. Dentre as t´ecnicas de segmenta¸c˜ao presentes na literatura, a t´ecnica de modelos ativos ´e uma das abordagens mais populares durante as duas ´ultimas d´ecadas e tem sido amplamente utilizada em segmenta¸c˜ao de imagens m´edicas, obtendo um sucesso consider´avel. Os modelos ativos que atuam em aplica¸c˜oes tridimensionais s˜ao denominados de M´etodos de Superf´ıcies Ativas (MSA), que por sua vez tˆem sido bastante utilizados na segmenta¸c˜ao de objetos 3D, evoluindo sob a influˆencia de suas energias at´e convergir para a superf´ıcie desejada. Neste sentido, tendo em vista que a extra¸c˜ao de superf´ıcie ´e essencial para a obten¸c˜ao de uma correta segmenta¸c˜ao, a presente tese realiza um estudo sobre MSA, identificando suas vantagens e limita¸c˜oes, e prop˜oe um novo MSA para a segmenta¸c˜ao de lobos pulmonares em imagens de TC. Esta novo MSA tem como contribui¸c˜oes for¸cas internas para malhas n˜ao estruturadas, energias externas baseadas em textura LBP e matriz Hessiana, al´em de inicializa¸c˜ao autom´atica por lobo pulmonar. Para validar a proposta ´e realizado um estudo comparativo da atua¸c˜ao das for¸cas internas em imagens sint´eticas, al´em da compara¸c˜ao da segmenta¸c˜ao dos lobos pulmonares obtida pelo m´etodo proposto com a segmenta¸c˜ao de um padr˜ao ouro realizado por uma junta m´edica especialista. Os resultados comprovam que as for¸cas internas apresentam bom desempenho na segmenta¸c˜ao de imagens sint´eticas com valores de distˆancia m´edia menores que 1 voxel e com medidas de ajuste maiores do que 0,95. A inicializa¸c˜ao autom´atica tamb´em obteve resultados relevantes, com taxa de acerto geral igual a 94%. Por fim, os valores obtidos para a segmenta¸c˜ao dos lobos pulmonares permitem validar o m´etodo proposto com valores de distˆancia m´edia igual a 1,93 mm e valores de ajuste de tamanho e de forma de 0,98 e 0,89, respectivamente. Neste contexto, conclui-se que as m´etricas apresentadas neste trabalho s˜ao suficientes para validar a segmenta¸c˜ao lobar obtida nesta tese
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Slavík, Roman. "Detektor obličejů pro platformu Android." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-236977.

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This master's thesis deals with face detection on mobile phones with Android OS. The introduction describes some algorithms used for pattern detection from image, as well as various techniques of features extracting. After that Android platform development specifics, including basic description of development tools, are described. Architecture of SIMD is introduced in next part of this work. After acquiring basic knowleage analysis and implementation of final app are descrited. Performance tests are conducted whose results are summarized in the conclusion.
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Kremličková, Lenka. "Hodnocení viability kardiomyocytů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316800.

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The aim of this diploma thesis is to get acquainted with the properties of image data and the principle of their capture. Literary research on methods of image segmentation in the area of cardiac tissue imaging and, last but not least, efforts to find methods for classification of dead cardiomyocytes and analysis of their viability. Dead cardiomyocytes were analyzed for their shape and similarity to the template created as a mean of dead cells. Another approach was the application of the method based on local binary characters and the computation of symptoms from a simple and associated histogram.
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Kadlček, Filip. "Implementace obrazových klasifikátorů v FPGA." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237091.

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The thesis deals with image classifiers and their implementation using FPGA technology. There are discussed weak and strong classifiers in the work. As an example of strong classifiers, the AdaBoost algorithm is described. In the case of weak classifiers, basic types of feature classifiers are shown, including Haar and Gabor wavelets. The rest of work is primarily focused on LBP, LRP and LR classifiers, which are well suitable for efficient implementation in FPGAs. With these classifiers is designed pseudo-parallel architecture. Process of classifications is divided on software and hardware parts. The thesis deals with hardware part of classifications. The designed classifier is very fast and produces results of classification every clock cycle.
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Romero, Mier y. Teran Andrés. "Real-time multi-target tracking : a study on color-texture covariance matrices and descriptor/operator switching." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-01002065.

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Visual recognition is the problem of learning visual categories from a limited set of samples and identifying new instances of those categories, the problem is often separated into two types: the specific case and the generic category case. In the specific case the objective is to identify instances of a particular object, place or person. Whereas in the generic category case we seek to recognize different instances that belong to the same conceptual class: cars, pedestrians, road signs and mugs. Specific object recognition works by matching and geometric verification. In contrast, generic object categorization often includes a statistical model of their appearance and/or shape.This thesis proposes a computer vision system for detecting and tracking multiple targets in videos. A preliminary work of this thesis consists on the adaptation of color according to lighting variations and relevance of the color. Then, literature shows a wide variety of tracking methods, which have both advantages and limitations, depending on the object to track and the context. Here, a deterministic method is developed to automatically adapt the tracking method to the context through the cooperation of two complementary techniques. A first proposition combines covariance matching for modeling characteristics texture-color information with optical flow (KLT) of a set of points uniformly distributed on the object . A second technique associates covariance and Mean-Shift. In both cases, the cooperation allows a good robustness of the tracking whatever the nature of the target, while reducing the global execution times .The second contribution is the definition of descriptors both discriminative and compact to be included in the target representation. To improve the ability of visual recognition of descriptors two approaches are proposed. The first is an adaptation operators (LBP to Local Binary Patterns ) for inclusion in the covariance matrices . This method is called ELBCM for Enhanced Local Binary Covariance Matrices . The second approach is based on the analysis of different spaces and color invariants to obtain a descriptor which is discriminating and robust to illumination changes.The third contribution addresses the problem of multi-target tracking, the difficulties of which are the matching ambiguities, the occlusions, the merging and division of trajectories.Finally to speed algorithms and provide a usable quick solution in embedded applications this thesis proposes a series of optimizations to accelerate the matching using covariance matrices. Data layout transformations, vectorizing the calculations (using SIMD instructions) and some loop transformations had made possible the real-time execution of the algorithm not only on Intel classic but also on embedded platforms (ARM Cortex A9 and Intel U9300).
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Guo, Y. (Yimo). "Image and video analysis by local descriptors and deformable image registration." Doctoral thesis, Oulun yliopisto, 2013. http://urn.fi/urn:isbn:9789526201412.

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Abstract Image description plays an important role in representing inherent properties of entities and scenes in static images. Within the last few decades, it has become a fundamental issue of many practical vision tasks, such as texture classification, face recognition, material categorization, and medical image processing. The study of static image analysis can also be extended to video analysis, such as dynamic texture recognition, classification and synthesis. This thesis contributes to the research and development of image and video analysis from two aspects. In the first part of this work, two image description methods are presented to provide discriminative representations for image classification. They are designed in unsupervised (i.e., class labels of texture images are not available) and supervised (i.e., class labels of texture images are available) manner, respectively. First, a supervised model is developed to learn discriminative local patterns, which formulates the image description as an integrated three-layered model to estimate an optimal pattern subset of interest by simultaneously considering the robustness, discriminative power and representation capability of features. Second, in the case that class labels of training images are unavailable, a linear configuration model is presented to describe microscopic image structures in an unsupervised manner, which is subsequently combined together with a local descriptor: local binary pattern (LBP). This description is theoretically verified to be rotation invariant and is able to provide a discriminative complement to the conventional LBPs. In the second part of the thesis, based on static image description and deformable image registration, video analysis is studied for the applications of dynamic texture description, synthesis and recognition. First, a dynamic texture synthesis model is proposed to create a continuous and infinitely varying stream of images given a finite input video, which stitches video clips in the time domain by selecting proper matching frames and organizing them into a logical order. Second, a method for the application of facial expression recognition, which formulates the dynamic facial expression recognition problem as the construction of longitudinal atlases and groupwise image registration problem, is proposed<br>Tiivistelmä Kuvan deskriptiolla on tärkeä rooli staattisissa kuvissa esiintyvien luontaisten kokonaisuuksien ja näkymien kuvaamisessa. Viime vuosikymmeninä se on tullut perustavaa laatua olevaksi ongelmaksi monissa käytännön konenäön tehtävissä, kuten tekstuurien luokittelu, kasvojen tunnistaminen, materiaalien luokittelu ja lääketieteellisten kuvien analysointi. Staattisen kuva-analyysin tutkimusala voidaan myös laajentaa videoanalyysiin, kuten dynaamisten tekstuurien tunnistukseen, luokitteluun ja synteesiin. Tämä väitöskirjatutkimus myötävaikuttaa kuva- ja videoanalyysin tutkimukseen ja kehittymiseen kahdesta näkökulmasta. Työn ensimmäisessä osassa esitetään kaksi kuvan deskriptiomenetelmää erottelukykyisten esitystapojen luomiseksi kuvien luokitteluun. Ne suunnitellaan ohjaamattomiksi (eli tekstuurikuvien luokkien leimoja ei ole käytettävissä) tai ohjatuiksi (eli luokkien leimat ovat saatavilla). Aluksi kehitetään ohjattu malli oppimaan erottelukykyisiä paikallisia kuvioita, mikä formuloi kuvan deskriptiomenetelmän integroituna kolmikerroksisena mallina - tavoitteena estimoida optimaalinen kiinnostavien kuvioiden alijoukko ottamalla samanaikaisesti huomioon piirteiden robustisuus, erottelukyky ja esityskapasiteetti. Seuraavaksi, sellaisia tapauksia varten, joissa luokkaleimoja ei ole saatavilla, esitetään työssä lineaarinen konfiguraatiomalli kuvaamaan kuvan mikroskooppisia rakenteita ohjaamattomalla tavalla. Tätä käytetään sitten yhdessä paikallisen kuvaajan, eli local binary pattern (LBP) –operaattorin kanssa. Teoreettisella tarkastelulla osoitetaan kehitetyn kuvaajan olevan rotaatioinvariantti ja kykenevän tuottamaan erottelukykyistä, täydentävää informaatiota perinteiselle LBP-menetelmälle. Työn toisessa osassa tutkitaan videoanalyysiä, perustuen staattisen kuvan deskriptioon ja deformoituvaan kuvien rekisteröintiin – sovellusaloina dynaamisten tekstuurien kuvaaminen, synteesi ja tunnistaminen. Aluksi ehdotetaan sellainen malli dynaamisten tekstuurien synteesiin, joka luo jatkuvan ja äärettömän kuvien virran annetusta äärellisen mittaisesta videosta. Menetelmä liittää yhteen videon pätkiä aika-avaruudessa valitsemalla keskenään yhteensopivia kuvakehyksiä videosta ja järjestämällä ne loogiseen järjestykseen. Seuraavaksi työssä esitetään sellainen uusi menetelmä kasvojen ilmeiden tunnistukseen, joka formuloi dynaamisen kasvojen ilmeiden tunnistusongelman pitkittäissuuntaisten kartastojen rakentamisen ja ryhmäkohtaisen kuvien rekisteröinnin ongelmana
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Wang, Benjamin. "Lip Detection and Adaptive Tracking." DigitalCommons@CalPoly, 2017. https://digitalcommons.calpoly.edu/theses/1695.

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Performance of automatic speech recognition (ASR) systems utilizing only acoustic information degrades significantly in noisy environments such as a car cabins. Incorporating audio and visual information together can improve performance in these situations. This work proposes a lip detection and tracking algorithm to serve as a visual front end to an audio-visual automatic speech recognition (AVASR) system. Several color spaces are examined that are effective for segmenting lips from skin pixels. These color components and several features are used to characterize lips and to train cascaded lip detectors. Pre- and post-processing techniques are employed to maximize detector accuracy. The trained lip detector is incorporated into an adaptive mean-shift tracking algorithm for tracking lips in a car cabin environment. The resulting detector achieves 96.8% accuracy, and the tracker is shown to recover and adapt in scenarios where mean-shift alone fails.
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Huang, X. (Xiaohua). "Methods for facial expression recognition with applications in challenging situations." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526206561.

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Abstract In recent years, facial expression recognition has become a useful scheme for computers to affectively understand the emotional state of human beings. Facial representation and facial expression recognition under unconstrained environments have been two critical issues for facial expression recognition systems. This thesis contributes to the research and development of facial expression recognition systems from two aspects: first, feature extraction for facial expression recognition, and second, applications to challenging conditions. Spatial and temporal feature extraction methods are introduced to provide effective and discriminative features for facial expression recognition. The thesis begins with a spatial feature extraction method. This descriptor exploits magnitude while it improves local quantized pattern using improved vector quantization. It also makes the statistical patterns domain-adaptive and compact. Then, the thesis discusses two spatiotemporal feature extraction methods. The first method uses monogenic signal analysis as a preprocessing stage and extracts spatiotemporal features using local binary pattern. The second method extracts sparse spatiotemporal features using sparse cuboids and spatiotemporal local binary pattern. Both methods increase the discriminative capability of local binary pattern in the temporal domain. Based on feature extraction methods, three practical conditions, including illumination variations, facial occlusion and pose changes, are studied for the applications of facial expression recognition. First, with near-infrared imaging technique, a discriminative component-based single feature descriptor is proposed to achieve a high degree of robustness and stability to illumination variations. Second, occlusion detection is proposed to dynamically detect the occluded face regions. A novel system is further designed for handling effectively facial occlusion. Lastly, multi-view discriminative neighbor preserving embedding is developed to deal with pose change, which formulates multi-view facial expression recognition as a generalized eigenvalue problem. Experimental results on publicly available databases show that the effectiveness of the proposed approaches for the applications of facial expression recognition<br>Tiivistelmä Kasvonilmeiden tunnistamisesta on viime vuosina tullut tietokoneille hyödyllinen tapa ymmärtää affektiivisesti ihmisen tunnetilaa. Kasvojen esittäminen ja kasvonilmeiden tunnistaminen rajoittamattomissa ympäristöissä ovat olleet kaksi kriittistä ongelmaa kasvonilmeitä tunnistavien järjestelmien kannalta. Tämä väitöskirjatutkimus myötävaikuttaa kasvonilmeitä tunnistavien järjestelmien tutkimukseen ja kehittymiseen kahdesta näkökulmasta: piirteiden irrottamisesta kasvonilmeiden tunnistamista varten ja kasvonilmeiden tunnistamisesta haastavissa olosuhteissa. Työssä esitellään spatiaalisia ja temporaalisia piirteenirrotusmenetelmiä, jotka tuottavat tehokkaita ja erottelukykyisiä piirteitä kasvonilmeiden tunnistamiseen. Ensimmäisenä työssä esitellään spatiaalinen piirteenirrotusmenetelmä, joka parantaa paikallisia kvantisoituja piirteitä käyttämällä parannettua vektorikvantisointia. Menetelmä tekee myös tilastollisista malleista monikäyttöisiä ja tiiviitä. Seuraavaksi työssä esitellään kaksi spatiotemporaalista piirteenirrotusmenetelmää. Ensimmäinen näistä käyttää esikäsittelynä monogeenistä signaalianalyysiä ja irrottaa spatiotemporaaliset piirteet paikallisia binäärikuvioita käyttäen. Toinen menetelmä irrottaa harvoja spatiotemporaalisia piirteitä käyttäen harvoja kuusitahokkaita ja spatiotemporaalisia paikallisia binäärikuvioita. Molemmat menetelmät parantavat paikallisten binärikuvioiden erottelukykyä ajallisessa ulottuvuudessa. Piirteenirrotusmenetelmien pohjalta työssä tutkitaan kasvonilmeiden tunnistusta kolmessa käytännön olosuhteessa, joissa esiintyy vaihtelua valaistuksessa, okkluusiossa ja pään asennossa. Ensiksi ehdotetaan lähi-infrapuna kuvantamista hyödyntävää diskriminatiivistä komponenttipohjaista yhden piirteen kuvausta, jolla saavutetaan korkea suoritusvarmuus valaistuksen vaihtelun suhteen. Toiseksi ehdotetaan menetelmä okkluusion havainnointiin, jolla dynaamisesti havaitaan peittyneet kasvon alueet. Uudenlainen menetelmä on kehitetty käsittelemään kasvojen okkluusio tehokkaasti. Viimeiseksi työssä on kehitetty moninäkymäinen diskriminatiivisen naapuruston säilyttävään upottamiseen pohjautuva menetelmä käsittelemään pään asennon vaihtelut. Menetelmä kuvaa moninäkymäisen kasvonilmeiden tunnistamisen yleistettynä ominaisarvohajotelmana. Kokeelliset tulokset julkisilla tietokannoilla osoittavat tässä työssä ehdotetut menetelmät suorituskykyisiksi kasvonilmeiden tunnistamisessa
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Books on the topic "Local binary patterns (LBP)"

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Abdenour, Hadid, Zhao Guoying, Ahonen Timo, and SpringerLink (Online service), eds. Computer Vision Using Local Binary Patterns. Springer-Verlag London Limited, 2011.

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Pietikäinen, Matti, Abdenour Hadid, Guoying Zhao, and Timo Ahonen. Computer Vision Using Local Binary Patterns. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-748-8.

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Brahnam, Sheryl, Lakhmi C. Jain, Loris Nanni, and Alessandra Lumini, eds. Local Binary Patterns: New Variants and Applications. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-39289-4.

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Book chapters on the topic "Local binary patterns (LBP)"

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Bianconi, Francesco, and Antonio Fernández. "A Unifying Framework for LBP and Related Methods." In Local Binary Patterns: New Variants and Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39289-4_2.

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Chan, Chi ho, Josef Kittler, and Norman Poh. "State-of-the-Art LBP Descriptor for Face Recognition." In Local Binary Patterns: New Variants and Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39289-4_9.

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Ruiz-del-Solar, Javier, Rodrigo Verschae, Gabriel Hermosilla, and Mauricio Correa. "Thermal Face Recognition in Unconstrained Environments Using Histograms of LBP Features." In Local Binary Patterns: New Variants and Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39289-4_10.

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Khan, Maleika Heenaye Mamode. "Representation of Dorsal Hand Vein Pattern Using Local Binary Patterns (LBP)." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18681-8_26.

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Zhang, Guangcheng, Xiangsheng Huang, Stan Z. Li, Yangsheng Wang, and Xihong Wu. "Boosting Local Binary Pattern (LBP)-Based Face Recognition." In Advances in Biometric Person Authentication. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30548-4_21.

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Hannad, Yaâcoub, Imran Siddiqi, and Mohamed El Youssfi El Kettani. "Arabic Writer Identification Using Local Binary Patterns (LBP) of Handwritten Fragments." In Pattern Recognition and Image Analysis. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19390-8_27.

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Truong, Hung Phuoc, and Yong-Guk Kim. "Enhanced Line Local Binary Patterns (EL-LBP): An Efficient Image Representation for Face Recognition." In Advanced Concepts for Intelligent Vision Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01449-0_24.

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Gaikwad, Ashok, Vivek Mahale, Mouad M. H. Ali, and Pravin L. Yannawar. "Detection and Analysis of Video Inconsistency Based on Local Binary Pattern (LBP)." In Communications in Computer and Information Science. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9181-1_9.

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Ganguly, Suranjan, Debotosh Bhattacharjee, and Mita Nasipuri. "The SI-LBP: A New Framework for Obtaining 3D Local Binary Patterns from Shape-Index." In Lecture Notes in Electrical Engineering. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0557-2_35.

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Azam, Kazi Sultana Farhana, Farhin Farhad Riya, and Shah Tuhin Ahmed. "Leaf Detection Using Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and Classifying with SVM Utilizing Claim Dataset." In Intelligent Data Communication Technologies and Internet of Things. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9509-7_27.

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Conference papers on the topic "Local binary patterns (LBP)"

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Bayram, Erkan, and Vasif Nabiyev. "Classification of Camouflage Images Using Local Binary Patterns (LBP)." In 2021 29th Signal Processing and Communications Applications Conference (SIU). IEEE, 2021. http://dx.doi.org/10.1109/siu53274.2021.9478040.

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Werghi, Naoufel, Stefano Berretti, Alberto Del Bimbo, and Pietro Pala. "The Mesh-LBP: Computing Local Binary Patterns on Discrete Manifolds." In 2013 IEEE International Conference on Computer Vision Workshops (ICCVW). IEEE, 2013. http://dx.doi.org/10.1109/iccvw.2013.78.

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Mehta, Rakesh, Jirui Yuan, and Karen Egiazarian. "Local polynomial approximation-local binary pattern (LPA-LBP) based face classification." In IS&T/SPIE Electronic Imaging, edited by David Akopian, Reiner Creutzburg, Cees G. M. Snoek, Nicu Sebe, and Lyndon Kennedy. SPIE, 2011. http://dx.doi.org/10.1117/12.878796.

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Singh, Aditya, Ramesh K. Sunkaria, and Anterpreet Kaur Bedi. "Variants of Local Binary Pattern: A Review." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.21.

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In everyday life, image data, such as information, processing and computer vision, play a significant role in many applications; such as image classification, image segmentation and image retrieval. A preferred attributes that has been applied in a lots of picture applications is texture. This was forming an image using an array of pixels. Texture played a significant part in image segmentation and image detection and retrieval. In addition ranked the classifications of the texture then local binary pattern are coming. The LBP method seemed to work very effectively in real time. In the LBP method, comparing the values of the central pixels with the values of the neighbouring pixels and to attribute the binary values on these values. In this paper providing an overview for the local binary model and their benefits.
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Y.B., Ravi Kumar, and Ravi Kumar C.N. "Local binary pattern: An improved LBP to extract nonuniform LBP patterns with Gabor filter to increase the rate of face similarity." In 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP). IEEE, 2016. http://dx.doi.org/10.1109/ccip.2016.7802878.

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Touahri, Radia, Nabiha AzizI, Nacer Eddine Hammami, Monther Aldwairi, and Farid Benaida. "Automated Breast Tumor Diagnosis Using Local Binary Patterns (LBP) Based on Deep Learning Classification." In 2019 International Conference on Computer and Information Sciences (ICCIS). IEEE, 2019. http://dx.doi.org/10.1109/iccisci.2019.8716428.

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Pinjari, Shakil A., and Nitin N. Patil. "A modified approach of fragile watermarking using Local Binary Pattern (LBP)." In 2015 International Conference on Pervasive Computing (ICPC). IEEE, 2015. http://dx.doi.org/10.1109/pervasive.2015.7087100.

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Pinjari, Shakil A., and Nitin N. Patil. "A pixel based fragile watermarking technique using LBP (Local Binary Pattern)." In 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC). IEEE, 2016. http://dx.doi.org/10.1109/icgtspicc.2016.7955296.

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Zhilali'l, Abdurrahman Fi, Muhammad Nasrun, and Casi Setianingsih. "Face Recognition Using Local Binary Pattern (LBP) and Local Enhancement (LE) Methods At Night Period." In Proceedings of the 2018 International Conference on Industrial Enterprise and System Engineering (IcoIESE 2018). Atlantis Press, 2019. http://dx.doi.org/10.2991/icoiese-18.2019.19.

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Fronitasari, Dini, and Dadang Gunawan. "Palm vein recognition by using modified of local binary pattern (LBP) for extraction feature." In 2017 15th International Conference on Quality in Research (QiR): International Symposium on Electrical and Computer Engineering. IEEE, 2017. http://dx.doi.org/10.1109/qir.2017.8168444.

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