Relevant bibliographies by topics / Local extensive binary pattern

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Academic literature on the topic 'Local extensive binary pattern'

Author: Grafiati
Published: 4 June 2025
Last updated: 6 July 2025

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Journal articles on the topic "Local extensive binary pattern"

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Xu, Xiaochun, Bin Li, and Q. M. Jonathan Wu. "A Completed Multi-Scale Local Statistics Pattern for Texture Classification." Image Analysis and Stereology 43, no. 3 (2024): 277–93. http://dx.doi.org/10.5566/ias.3037.

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Binary pattern methods play a vital role in extracting texture features. However, most of existing methods struggle to capture comprehensive and discriminative texture information. This paper aims to propose a novel multi-statistic binary pattern to extract rotation invariance statistic features for texture classification. First, this paper encodes the center pixel, mean, variance and range of local neighborhood by corresponding multi-scale threshold, and proposes the local center pattern, local mean pattern, local variance pattern and local range pattern. Then, based on the compact multi-pattern encoding strategy, the four sub-patterns are jointly encoded in a 4-bit binary pattern, named as multi-scale local statistics pattern. Finally, for comprehensive texture representation, the multi-scale local statistics pattern is jointly combined with local sign pattern and local magnitude pattern to generate a completed multi-scale local statistics pattern for texture classification. Extensive experiments conducted on three representative databases demonstrate that the proposed completed multi-scale local statistics pattern achieves competitive classification performance compared with other state-of-the-art approaches.
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Xing, Ning, and Lan Xian Gui. "Preprocessing for Local Bianry Pattern Based Face Recognition." Advanced Materials Research 760-762 (September 2013): 1434–37. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1434.

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This paper studies the pre-processing methods on Local Binary Pattern (LBP) for face recognition. Three methods are well investigated, such as Gaussian smoothing, histogram equalization and Sobel operator. The extensive experiments on FERET database show that the best recognition rate increase is achieved by combination of histogram equalization, Gaussian smoothing, and Sobel with LBP. The conclusion achieved in the paper is useful for real face recognition system.
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SHARIATMADAR, ZAHRA S., and KARIM FAEZ. "FINGER-KNUCKLE-PRINT RECOGNITION VIA ENCODING LOCAL-BINARY-PATTERN." Journal of Circuits, Systems and Computers 22, no. 06 (2013): 1350050. http://dx.doi.org/10.1142/s0218126613500503.

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Biometrics-based authentication is an effective approach which is used for automatically recognizing a person's identity. Recently, it has been found that the finger-knuckle-print (FKP), which refers to the texture pattern produced by the finger knuckle bending, is highly unique and can be used as a biometric identifier. In this paper, we present an effective FKP recognition scheme for personal identification and identity verification. This method is a new encoding scheme based on local binary pattern (LBP). Each image first is decomposed in several blocks, each block is convolved with a bank of Gabor filters and then, the LBPs histograms are extracted from the convolved images. Finally, a BioHashing approach is applied on the obtained fixed-length feature vectors. Extensive experiments conducted over the Poly-U FKP database demonstrated the efficiency and effectiveness of our proposed method.
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Hardeep, Singh, and Gagandeep. "Local Binary Patterns and Its Application to Facial Analysis." International Journal of Engineering Research and Reviews 10, no. 3 (2022): 11–20. https://doi.org/10.5281/zenodo.7014367.

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<strong>Abstract:</strong> This paper focuses on the Local Binary Patterns and its application to facial analysis. LBP is a non-parametric descriptor and used to extract, analyze, recognize and classify the different modality images. It summarizes the local patterns of image characteristics efficiently. In image processing, we have to extract features from a set of different texture or facial images, the Local Binary Pattern is a descriptor to extract, analyze, recognize and classify that data. There are detection, face representation, face detection and face recognition processes needed to analyze a face, from which the data obtained of different faces are comparatively checked according a specific exact facial image of person. This technique is applied at biometric machine and at other purposes to recognize an authentic face image. This paper facial analysis process and different local binary pattern techniques applied for facial detection and recognition are extensively reviewed. <strong>Keywords:</strong> Local Binary Pattern, LBP, Face Detection, Recognition. <strong>Title:</strong> Local Binary Patterns and Its Application to Facial Analysis <strong>Author:</strong> Hardeep Singh, Gagandeep <strong>International Journal of Engineering Research and Reviews</strong> <strong>ISSN 2348-697X (Online)</strong> <strong>Vol. 10, Issue 3, July 2022 - September 2022</strong> <strong>Page No: 11-20</strong> <strong>Research Publish Journals</strong> <strong>Website: www.researchpublish.com</strong> <strong>Published Date: 22-August-2022</strong> <strong>DOI: </strong><strong>https://doi.org/10.5281/zenodo.7014367</strong> <strong>Paper Download Link (Source)</strong> <strong>https://www.researchpublish.com/papers/local-binary-patterns-and-its-application-to-facial-analysis</strong>
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Zeng, Hui, Xiuqing Wang, and Yu Gu. "Center Symmetric Local Multilevel Pattern Based Descriptor and Its Application in Image Matching." International Journal of Optics 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/1584514.

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This paper presents an effective local image region description method, called CS-LMP (Center Symmetric Local Multilevel Pattern) descriptor, and its application in image matching. The CS-LMP operator has no exponential computations, so the CS-LMP descriptor can encode the differences of the local intensity values using multiply quantization levels without increasing the dimension of the descriptor. Compared with the binary/ternary pattern based descriptors, the CS-LMP descriptor has better descriptive ability and computational efficiency. Extensive image matching experimental results testified the effectiveness of the proposed CS-LMP descriptor compared with other existing state-of-the-art descriptors.
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Mounika, Kavadapu, and M. Devendra. "Segementation of Blur Images Using Local Binary Pattern Technique." International Journal of Engineering & Technology 7, no. 4.7 (2018): 315. http://dx.doi.org/10.14419/ijet.v7i4.7.20569.

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Defocus blur is to a great degree regular in images caught utilizing optical imaging frameworks. It might be bothersome, however may likewise be a deliberate imaginative impact, in this manner it can either upgrade or hinder our visual view of the image scene. For assignments, for example, image restoration and object recognition, one should need to portion an in part blurred image into blurred and non-blurred areas. In this paper, we propose sharpness metric in light of local binary patterns and a hearty segmentation calculation to isolate all through focus image districts. The proposed sharpness metric adventures the perception that most local image fixes in blurry areas have altogether less of certain local binary patterns contrasted and those in sharp districts. Utilizing this metric together with image tangling and multiscale surmising, we got excellent sharpness maps. Tests on several halfway blurred images were utilized to assess our blur segmentation calculation and six comparator techniques. The outcomes demonstrate that our calculation accomplishes similar segmentation comes about with the best in class and have enormous speed advantage over the others. in Extension we are using LLBP (Line Local Binary Pattern ) for getting better output in blur images.
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Lian, Yaxin, and Yongsheng Dong. "Texture Classification Method Based on Local Enhancement and Non-local Median Patterns." Academic Journal of Science and Technology 11, no. 1 (2024): 88–96. http://dx.doi.org/10.54097/6xvc9784.

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Local binary pattern (LBP) serves as a highly powerful method for texture classification. LBP and its variant methods are extensively applied across various domains of image processing.In increasingly complex imaging environments, LBP faces two issues: (1) Loss of detail information during feature extraction. (2) Sensitivity to noise. To mitigate these issues, this paper proposes a method called Local Enhancement and Non-local Median Pattern (LENMP). It consists of two operators: Local Adaptive Contrast Enhancement Pattern (LEP) and Non-local Median Binary Pattern (NMBP). The LEP operator captures the sign and magnitude details of local image characteristics, while the NMBP operator captures the global information of image features. First, the image is processed using the threshold ACE (thACE) algorithm to enhance the contrast of high-frequency information in the image, extracting image contour edge information. Then, median extraction is performed separately on the two operators to capture larger spatial texture detail information. Finally, comparative experiments are conducted on multiple datasets (Outex, CUReT, Brodatz, KTH-TIPS, and Kylberg) with representative texture classification methods. The results indicate that our proposed LENMP texture classification method exhibits good classification performance and remains competitive compared to the latest descriptors.
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Rabidas, Rinku, Abhishek Midya, Jayasree Chakraborty, and Wasim Arif. "Multi-Resolution Analysis of Edge-Texture Features for Mammographic Mass Classification." Journal of Circuits, Systems and Computers 29, no. 10 (2019): 2050156. http://dx.doi.org/10.1142/s021812662050156x.

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In this paper, multi-resolution analysis of two edge-texture based descriptors, Discriminative Robust Local Binary Pattern (DRlbp) and Discriminative Robust Local Ternary Pattern (DRltp), are proposed for the determination of mammographic masses as benign or malignant. As an extension of Local Binary Pattern (LBP) and Local Ternary Pattern (LTP), DRlbp and LTP-based features overcome the drawbacks of these features preserving the edge information along with texture. With the hypothesis that multi-resolution analysis of these features for different regions related to mammaographic masses with wavelet transform will capture more discriminating patterns and thus can help in characterizing masses. In order to evaluate the efficiency of the proposed approach, several experiments are carried out using the mini-MIAS database where a 5-fold cross validation technique is incorporated with Support Vector Machine (SVM) on the optimal set of features obtained via stepwise logistic regression method. An area under the receiver operating characteristic (ROC) curve ([Formula: see text] value) of 0.96 is achieved with DRlbp attributes as the best performance. The superiority of the proposed scheme is established by comparing the obtained results with recently developed other competing schemes.
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Chen, Shen, Taiping Yao, Yang Chen, Shouhong Ding, Jilin Li, and Rongrong Ji. "Local Relation Learning for Face Forgery Detection." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1081–88. http://dx.doi.org/10.1609/aaai.v35i2.16193.

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With the rapid development of facial manipulation techniques, face forgery has received considerable attention in digital media forensics due to security concerns. Most existing methods formulate face forgery detection as a classification problem and utilize binary labels or manipulated region masks as supervision. However, without considering the correlation between local regions, these global supervisions are insufficient to learn a generalized feature and prone to overfitting. To address this issue, we propose a novel perspective of face forgery detection via local relation learning. Specifically, we propose a Multi-scale Patch Similarity Module (MPSM), which measures the similarity between features of local regions and forms a robust and generalized similarity pattern. Moreover, we propose an RGB-Frequency Attention Module (RFAM) to fuse information in both RGB and frequency domains for more comprehensive local feature representation, which further improves the reliability of the similarity pattern. Extensive experiments show that the proposed method consistently outperforms the state-of-the-arts on widely-used benchmarks. Furthermore, detailed visualization shows the robustness and interpretability of our method.
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Khaleefah, Shihab Hamad, Salama A. Mostafa, Aida Mustapha, Noor Azah Samsudin, Mohammad Faidzul Nasrudin, and Abdullah Baz. "A survey on local binary pattern and gabor filter as texture descriptors of smart profiling systems." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (2020): 1379–87. https://doi.org/10.11591/ijeecs.v20.i3.pp1379-1387.

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With the dramatic expansion of image information nowadays, image processing and computer visions are playing a significant role in terms of several applications such as image classification, image segmentation, pattern recognition, and image retrieval. One of the important features that have been used in many image applications is texture. The texture is the characteristic of a set of pixels that formed the image. Therefore, analyzing such texture would have a significant impact on segmenting the image or detecting important portions of such image. This paper aims to overview the feature extraction and description techniques depicted in the literature to characterize regions for images. In particular, two of popular descriptors will be examined including Local Binary Pattern (LBP) and Gabor Filter. The key characteristic behind such investigation lies in how the features of an image would contribute toward the process of recognition and image classification. In this regard, an extensive discussion is provided on both LBP and Gabor descriptors along with the efforts that have been intended to combine them. The reason behind investigating these descriptors is that they are considered the most common local and global descriptors used in the literature. The overall aim of this survey is to show current trends on using, modifying and adapting these descriptors in the domain of image processing.
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Dissertations / Theses on the topic "Local extensive binary pattern"

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Chan, Chi Ho. "Multi-scale local Binary Pattern Histogram for Face Recognition." Thesis, University of Surrey, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.493135.

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Recently, the research in face recognition has focused on developing a face representation that is capable of capturing the relevant information in a manner which is invariant to facial expression and illumination. Motivated by a simple but powerful texture descriptor, called Local Binary Pattern (LBP), our proposed system extends this descriptor to evoke multiresolution and multispectral analysis for face recognition. The first descriptor, namely Multi-scale Local Binary Pattern Histogram (MLBPH), provides a robust system which is relatively insensitive to localisation errors because it benefits from the multiresolution information captured from the regional histogram.
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Lindahl, Tobias. "Study of Local Binary Patterns." Thesis, Linköping University, Department of Science and Technology, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-9415.

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<p>This Masters thesis studies the concept of local binary patterns, which describe the neighbourhood of a pixel in a digital image by binary derivatives. The operator is often used in texture analysis and has been successfully used in facial recognition.</p><p>This thesis suggests two methods based on some basic ideas of Björn Kruse and studies of literature on the subject. The first suggested method presented is an algorithm which reproduces images from their local binary patterns by a kind of integration of the binary derivatives. This method is a way to prove the preservation of information. The second suggested method is a technique of interpolating missing pixels in a single CCD camera based on local binary patterns and machine learning. The algorithm has shown some very promising results even though in its current form it does not keep up with the best algorithms of today.</p>
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Mäenpää, T. (Topi). "The local binary pattern approach to texture analysis — extensions and applications." Doctoral thesis, University of Oulu, 2003. http://urn.fi/urn:isbn:9514270762.

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Abstract This thesis presents extensions to the local binary pattern (LBP) texture analysis operator. The operator is defined as a gray-scale invariant texture measure, derived from a general definition of texture in a local neighborhood. It is made invariant against the rotation of the image domain, and supplemented with a rotation invariant measure of local contrast. The LBP is proposed as a unifying texture model that describes the formation of a texture with micro-textons and their statistical placement rules. The basic LBP is extended to facilitate the analysis of textures with multiple scales by combining neighborhoods with different sizes. The possible instability in sparse sampling is addressed with Gaussian low-pass filtering, which seems to be somewhat helpful. Cellular automata are used as texture features, presumably for the first time ever. With a straightforward inversion algorithm, arbitrarily large binary neighborhoods are encoded with an eight-bit cellular automaton rule, resulting in a very compact multi-scale texture descriptor. The performance of the new operator is shown in an experiment involving textures with multiple spatial scales. An opponent-color version of the LBP is introduced and applied to color textures. Good results are obtained in static illumination conditions. An empirical study with different color and texture measures however shows that color and texture should be treated separately. A number of different applications of the LBP operator are presented, emphasizing real-time issues. A very fast software implementation of the operator is introduced, and different ways of speeding up classification are evaluated. The operator is successfully applied to industrial visual inspection applications and to image retrieval.
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RIBEIRO, M. V. L. "Proposta de Local Binary Pattern Coerente e Incoerente na Categorização de Cenas." Universidade Federal do Espírito Santo, 2017. http://repositorio.ufes.br/handle/10/9682.

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Made available in DSpace on 2018-08-02T00:01:14Z (GMT). No. of bitstreams: 1 tese_9974_Dissertação de Mestrado - Matheus Ribeiro.pdf: 13133495 bytes, checksum: c89441388ef04fc065e4bfc94cdc216f (MD5) Previous issue date: 2017-10-11<br>Este trabalho propõe um novo descritor visual de cenas a partir da técnica Local Binary Pattern (LBP) e explorando a informação espacial utilizando o algoritmo Color Coherent Vector (CCV). O LBP se caracteriza por ser uma técnica não linear e não paramétrica, dispensando conceitos intermediários no processo de descrição da imagem, tornando uma alternativa para usuários leigos com pouco conhecimento na área. Já a representação CCV mostrou ser uma técnica que busca mitigar o problema da falta de informação espacial pelos histogramas, expressando a imagem em pixeis coerentes e pixeis incoerentes sem que aumente a dimensionalidade dos dados. Nesse sentido, uma primeira abordagem foi a proposta das técnicas LBP Incoerente e LBP Coerente na classificação de cenas. Resultados preliminares, empregando-se K-NN como classificador, demonstraram que o LBP Incoerente apresenta um bom compromisso entre acurácia e dimensão de representação dos dados. Em seguida, no intuito de se incluir o conceito de contexto, para mitigar o problema da localidade do LBP, foi proposto o Contextual Modified Local Binary Pattern Incoerente (CMLBP Incoerente), que modela a distribuição das estruturas locais através do LBP, adicionando informação contextual, inspirado no algoritmo Contextual Modified Census Transform (CMCT). Entre outras características, o CMLBP Incoerente demonstrou capacidade em descartar regiões homogêneas, representadas pelos pixeis coerentes através do algoritmo CCV. Em experimentos realizados com bancos de dados consagrados na literatura, o CMLBP apresentou resultados melhores que as técnicas originais que não descartam os pixeis coerentes, em quase todas as situações. Para cenas com muitos detalhes e informações os resultados foram satisfatórios e com um maior destaque, superando técnicas conhecidas na literatura. Os resultados obtidos foram encorajadores para a busca de um descritor com boa capacidade discriminante e baixa dimensionalidade na representação de imagens.
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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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Eriksson, Josefine, and Lindelöf Anna. "Measuring Student Attention with Face Detection: : Viola-Jones versus Multi-Block Local Binary Pattern using OpenCV." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166416.

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The purpose of this study is to discuss and attempt to approach an answer to the question of how face detection could be used to measure attention in a lecture hall.The conclusion might help further studies in using face detection to provide teachers with tools which can be used to improve learning during lectures. Face detection in real time applications became possible in 2001 when Viola and Jones presented a new method several times faster than any previous attempt. In 2007 Liao et al. presented a method using multi-block local binary patterns (MB-LBP) for the purpose of overcoming the simplicity and limitations of the Viola-Jones method. Computer vision libraries such as OpenCV make it easy to implement such algorithms. It currently supports both the Viola-Jones algorithm and the MB-LBP algorithm. This study compared these two face detection methods to see how they perform in terms of sensitivity and precision and attempted to identified limitations of both methods when used to detect attention in a simulated lecture environment. The study was conducted using boosted algorithms and functionality provided by OpenCV. The input data consisted of a recorded simulated lecture with 6 subjects performing different poses, labeled either attention or no attention, during certain periods of time, each pose recognized from a previously recorded actual lecture as a commonly occurring pose. The most significant difference of performance identified in the study was that the MB-LBP method performed face detection in an image three times faster than for Viola-Jones which confirmed previous reported results. Both methods generated high sensitivity values for all poses, but low precision values for two of the poses.The ability of both methods to detect downward tilted faces contributed to a high number of false positives returned when subjects performed the two poses of subjects taking notes or subjects performing activities labeled as no attention. Due to the low precision values caused by this, both methods were not considered to measure attention effectively. It is therefore suggested to instead train a MB-LBP-based method for the specific task of measuring attention in a lecture hall by training it to reject downward-tilted faces and to accept only instances conforming to the chosen definition of attention.
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Ylioinas, J. (Juha). "Towards optimal local binary patterns in texture and face description." Doctoral thesis, Oulun yliopisto, 2016. http://urn.fi/urn:isbn:9789526214498.

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Abstract Local binary patterns (LBP) are among the most popular image description methods and have been successfully applied in a diverse set of computer vision problems, covering texture classification, material categorization, face recognition, and image segmentation, to name only a few. The popularity of the LBP methodology can be verified by inspecting the number of existing studies about its different variations and extensions. The number of those studies is vast. Currently, the methodology has been acknowledged as one of the milestones in face recognition research. The starting point of this research is to gain more understanding of which principles the original LBP descriptor is based on. After gaining some degree of insight, yet another try is made to improve some steps of the LBP pipeline, consisted of image pre-processing, pattern sampling, pattern encoding, binning, and further histogram post-processing. The main contribution of this thesis is a bunch of novel LBP extensions that partly try to unify some of the existing derivatives and extensions. The basis for the design of the new additional LBP methodology is to maximise data-driven premises, at the same time minimizing the need for tuning by hand. Prior to local binary pattern extraction, the thesis presents an image upsampling step dubbed as image pre-interpolation. As a natural consequence of upsampling, a greater number of patterns can be extracted and binned to a histogram improving the representational performance of the final descriptor. To improve the following two steps of the LBP pipeline, namely pattern sampling and encoding, three different learning-based methods are introduced. Finally, a unifying model is presented for the last step of the LBP pipeline, namely for local binary pattern histogram post-processing. As a special case of this, a novel histogram smoothing scheme is proposed, which shares the motivation and the effects with the image pre-interpolation for the most of its part. Deriving descriptors for such face recognition problems as face verification or age estimation has been and continues to be among the most popular domains where LBP has ever been applied. This study is not an exception in that regard as the main investigations and conclusions here are made on the basis of how the proposed LBP variations perform especially in the problems of face recognition. The experimental part of the study demonstrates that the proposed methods, experimentally validated using publicly available texture and face datasets, yield results comparable to the best performing LBP variants found in the literature, reported with the corresponding benchmarks<br>Tiivistelmä Paikalliset binäärikuviot kuuluvat suosituimpiin menetelmiin kuville suoritettavassa piirteenirrotuksessa. Menetelmää on sovellettu moniin konenäön ongelmiin, kuten tekstuurien luokittelu, materiaalien luokittelu, kasvojen tunnistus ja kuvien segmentointi. Menetelmän suosiota kuvastaa hyvin siitä kehitettyjen erilaisten johdannaisten suuri lukumäärä ja se, että nykyään kyseinen menetelmien perhe on tunnustettu yhdeksi virstanpylvääksi kasvojentunnistuksen tutkimusalueella. Tämän tutkimuksen lähtökohtana on ymmärtää periaatteita, joihin tehokkaimpien paikallisten binäärikuvioiden suorituskyky perustuu. Tämän jälkeen tavoitteena on kehittää parannuksia menetelmän eri askelille, joita ovat kuvan esikäsittely, binäärikuvioiden näytteistys ja enkoodaus, sekä histogrammin koostaminen ja jälkikäsittely. Esiteltävien uusien menetelmien lähtökohtana on hyödyntää mahdollisimman paljon kohdesovelluksesta saatavaa tietoa automaattisesti. Ensimmäisenä menetelmänä esitellään kuvan ylösnäytteistykseen perustuva paikallisten binäärikuvioiden johdannainen. Ylösnäytteistyksen luonnollisena seurauksena saadaan näytteistettyä enemmän binäärikuvioita, jotka histogrammiin koottuna tekevät piirrevektorista alkuperäistä erottelevamman. Seuraavaksi esitellään kolme oppimiseen perustuvaa menetelmää paikallisten binäärikuvioiden laskemiseksi ja niiden enkoodaukseen. Lopuksi esitellään paikallisten binäärikuvioiden histogrammin jälkikäsittelyn yleistävä malli. Tähän malliin liittyen esitellään histogrammin silottamiseen tarkoitettu operaatio, jonka eräs tärkeimmistä motivaatioista on sama kuin kuvan ylösnäytteistämiseen perustuvalla johdannaisella. Erilaisten piirteenirrotusmenetelmien kehittäminen kasvojentunnistuksen osa-alueille on erittäin suosittu paikallisten binäärikuvioiden sovellusalue. Myös tässä työssä tutkittiin miten kehitetyt johdannaiset suoriutuvat näissä osa-ongelmissa. Tutkimuksen kokeellinen osuus ja siihen liittyvät numeeriset tulokset osoittavat, että esitellyt menetelmät ovat vertailukelpoisia kirjallisuudesta löytyvien parhaimpien paikallisten binäärikuvioiden johdannaisten kanssa
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Nguyen, Thanh Le Vi. "Local Binary Pattern based algorithms for the discrimination and detection of crops and weeds with similar morphologies." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2020. https://ro.ecu.edu.au/theses/2359.

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In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication practice in weed management and weed invasion. However, the excessive use of herbicides has a detrimental impact on the human health, economy and environment. Before weeds are resistant to herbicides and respond better to weed control strategies, it is necessary to control them in the fallow, pre-sowing, early post-emergent and in pasture phases. Moreover, the development of herbicide resistance in weeds is the driving force for inventing precision and automation weed treatments. Various weed detection techniques have been developed to identify weed species in crop fields, aimed at improving the crop quality, reducing herbicide and water usage and minimising environmental impacts. In this thesis, Local Binary Pattern (LBP)-based algorithms are developed and tested experimentally, which are based on extracting dominant plant features from camera images to precisely detecting weeds from crops in real time. Based on the efficient computation and robustness of the first LBP method, an improved LBP-based method is developed based on using three different LBP operators for plant feature extraction in conjunction with a Support Vector Machine (SVM) method for multiclass plant classification. A 24,000-image dataset, collected using a testing facility under simulated field conditions (Testbed system), is used for algorithm training, validation and testing. The dataset, which is published online under the name “bccr-segset”, consists of four subclasses: background, Canola (Brassica napus), Corn (Zea mays), and Wild radish (Raphanus raphanistrum). In addition, the dataset comprises plant images collected at four crop growth stages, for each subclass. The computer-controlled Testbed is designed to rapidly label plant images and generate the “bccr-segset” dataset. Experimental results show that the classification accuracy of the improved LBP-based algorithm is 91.85%, for the four classes. Due to the similarity of the morphologies of the canola (crop) and wild radish (weed) leaves, the conventional LBP-based method has limited ability to discriminate broadleaf crops from weeds. To overcome this limitation and complex field conditions (illumination variation, poses, viewpoints, and occlusions), a novel LBP-based method (denoted k-FLBPCM) is developed to enhance the classification accuracy of crops and weeds with similar morphologies. Our contributions include (i) the use of opening and closing morphological operators in pre-processing of plant images, (ii) the development of the k-FLBPCM method by combining two methods, namely, the filtered local binary pattern (LBP) method and the contour-based masking method with a coefficient k, and (iii) the optimal use of SVM with the radial basis function (RBF) kernel to precisely identify broadleaf plants based on their distinctive features. The high performance of this k-FLBPCM method is demonstrated by experimentally attaining up to 98.63% classification accuracy at four different growth stages for all classes of the “bccr-segset” dataset. To evaluate performance of the k-FLBPCM algorithm in real-time, a comparison analysis between our novel method (k-FLBPCM) and deep convolutional neural networks (DCNNs) is conducted on morphologically similar crops and weeds. Various DCNN models, namely VGG-16, VGG-19, ResNet50 and InceptionV3, are optimised, by fine-tuning their hyper-parameters, and tested. Based on the experimental results on the “bccr-segset” dataset collected from the laboratory and the “fieldtrip_can_weeds” dataset collected from the field under practical environments, the classification accuracies of the DCNN models and the k-FLBPCM method are almost similar. Another experiment is conducted by training the algorithms with plant images obtained at mature stages and testing them at early stages. In this case, the new k-FLBPCM method outperformed the state-of-the-art CNN models in identifying small leaf shapes of canola-radish (crop-weed) at early growth stages, with an order of magnitude lower error rates in comparison with DCNN models. Furthermore, the execution time of the k-FLBPCM method during the training and test phases was faster than the DCNN counterparts, with an identification time difference of approximately 0.224ms per image for the laboratory dataset and 0.346ms per image for the field dataset. These results demonstrate the ability of the k-FLBPCM method to rapidly detect weeds from crops of similar appearance in real time with less data, and generalize to different size plants better than the CNN-based methods.
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Cui, Chen. "Adaptive weighted local textural features for illumination, expression and occlusion invariant face recognition." University of Dayton / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1374782158.

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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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Books on the topic "Local extensive binary pattern"

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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, Limited, 2013.

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Lumini, Alessandra, Lakhmi C. Jain, Sheryl Brahnam, and Loris Nanni. Local Binary Patterns: New Variants and Applications. Springer London, Limited, 2013.

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Lumini, Alessandra, Lakhmi C. Jain, Sheryl Brahnam, and Loris Nanni. Local Binary Patterns: New Variants and Applications. Springer Berlin / Heidelberg, 2016.

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Lumini, Alessandra, Lakhmi C. Jain, Sheryl Brahnam, and Loris Nanni. Local Binary Patterns: New Variants and New Applications. Springer Berlin / Heidelberg, 2013.

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Book chapters on the topic "Local extensive binary pattern"

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Khare, Divya, D. R. Gangodkar, and Saurabh Dwivedi. "Improved Rotated Local Binary Pattern." In Data Management, Analytics and Innovation. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1402-5_1.

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Kas, Mohamed, Youssef El Merabet, Yassine Ruichek, and Rochdi Messoussi. "Local Directional Multi Radius Binary Pattern." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76357-6_4.

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Varma, Satishkumar, and Sanjay Talbar. "Video Retrieval Using Local Binary Pattern." In Computational Intelligence in Data Mining - Volume 1. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2205-7_12.

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Tüű-Szabó, Boldizsár, Gábor Kovács, Péter Földesi, Szilvia Nagy, and László T. Kóczy. "Local Binary Pattern-Based Fingerprint Matching." In Studies in Computational Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74970-5_21.

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Janusch, Ines, and Walter G. Kropatsch. "Reeb Graphs Through Local Binary Patterns." In Graph-Based Representations in Pattern Recognition. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18224-7_6.

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Shabat, Abuobayda M., and Jules-Raymond Tapamo. "Directional Local Binary Pattern for Texture Analysis." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41501-7_26.

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Gupta, Sumit Kumar, Susheel Yadav, Dhirendra Pratap Singh, and Jaytrilok Choudhary. "Local Roughness Binary Pattern for Texture Classification." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4687-5_26.

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Srinivasan, Dhana Srinithi, Soundarya Ravichandran, Thamizhi Shanmugam Indrani, and G. R. Karpagam. "Local Binary Pattern-Based Criminal Identification System." In Sustainable Digital Technologies for Smart Cities. CRC Press, 2023. http://dx.doi.org/10.1201/9781003307716-4.

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Singh, Aditya, Ramesh K. Sunkaria, and Anterpreet Kaur. "A Review on Local Binary Pattern Variants." In Proceedings of First International Conference on Computational Electronics for Wireless Communications. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6246-1_46.

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Vianney, Eone Etoua Oscar, Tapamo Kenfack Hippolyte Michel, Mboule Ebele Brice Auguste, Mbietieu Amos Mbietieu, and Essuthi Essoh Serge Leonel. "Race Recognition Using Enhanced Local Binary Pattern." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93314-2_8.

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Conference papers on the topic "Local extensive binary pattern"

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Hegenbart, Sebastian, and Andreas Uhl. "An Orientation-Adaptive Extension to Scale-Adaptive Local Binary Patterns." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.202.

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Saleem, Sajid, Abdul Bais, and Robert Sablatnig. "A Gradient Extension of Center Symmetric Local Binary Patterns for Robust RGB-NIR Image Matching." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.150.

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R, Akash, and Sukanya S. T. "Kinship Measurement on Face Images by Structured Similarity Fusion." In The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/ijux3686/ngcesi23p31.

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Kinship verification, which is a challenging problem in computer vision and pattern discovery. It has several applications, such as organizing photo albums, recognizing resemblances among humans, and finding missing children. A system for facial kinship verification based on several kinds of texture descriptors (local binary patterns, local ternary patterns, local directional patterns, local phase quantization, and binarized statistical image features) with pyramid multilevel (PML) face representation for feature extraction along with our proposed paired feature representation and our proposed robust feature selection to reduce the number of features. The proposed approach consists of the following three main stages: (1) face pre-processing, (2) feature extraction and selection, and (3) kinship verification. Extensive experiments are conducted on five publicly available databases (Cornell, UB KinFace, Family 101, KinFace W-I, and KinFace W-II). Additionally, a wide experiment for each stage to find the best and most suitable settings. Many comparisons with state-of-the-art methods and through these comparisons, it appears that our experiments show stable and good results.
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Ma, Yan. "Number Local binary pattern: An Extended Local Binary Pattern." In 2011 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2011. http://dx.doi.org/10.1109/icwapr.2011.6014464.

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Wu, Zhize, Yu Xia, and Shouhong Wan. "An Extension to the Local Binary Patterns for Image Retrieval." In CCA 2014. Science & Engineering Research Support soCiety, 2014. http://dx.doi.org/10.14257/astl.2014.45.16.

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Lin, Jeng-Hau, Justin Lazarow, Yunfan Yang, Dezhi Hong, Rajesh K. Gupta, and Zhuowen Tu. "Local Binary Pattern Networks." In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2020. http://dx.doi.org/10.1109/wacv45572.2020.9093550.

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Chen, Jie, Vili Kellokumpu, Guoying Zhao, and Matti Pietikainen. "RLBP: Robust Local Binary Pattern." In British Machine Vision Conference 2013. British Machine Vision Association, 2013. http://dx.doi.org/10.5244/c.27.122.

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Sharmila Kumari, M., and Bheemappa Arjunappa Mudalawar. "Local Binary Pattern and Block Based Local Binary Pattern for Face Recognition:An Empirical Study." In Second International Conference on Signal Processing, Image Processing and VLSI. Research Publishing Services, 2015. http://dx.doi.org/10.3850/978-981-09-6200-5_o-86.

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Ekenel, Hazim K., Mika Fischer, Erkin Tekeli, Rainer Stiefelhagen, and Aytul Ercil. "Local binary pattern domain local appearance face recognition." In 2008 IEEE 16th Signal Processing, Communication and Applications Conference (SIU). IEEE, 2008. http://dx.doi.org/10.1109/siu.2008.4632751.

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Nabiyev, Vasif V., and Fuat Bolukbas. "Race recognition with Local Binary Pattern." In 2009 International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2009. http://dx.doi.org/10.1109/icaict.2009.5372559.

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Reports on the topic "Local extensive binary pattern"

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Brandenberg, Scott, Jonathan Stewart, Kenneth Hudson, Dong Youp Kwak, Paolo Zimmaro, and Quin Parker. Ground Failure of Hydraulic Fills in Chiba, Japan and Data Archival in Community Database. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, 2024. http://dx.doi.org/10.55461/amnh7013.

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This report describes analysis of ground failure and lack thereof observed in the Mihama Ward portion of Chiba, Japan following the 2011 M9.0 Tohoku Earthquake. In conjunction with this work, we have also significantly expanded the laboratory component of the Next Generation Liquefaction (NGL) relational database. The district referred to as Mihama Ward is on ground composed of hydraulic fill sluiced in by pipes, thereby resulting in a gradient of soil coarseness, with coarser soils deposited near the pipes and fine-grained soils carried further away. Observations from local researchers at Chiba University following the 2011 Tohoku Earthquake indicate that ground failure was observed closer to the locations where the pipes deposited the soil, and not further away. This ground failure consisted of extensive sand boiling and ground cracking, which led to building settlement and pipe breaks. Our hypothesis at the outset of the project was that liquefaction susceptibility might explain the pattern of ground failure. Specifically, soils deposited near the pipes are susceptible due to their coarser texture, while soils further from the pipes may be non-susceptible due to the presence of clay minerals and higher plasticity. Were this hypothesis borne out by evidence, soil in the transition zone would have provided important insights about liquefaction susceptibility. Based on testing of soils in our laboratory, we find this hypothesis to be only partially correct. We have confirmed that there are regions with high clay contents and no ground failure and other regions with predominantly granular soils and extensive surface manifestation of liquefaction. Where the hypothesis breaks down is in the transition zone, where we found that the fine-grained soils are non-plastic, and therefore they are susceptible to liquefaction. Our interpretation is that these silt materials likely liquefied during the earthquake, but did not manifest liquefaction. Two factors may have contributed to this lack of manifestation: (1) level ground conditions and lack of large driving static shear stresses (structures in the region are light residential construction) and (2) the silt is less likely to erode to the surface and form silt boils than the sandier soils that produced surface manifestations. This case history points to the importance of separating triggering (defined as the development of significant excess pore pressure and loss of strength) from manifestation (defined as observations of ground failure, including cracking, sand boils, and lateral spreading). The Mihama Ward case history involved laboratory tests performed by Tokyo Soil Research Co. Ltd. and the UCLA geotechnical laboratory. Given the importance of this data to the understanding of this case history, we recognized a need to incorporate laboratory tests in the NGL database alongside field tests and liquefaction observations. We therefore developed an organizational structure for laboratory tests, including direct simple shear, triaxial compression, and consolidation, and implemented the schema in the NGL database. We then uploaded data from tests performed by Tokyo Soil and UCLA. Furthermore, numerous other researchers have also uploaded laboratory test data for other sites. This report describes the organizational structure of the laboratory component of the database, and a tool for interacting with laboratory data.
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