Relevant bibliographies by topics / Distance de Bhattacharyya

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Distance de Bhattacharyya'

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Published: 2 February 2023

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Journal articles on the topic "Distance de Bhattacharyya"

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Mohammadi, Arash, and Konstantinos N. Plataniotis. "Improper Complex-Valued Bhattacharyya Distance." IEEE Transactions on Neural Networks and Learning Systems 27, no. 5 (May 2016): 1049–64. http://dx.doi.org/10.1109/tnnls.2015.2436064.

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Yoon, Jiho, and Chulhee Lee. "Edge Detection Using the Bhattacharyya Distance with Adjustable Block Space." Electronic Imaging 2020, no. 10 (January 26, 2020): 133–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.10.ipas-133.

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In this paper, we propose a new edge detection method for color images, based on the Bhattacharyya distance with adjustable block space. First, the Wiener filter was used to remove the noise as pre-processing. To calculate the Bhattacharyya distance, a pair of blocks were extracted for each pixel. To detect subtle edges, we adjusted the block space. The mean vector and covariance matrix were computed from each block. Using the mean vectors and covariance matrices, we computed the Bhattacharyya distance, which was used to detect edges. By adjusting the block space, we were able to detect weak edges, which other edge detections failed to detect. Experimental results show promising results compared to some existing edge detection methods.
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Choi, Euisun, and Chulhee Lee. "Feature extraction based on the Bhattacharyya distance." Pattern Recognition 36, no. 8 (August 2003): 1703–9. http://dx.doi.org/10.1016/s0031-3203(03)00035-9.

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Mahgoob Nafi, Shahad, and Sawsen Abdulhadi Mahmood. "Moving Objects Detection Based on Bhattacharyya Distance Measurement." Journal of Engineering and Applied Sciences 14, no. 12 (December 10, 2019): 4043–51. http://dx.doi.org/10.36478/jeasci.2019.4043.4051.

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Ibrahim, Assit prof Abdul-Wahab Sami, and Rasha Jamal Hindi. "Identification system by Tongue based on Bhattacharyya distance." Journal of Physics: Conference Series 1530 (May 2020): 012093. http://dx.doi.org/10.1088/1742-6596/1530/1/012093.

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Mehdi, Agouzal, Merzouqi Maria, and Moha Arouch. "Reduction of Hyperspectral image based on OSP and a Filter based on Bhattacharyya Distance." International Journal of Emerging Technology and Advanced Engineering 12, no. 4 (April 2, 2022): 86–93. http://dx.doi.org/10.46338/ijetae0422_12.

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Abstract— This article proposes a new method to reduce the dimensionality of the hyperspectral image of Pavia. It became obvious to reduce the hyperspectral image, before their classification. This reduction is done by several strategies and approaches according to the literature. The high dimensionality of the hyperspectral image was and remains a challenge to overcome. Since it contains labelled pixels that belong to the target area and others considered as intruders. For this reason, the proposed method aims to extract the classified pixels by the application of the orthogonal projection of the ground truth on the bands then a selection is made adopting a filter based on the minimization of the distance Bhattacharyya inter classes (one against one: band class against ground truth class). Two other distances Jeffries Matusita and Kullback Leibler were applied in the same level of the algorithm in order to validate the appropriate distance, also to confirm the reliability of the process of the new method. The results of the proposed method obtained by SVM-RBF and also KNN demonstrate a remarkable improvement in classification accuracy. The proposed procedure was able to reach over 94% for only 18 bands; hence a simple Bhattacharyya filter got just 91.45%. Keywords—Bhattacharyya, Classification, Extraction, Jefferies Matusita, Kullback Leibler, KNN, Selection, OSP, RBF-SVM. Reduction of dimensionality
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Yu, Yuanlong, Jason Gu, and Junzheng Wang. "Bhattacharyya distance‐based irregular pyramid method for image segmentation." IET Computer Vision 8, no. 6 (December 2014): 510–22. http://dx.doi.org/10.1049/iet-cvi.2013.0149.

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Lu, Jingyi, Jikang Yue, Lijuan Zhu, and Gongfa Li. "Variational mode decomposition denoising combined with improved Bhattacharyya distance." Measurement 151 (February 2020): 107283. http://dx.doi.org/10.1016/j.measurement.2019.107283.

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Chaudhuri, G., J. D. Borwankar, and P. R. K. Rao. "Bhattacharyya distance based linear discriminant function for stationary time series." Communications in Statistics - Theory and Methods 20, no. 7 (January 1991): 2195–205. http://dx.doi.org/10.1080/03610929108830627.

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Bi, Sifeng, Matteo Broggi, and Michael Beer. "The role of the Bhattacharyya distance in stochastic model updating." Mechanical Systems and Signal Processing 117 (February 2019): 437–52. http://dx.doi.org/10.1016/j.ymssp.2018.08.017.

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Dissertations / Theses on the topic "Distance de Bhattacharyya"

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Janse, Sarah A. "INFERENCE USING BHATTACHARYYA DISTANCE TO MODEL INTERACTION EFFECTS WHEN THE NUMBER OF PREDICTORS FAR EXCEEDS THE SAMPLE SIZE." UKnowledge, 2017. https://uknowledge.uky.edu/statistics_etds/30.

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In recent years, statistical analyses, algorithms, and modeling of big data have been constrained due to computational complexity. Further, the added complexity of relationships among response and explanatory variables, such as higher-order interaction effects, make identifying predictors using standard statistical techniques difficult. These difficulties are only exacerbated in the case of small sample sizes in some studies. Recent analyses have targeted the identification of interaction effects in big data, but the development of methods to identify higher-order interaction effects has been limited by computational concerns. One recently studied method is the Feasible Solutions Algorithm (FSA), a fast, flexible method that aims to find a set of statistically optimal models via a stochastic search algorithm. Although FSA has shown promise, its current limits include that the user must choose the number of times to run the algorithm. Here, statistical guidance is provided for this number iterations by deriving a lower bound on the probability of obtaining the statistically optimal model in a number of iterations of FSA. Moreover, logistic regression is severely limited when two predictors can perfectly separate the two outcomes. In the case of small sample sizes, this occurs quite often by chance, especially in the case of a large number of predictors. Bhattacharyya distance is proposed as an alternative method to address this limitation. However, little is known about the theoretical properties or distribution of B-distance. Thus, properties and the distribution of this distance measure are derived here. A hypothesis test and confidence interval are developed and tested on both simulated and real data.
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Iyer, Balaji S. "Design of a Classifier for Bearing Health Prognostics using Time Series Data." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1543922781446885.

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Dihl, Leandro Lorenzett. "Rastreamento de objetos usando descritores estatísticos." Universidade do Vale do Rio do Sinos, 2009. http://www.repositorio.jesuita.org.br/handle/UNISINOS/2273.

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O baixo custo dos sistemas de aquisição de imagens e o aumento no poder computacional das máquinas disponíveis têm causado uma demanda crescente pela análise automatizada de vídeo, em diversas aplicações, como segurança, interfaces homem-computador, análise de desempenho esportivo, etc. O rastreamento de objetos através de câmeras de vídeo é parte desta análise, e tem-se mostrado um problema desafiador na área de visão computacional. Este trabalho apresenta uma nova abordagem para o rastreamento de objetos baseada em fragmentos. Inicialmente, a região selecionada para o rastreamento é dividida em sub-regiões retangulares (fragmentos), e cada fragmento é rastreado independentemente. Além disso, o histórico de movimentação do objeto é utilizado para estimar sua posição no quadro seguinte. O deslocamento global do objeto é então obtido combinando os deslocamentos de cada fragmento e o deslocamento previsto, de modo a priorizar fragmentos com deslocamento coerente. Um esquema de atualização é aplicado no modelo
The low cost of image acquisition systems and increase the computational power of available machines have caused a growing demand for automated video analysis in several applications, such as surveillance, human-computer interfaces, analysis of sports performance, etc. Object tracking through the video sequence is part of this analysis, and it has been a challenging problem in the computer vision area. This work presents a new approach for object tracking based on fragments. Initially, the region selected for tracking is divided into rectangular subregions (patches, or fragments), and each patch is tracked independently. Moreover, the motion history of the object is used to estimate its position in the subsequent frames. The overall displacement of the object is then obtained combining the displacements of each patch and the predicted displacement vector in order to priorize fragments presenting consistent displacement. An update scheme is also applied to the model, to deal with illumination and appearance c
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Junior, Jarbas Joaci de Mesquita Sá. "Identificação de espécies vegetais por meio de análise de imagens microscópicas de folhas." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-12052008-142428/.

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A taxonomia vegetal atualmente exige um grande esforço dos botânicos, desde o processo de aquisição do espécime até a morosa comparação com as amostras já catalogadas em um herbário. Nesse contexto, o projeto TreeVis surge como uma ferramenta para a identificação de vegetais por meio da análise de atributos foliares. Este trabalho é uma ramificação do projeto TreeVis e tem o objetivo de identificar vegetais por meio da análise do corte transversal de uma folha ampliado por um microscópio. Para tanto, foram extraídas assinaturas da cutícula, epiderme superior, parênquima paliçádico e parênquima lacunoso. Cada assinatura foi avaliada isoladamente por uma rede neural pelo método leave-one-out para verificar a sua capacidade de discriminar as amostras. Uma vez selecionados os vetores de características mais importantes, os mesmos foram combinados de duas maneiras. A primeira abordagem foi a simples concatenação dos vetores selecionados; a segunda, mais elaborada, reduziu a dimensionalidade (três atributos apenas) de algumas das assinaturas componentes antes de fazer a concatenação. Os vetores finais obtidos pelas duas abordagens foram testados com rede neural via leave-one-out para medir a taxa de acertos alcançada pelo sinergismo das assinaturas das diferentes partes da folha. Os experimentos consitiram na identificação de oito espécies diferentes e na identificação da espécie Gochnatia polymorpha nos ambientes Cerrado e Mata Ciliar, nas estações Chuvosa e Seca, e sob condições de Sol e Sombra
Currently, taxonomy demands a great effort from the botanists, ranging from the process of acquisition of the sample to the comparison with the species already classified in the herbarium. For this reason, the TreeVis is a project created to identify vegetal species using leaf attributes. This work is a part of the TreeVis project and aims at identifying vegetal species by analysing cross-sections of leaves amplified by a microscope. Signatures were extract from cuticle, adaxial epiderm, palisade parenchyma and sponge parenchyma. Each signature was analysed by a neural network with the leave-one-out method to verify its ability to identify species. Once the most important feature vectors were selected, two different approachs were adopted. The first was a simple concatenation of the selected feature vectors. The second, and more elaborated approach, consisted of reducing the dimensionality (three attributes only) of some component signatures before the feature vector concatenation. The final vectors obtained by these two approaches were tested by a neural network with leave-one-out to measure the correctness rate reached by the synergism of the signatures of different leaf regions. The experiments resulted in the identification of eight different species and the identification of the Gochnatia polymorpha species in Cerradão and Gallery Forest environments, Wet and Dry seasons, and under Sun and Shadow constraints
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Maitre, Julien. "Détection et analyse des signaux faibles. Développement d’un framework d’investigation numérique pour un service caché Lanceurs d’alerte." Thesis, La Rochelle, 2022. http://www.theses.fr/2022LAROS020.

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Ce manuscrit s’inscrit dans le cadre du développement d’une plateforme d’analyse automatique de documents associée à un service sécurisé lanceurs d’alerte, de type GlobalLeaks. Nous proposons une chaine d’extraction à partir de corpus de document, d’analyse semi-automatisée et de recherche au moyen de requêtes Web pour in fine, proposer des tableaux de bord décrivant les signaux faibles potentiels. Nous identifions et levons un certain nombre de verrous méthodologiques et technologiques inhérents : 1) à l’analyse automatique de contenus textuels avec un minimum d’a priori, 2) à l’enrichissement de l’information à partir de recherches Web 3) à la visualisation sous forme de tableau de bord et d’une représentation dans un espace 3D interactif. Ces approches, statique et dynamique, sont appliquées au contexte du data journalisme, et en particulier, au traitement, analyse et hiérarchisation d’informations hétérogènes présentes dans des documents. Cette thèse propose également une étude de faisabilité et de prototypage par la mise en œuvre d’une chaine de traitement sous forme d’un logiciel. La construction de celui-ci a nécessité la caractérisation d’un signal faible pour lequel nous avons proposé une définition. Notre objectif est de fournir un outil paramétrable et générique à toute thématique. La solution que nous proposons repose sur deux approches : statique et dynamique. Dans l’approche statique, contrairement aux approches existantes nécessitant la connaissance de termes pertinents dans un domaine spécifique, nous proposons une solution s’appuyant sur des techniques nécessitant une intervention moindre de l’expert du domaine. Dans ce contexte, nous proposons une nouvelle approche de modélisation thématique multi-niveaux. Cette méthode d’approche conjointe combine une modélisation thématique, un plongement de mots et un algorithme où le recours à un expert du domaine permet d’évaluer la pertinence des résultats et d’identifier les thèmes porteurs de signaux faibles potentiels. Dans l’approche dynamique, nous intégrons une solution de veille à partir des signaux faibles potentiels trouvées dans les corpus initiaux et effectuons un suivi pour étudier leur évolution. Nous proposons donc une solution d’agent mining combinant data mining et système multi-agents où des agents animés par des forces d’attraction/répulsion représentant documents et mots se déplacent. La visualisation des résultats est réalisée sous forme de tableau de bord et de représentation dans un espace 3D interactif dans unclient Unity. Dans un premier temps, l’approche statique a été évaluée dans une preuve de concept sur des corpus synthétiques et réelles utilisés comme vérité terrain. L’ensemble de la chaine de traitement (approches statique et dynamique), mise en œuvre dans le logiciel WILD, est dans un deuxième temps appliquée sur des données réelles provenant de bases documentaires
This manuscript provides the basis for a complete chain of document analysis for a whistleblower service, such as GlobalLeaks. We propose a chain of semi-automated analysis of text document and search using websearch queries to in fine present dashboards describing weak signals. We identify and solve methodological and technological barriers inherent to : 1) automated analysis of text document with minimum a priori information,2) enrichment of information using web search 3) data visualization dashboard and 3D interactive environment. These static and dynamic approaches are used in the context of data journalism for processing heterogeneous types of information within documents. This thesis also proposed a feasibility study and prototyping by the implementation of a processing chain in the form of a software. This construction requires a weak signal definition. Our goal is to provide configurable and generic tool. Our solution is based on two approaches : static and dynamic. In the static approach, we propose a solution requiring less intervention from the domain expert. In this context, we propose a new approach of multi-leveltopic modeling. This joint approach combines topic modeling, word embedding and an algorithm. The use of a expert helps to assess the relevance of the results and to identify topics with weak signals. In the dynamic approach, we integrate a solution for monitoring weak signals and we follow up to study their evolution. Wetherefore propose and agent mining solution which combines data mining and multi-agent system where agents representing documents and words are animated by attraction/repulsion forces. The results are presented in a data visualization dashboard and a 3D interactive environment in Unity. First, the static approach is evaluated in a proof-of-concept with synthetic and real text corpus. Second, the complete chain of document analysis (static and dynamic) is implemented in a software and are applied to data from document databases
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Cepeda, Fuentealba Sebastián. "Segmentación de vasos sanguíneos de retina usando selección de características mediantes distancia de bhattacharyya y algoritmos genéticos, para un clasificador por maximización de la entropía." Tesis, Universidad de Chile, 2016. http://repositorio.uchile.cl/handle/2250/138129.

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Magíster en Ciencias de la Ingeniería, Mención Eléctrica
Ingeniero Civil Eléctrico
La segmentación de vasos sanguíneos en imágenes digitales permite tener un método no invasivo de diagnosticar enfermedades como diabetes, hipertensión y algunas enfermedades cardiovasculares. Puede servir en la implementación de programas para la detección temprana de varias enfermedades de la retina y también para la identificación biométrica basada en la forma de los vasos sanguíneos. La segmentación manual de vasos sanguíneos de retina es una tarea que consume mucho tiempo y requiere entrenamiento y habilidad. Los vasos sanguíneos en la retina están compuestos de arterias y venas que se presentan como líneas oscuras en un fondo relativamente uniforme. La dificultad de su segmentación se debe a su forma, tamaño y luminosidad altamente variables, ruido en la imagen, además de su cruce y bifurcación. En los métodos para la segmentación automática de vasos previamente publicados en revistas internacionales están aquellos que obtienen un vector de características por pixel utilizando el canal verde de la imagen y la respuesta a un filtro gaussiano en 5 escalas que ocupa k-vecinos más cercanos (KNN) como clasificador. Otro método crea un vector de 27 características y usa k-vecinos más cercanos como clasificador. Otro método extrae un vector de 5 características, incluyendo el canal verde y la respuesta a filtros Gabor en 4 escalas y usa un clasificador bayesiano. En esta tesis se propone un método de segmentación automática de vasos sanguíneos de cuatro etapas. Primero, se extrae el canal verde de la imagen, ya que es donde más destacan los vasos sanguíneos. A continuación se efectúa una ecualización de histograma adaptiva para mejorar el contraste entre los pixeles del fondo y de los vasos sanguíneos. Luego se aplica un banco de filtros correspondientes a una suma de filtros Gabor, obteniendo como resultado el máximo de las respuestas al banco de filtros. Finalmente, se segmenta la respuesta al banco de filtros usando un umbral calculado con la maximización de la entropía de la matriz de co-ocurrencia. Para la optimización de los parámetros y evaluación de resultados se utilizó la base de datos DRIVE ya que es una base de datos marcada y disponible internacionalmente, que permite comparar los resultados obtenidos con otros publicados previamente. La optimización de los parámetros de la ecualización de histograma adaptiva y la elección del canal verde se realizó maximizando la distancia de Bhattacharyya entre las clases de vasos sanguíneos y fondo de las imágenes. Los parámetros de los filtros fueron optimizados mediante algoritmos genéticos, maximizando el accuracy de la segmentación. De las 40 imágenes de la base de datos DRIVE se eligieron 10 para el conjunto de entrenamiento y 10 para el de validación. El conjunto de prueba usa las 20 imágenes estándares. Los resultados muestran que la precisión obtenida para el conjunto de prueba fue de 0,9462, lo que es similar a los resultados obtenidos por las mejores publicaciones en la misma base de datos y a la obtenida por el segundo experto humano (0,9473). Al comparar con uno de los métodos con mejores resultados (precisión de 0,9466), el tiempo de segmentación disminuyó de 120[s] en el trabajo previo, a 5[s] en el método propuesto. En comparación con los resultados de una implementación de redes neuronales convolucionales, ésta tardó más (170[s]) y su precisión fue menor que con el método propuesto. Por lo tanto el método propuesto muestra una precisión cercana a la máxima previamente publicada pero con un tiempo de procesamiento mucho menor. A futuro el método podría paralelizarse para mejorar aún más su tiempo de cómputo.
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Essid, Slim. "Classification automatique des signaux audio-fréquences : reconnaissance des instruments de musique." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2005. http://pastel.archives-ouvertes.fr/pastel-00002738.

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L'objet de cette thèse est de contribuer à améliorer l'identification automatique des instruments de musique dans des contextes réalistes, (sur des solos de musique, mais également sur des pièces multi-instrumentales). Nous abordons le problème suivant une approche de classification automatique en nous efforçant de rechercher des réalisations performantes des différents modules constituant le système que nous proposons. Nous adoptons un schéma de classification hiérarchique basé sur des taxonomies des instruments et des mélanges d'instruments. Ces taxonomies sont inférées au moyen d'un algorithme de clustering hiérarchique exploitant des distances probabilistes robustes qui sont calculées en utilisant une méthode à noyau. Le système exploite un nouvel algorithme de sélection automatique des attributs pour produire une description efficace des signaux audio qui, associée à des machines à vecteurs supports, permet d'atteindre des taux de reconnaissance élevés sur des pièces sonores reflétant la diversité de la pratique musicale et des conditions d'enregistrement rencontrées dans le monde réel. Notre architecture parvient ainsi à identifier jusqu'à quatre instruments joués simultanément, à partir d'extraits de jazz incluant des percussions.
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"Particle Image Segmentation Based on Bhattacharyya Distance." Master's thesis, 2015. http://hdl.handle.net/2286/R.I.34888.

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abstract: Image segmentation is of great importance and value in many applications. In computer vision, image segmentation is the tool and process of locating objects and boundaries within images. The segmentation result may provide more meaningful image data. Generally, there are two fundamental image segmentation algorithms: discontinuity and similarity. The idea behind discontinuity is locating the abrupt changes in intensity of images, as are often seen in edges or boundaries. Similarity subdivides an image into regions that fit the pre-defined criteria. The algorithm utilized in this thesis is the second category. This study addresses the problem of particle image segmentation by measuring the similarity between a sampled region and an adjacent region, based on Bhattacharyya distance and an image feature extraction technique that uses distribution of local binary patterns and pattern contrasts. A boundary smoothing process is developed to improve the accuracy of the segmentation. The novel particle image segmentation algorithm is tested using four different cases of particle image velocimetry (PIV) images. The obtained experimental results of segmentations provide partitioning of the objects within 10 percent error rate. Ground-truth segmentation data, which are manually segmented image from each case, are used to calculate the error rate of the segmentations.
Dissertation/Thesis
Masters Thesis Electrical Engineering 2015
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Book chapters on the topic "Distance de Bhattacharyya"

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Sharif, Md Haidar, Sahin Uyaver, and Chabane Djeraba. "Crowd Behavior Surveillance Using Bhattacharyya Distance Metric." In Computational Modeling of Objects Represented in Images, 311–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12712-0_28.

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Bi, Sifeng, and Michael Beer. "Overview of Stochastic Model Updating in Aerospace Application Under Uncertainty Treatment." In Uncertainty in Engineering, 115–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83640-5_8.

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AbstractThis chapter presents the technique route of model updating in the presence of imprecise probabilities. The emphasis is put on the inevitable uncertainties, in both numerical simulations and experimental measurements, leading the updating methodology to be significantly extended from deterministic sense to stochastic sense. This extension requires that the model parameters are not regarded as unknown-but-fixed values, but random variables with uncertain distributions, i.e. the imprecise probabilities. The final objective of stochastic model updating is no longer a single model prediction with maximal fidelity to a single experiment, but rather the calibrated distribution coefficients allowing the model predictions to fit with the experimental measurements in a probabilistic point of view. The involvement of uncertainty within a Bayesian updating framework is achieved by developing a novel uncertainty quantification metric, i.e. the Bhattacharyya distance, instead of the typical Euclidian distance. The overall approach is demonstrated by solving the model updating sub-problem of the NASA uncertainty quantification challenge. The demonstration provides a clear comparison between performances of the Euclidian distance and the Bhattacharyya distance, and thus promotes a better understanding of the principle of stochastic model updating, as no longer to determine the unknown-but-fixed parameters, but rather to reduce the uncertainty bounds of the model prediction and meanwhile to guarantee the existing experimental data to be still enveloped within the updated uncertainty space.
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Liang, Shuang, Ning Liu, Guanxiang Wang, and Wei Guo. "Camera Sabotage Detection Based on LOG Histogram and Bhattacharyya Distance." In Lecture Notes in Electrical Engineering, 197–203. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27323-0_25.

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Liu, Qingshan, and Dimitris N. Metaxas. "Unifying Subspace and Distance Metric Learning with Bhattacharyya Coefficient for Image Classification." In Emerging Trends in Visual Computing, 254–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00826-9_11.

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Lahlimi, Mohammed, Mounir Ait Kerroum, and Youssef Fakhri. "Band Selection with Bhattacharyya Distance Based on the Gaussian Mixture Model for Hyperspectral Image Classification." In Recent Advances in Electrical and Information Technologies for Sustainable Development, 87–94. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05276-8_10.

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El merabet, Youssef, Yassine Ruichek, Saman Ghaffarian, Zineb Samir, Tarik Boujiha, Raja Touahni, and Rochdi Messoussi. "Horizon Line Detection from Fisheye Images Using Color Local Image Region Descriptors and Bhattacharyya Coefficient-Based Distance." In Advanced Concepts for Intelligent Vision Systems, 58–70. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48680-2_6.

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Migdał, Wiesław, Jacek Wodecki, Maciej Wuczyński, Paweł Stefaniak, Agnieszka Wyłomańska, and Radosław Zimroz. "Long Term Temperature Data Analysis for Damage Detection in Electric Motor Bearings with Density Modeling and Bhattacharyya Distance." In Applied Condition Monitoring, 151–59. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11220-2_16.

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Abci, Boussad, Joudy Nader, Maan El Badaoui El Najjar, and Vincent Cocquempot. "Fault-Tolerant Multi-sensor Fusion and Thresholding Based on the Bhattacharyya Distance with Application to a Multi-robot System." In Lecture Notes in Control and Information Sciences - Proceedings, 347–64. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85318-1_21.

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Gupta, Surendra, Urjita Thakar, and Sanjiv Tokekar. "Canonical Correlation Analysis with Bhattacharya Similarity Distance for Multiview Data Representation." In Advances in Intelligent Systems and Computing, 505–16. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2712-5_41.

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"Other Distances for Face Recognition." In Similarity Measures for Face Recognition, edited by Enrico Vezzetti and Federica Marcolin, 57–67. BENTHAM SCIENCE PUBLISHERS, 2015. http://dx.doi.org/10.2174/9781681080444115010008.

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Abstract:
Other distances are employed for face recognition, but their usage within the field is less preponderant than the previous ones. This chapter collects these measures, which are known as bottleneck, Procrustes, Earth mover’s, and Bhattacharyya distances. A subsection dealing with performances is only presented for the Bhattacharyya distance, which, although a non-extensive application in the field of face recognition, is one of the most efficient measures of the branch.
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Conference papers on the topic "Distance de Bhattacharyya"

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Xuan Guorong, Chai Peiqi, and Wu Minhui. "Bhattacharyya distance feature selection." In Proceedings of 13th International Conference on Pattern Recognition. IEEE, 1996. http://dx.doi.org/10.1109/icpr.1996.546751.

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Ke, Ke, Tao Zhao, and Ou Li. "Bhattacharyya Distance for Blind Image Steganalysis." In 2010 International Conference on Multimedia Information Networking and Security. IEEE, 2010. http://dx.doi.org/10.1109/mines.2010.143.

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Mak, Brian, and Etienne Barnard. "Phone clustering using the bhattacharyya distance." In 4th International Conference on Spoken Language Processing (ICSLP 1996). ISCA: ISCA, 1996. http://dx.doi.org/10.21437/icslp.1996-508.

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Basener, William, and Marty Flynn. "Microscene evaluation using the Bhattacharyya distance." In Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VII, edited by Allen M. Larar, Makoto Suzuki, and Jianyu Wang. SPIE, 2018. http://dx.doi.org/10.1117/12.2327004.

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Cheon, Yongsung, and Chulhee Lee. "Color Edge Detection based on Bhattacharyya Distance." In 14th International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006433903680371.

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Shen, Bichuan, and C. H. Chen. "Bhattacharyya distance based video scene change detection." In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Mingyue Ding, Bir Bhanu, Friedrich M. Wahl, and Jonathan Roberts. SPIE, 2009. http://dx.doi.org/10.1117/12.837501.

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Guorong Xuan, Xiuming Zhu, Peiqi Chai, Zhenping Zhang, Yun Q. Shi, and Dongdong Fu. "Feature Selection based on the Bhattacharyya Distance." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.557.

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Guorong Xuan, Xiuming Zhu, Peiqi Chai, Zhenping Zhang, Yun Q. Shi, and Dongdong Fu. "Feature Selection based on the Bhattacharyya Distance." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.558.

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Han, J., and C. Lee. "Color Lane Line Detection Using the Bhattacharyya Distance." In 2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA). IEEE, 2020. http://dx.doi.org/10.1109/iisa50023.2020.9284147.

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Baskoro, Jatmiko Budi, Ari Wibisono, and Wisnu Jatmiko. "Bhattacharyya distance-based tracking: A vehicle counting application." In 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS). IEEE, 2017. http://dx.doi.org/10.1109/icacsis.2017.8355071.

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