Relevant bibliographies by topics / Bhattacharyya coefficients

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Academic literature on the topic 'Bhattacharyya coefficients'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Bhattacharyya coefficients"

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Peng, Xiao Bin, and Zhi Jun Li. "Target Scale Adaptive Control Based on Comparing Bhattacharyya Coefficient." Advanced Materials Research 971-973 (June 2014): 1772–77. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1772.

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Aiming at the limitations of the traditional mean shift, such as invariable kernel bandwidth, an improved tracking algorithm with the following strategies is proposed. The target model and the candidate are described by the similarity between them is evaluated by Bhattacharyya coefficient. This algorithm firstly calculates the Bhattacharyya coefficient of the template target histogram and template background histogram and calculates the Bhattacharyya coefficient of the candidate target histogram of the current frame and template background histogram when tracking. Then judge the change tendenc
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Peng, Ning Song, Jie Yang, and D. K. Zhou. "Study on Bhattacharyya Coefficients within Mean-Shift Framework and its Application." Soft Computing 10, no. 12 (2006): 1127–34. http://dx.doi.org/10.1007/s00500-005-0035-5.

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Dr., Aziz Makandar, Rashmi Somshekhar Mrs., and Smitha M. Ms. "Face Recognition by using wavelet based frame work." International Journal of Trend in Scientific Research and Development 2, no. 5 (2018): 1980–87. https://doi.org/10.31142/ijtsrd17067.

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Content based indexing methods are of great interest for image and video retrieval in audio visual archives, such as in the DiVAN project that we are currently developing. Detecting and recognizing human faces automatically in video data provide users with powerful tools for performing queries. The work is done for recognition of the face by using wavelet packet decomposition. Each face is described by a subset of band filtered images containing wavelet coefficients. These coefficients characterize the face texture and a set of simple statistical measures allow us to form compact and meaningfu
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He, Liang, Yuming Bo, and Gaopeng Zhao. "Multifeatures Based Compressive Sensing Tracking." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/439614.

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To benefit from the development of compressive sensing, we cast tracking as a sparse approximation problem in a particle filter framework based on multifeatures. In this framework, the target template is composed of multiple features extracted from visible and infrared frames; in addition, occlusion, interruption, and noises are addressed through a set of trivial templates. With this model, the sparsity is achieved via a compressive sensing approach without nonnegative constraints; then the residual between sparsity representation and the compressed sensing observation is used to measure the l
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Maza-Quiroga, Rosa, Karl Thurnhofer-Hemsi, Domingo López-Rodríguez, and Ezequiel López-Rubio. "Regression of the Rician Noise Level in 3D Magnetic Resonance Images from the Distribution of the First Significant Digit." Axioms 12, no. 12 (2023): 1117. http://dx.doi.org/10.3390/axioms12121117.

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This paper investigates the distribution characteristics of Fourier, discrete cosine, and discrete sine transform coefficients in T1 MRI images. This paper reveals their adherence to Benford’s law, characterized by a logarithmic distribution of first digits. The impact of Rician noise on the first digit distribution is examined, which causes deviations from the ideal distribution. A novel methodology is proposed for noise level estimation, employing metrics such as the Bhattacharyya distance, Kullback–Leibler divergence, total variation distance, Hellinger distance, and Jensen–Shannon divergen
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Al Mahmud, Nahyan, and Shahfida Amjad Munni. "Qualitative Analysis of PLP in LSTM for Bangla Speech Recognition." International journal of Multimedia & Its Applications 12, no. 5 (2020): 1–8. http://dx.doi.org/10.5121/ijma.2020.12501.

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The performance of various acoustic feature extraction methods has been compared in this work using Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic features are a series of vectors that represents the speech signals. They can be classified in either words or sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual
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Nahyan, Al Mahmud, and Amjad Munni Shahfida. "Qualitative Analysis of PLP in LSTM for Bangla Speech Recognition." International Journal of Multimedia & Its Applications (IJMA) 12, no. 5 (2022): 1–8. https://doi.org/10.5281/zenodo.7229505.

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The performance of various acoustic feature extraction methods has been compared in this work using Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic features are a series of vectors that represents the speech signals. They can be classified in either words or sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual
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Sahu, Sima, Harsh Vikram Singh, Basant Kumar, and Amit Kumar Singh. "A Bayesian Multiresolution Approach for Noise Removal in Medical Magnetic Resonance Images." Journal of Intelligent Systems 29, no. 1 (2018): 189–201. http://dx.doi.org/10.1515/jisys-2017-0402.

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Abstract A Bayesian approach using wavelet coefficient modeling is proposed for de-noising additive white Gaussian noise in medical magnetic resonance imaging (MRI). In a parallel acquisition process, the magnetic resonance image is affected by white Gaussian noise, which is additive in nature. A normal inverse Gaussian probability distribution function is taken for modeling the wavelet coefficients. A Bayesian approach is implemented for filtering the noisy wavelet coefficients. The maximum likelihood estimator and median absolute deviation estimator are used to find the signal parameters, si
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Trabelsi, Imen, and Med Salim Bouhlel. "Feature Selection for GUMI Kernel-Based SVM in Speech Emotion Recognition." International Journal of Synthetic Emotions 6, no. 2 (2015): 57–68. http://dx.doi.org/10.4018/ijse.2015070104.

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Speech emotion recognition is the indispensable requirement for efficient human machine interaction. Most modern automatic speech emotion recognition systems use Gaussian mixture models (GMM) and Support Vector Machines (SVM). GMM are known for their performance and scalability in the spectral modeling while SVM are known for their discriminatory power. A GMM-supervector characterizes an emotional style by the GMM parameters (mean vectors, covariance matrices, and mixture weights). GMM-supervector SVM benefits from both GMM and SVM frameworks. In this paper, the GMM-UBM mean interval (GUMI) ke
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Poloczek, Łukasz, Roman Kuziak, Valeriy Pidvysots’kyy, Danuta Szeliga, Jan Kusiak, and Maciej Pietrzyk. "Physical and Numerical Simulations for Predicting Distribution of Microstructural Features during Thermomechanical Processing of Steels." Materials 15, no. 5 (2022): 1660. http://dx.doi.org/10.3390/ma15051660.

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The design of modern construction materials with heterogeneous microstructures requires a numerical model that can predict the distribution of microstructural features instead of average values. The accuracy and reliability of such models depend on the proper identification of the coefficients for a particular material. This work was motivated by the need for advanced experimental data to identify stochastic material models. Extensive experiments were performed to supply data to identify a model of austenite microstructure evolution in steels during hot deformation and during the interpass tim
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Book chapters on the topic "Bhattacharyya coefficients"

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Bi, Sifeng, and Michael Beer. "Overview of Stochastic Model Updating in Aerospace Application Under Uncertainty Treatment." In Uncertainty in Engineering. 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 predicti
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Qin, Xiaofan, Wenan Tan, and Anqiong Tang. "A New Trust-Based Collaborative Filtering Measure Using Bhattacharyya Coefficient." In Computer Supported Cooperative Work and Social Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1377-0_31.

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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. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00826-9_11.

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Zhang, Chunxia, and Ming Yang. "An Improved Collaborative Filtering Algorithm Based on Bhattacharyya Coefficient and LDA Topic Model." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2122-1_17.

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Drees, Dominik, Florian Eilers, Ang Bian, and Xiaoyi Jiang. "A Bhattacharyya Coefficient-Based Framework for Noise Model-Aware Random Walker Image Segmentation." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16788-1_11.

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Noom, Jacques, Nguyen Hieu Thao, Oleg Soloviev, and Michel Verhaegen. "Closed-Loop Active Model Diagnosis Using Bhattacharyya Coefficient: Application to Automated Visual Inspection." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71187-0_60.

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El merabet, Youssef, Yassine Ruichek, Saman Ghaffarian, et al. "Horizon Line Detection from Fisheye Images Using Color Local Image Region Descriptors and Bhattacharyya Coefficient-Based Distance." In Advanced Concepts for Intelligent Vision Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48680-2_6.

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Evren, Atıf, Erhan Ustaoğlu, Elif Tuna, and Büşra Şahin. "Use of Relative Entropy Statistics in Contingency Tables." In Güncel Ekonometrik ve İstatistiksel Uygulamalar ile Akademik Çalışmalar. Özgür Yayınları, 2024. http://dx.doi.org/10.58830/ozgur.pub518.c2132.

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There are various information theoretic divergence measures used for determining associations between nominal variables. Among them, Shannon mutual information statistic is especially appealing, since its sampling properties are well-known. Although Shannon mutual information is more frequently used, Rényi and Tsallis mutual informations, as envelopes of various tools, provide much higher flexibility than Shannon mutual information. Indeed, Shannon mutual information is a special case of Kullback-Leibler divergence, Rényi, and Tsallis mutual informations. In this study, large sampling properti
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Sivasankar, Thota, Pavan Kumar Sharma, M. N. S. Ramya, Pithani Venkatesh, and G. D. Bairagi. "Evaluation of Multi-Temporal Sentinel-1 Dual Polarization SAR Data for Crop Type Classification." In Advances in Environmental Engineering and Green Technologies. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-5027-4.ch003.

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India is one of the highly populated countries, and its economy mainly depends on agriculture. The crop type classification is an essential requirement for ensuring food security, crop monitoring, and to understand the environmental consequences of cultivated ecosystems. This study exploits freely available multi-temporal SAR data for discriminating crop types, such as wheat, gram, and mustard, over Ashok Nagar district, Madhya Pradesh, India. Nine Sentinel-1 dual-polarized data acquired from January 2018 to April 2018 in interferometric wide swath mode are used. Class separability analysis us
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Conference papers on the topic "Bhattacharyya coefficients"

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Huiying Cao, Jiangzhou Deng, Huifang Guo, Bo He, and Yong Wang. "An improved recommendation algorithm based on Bhattacharyya Coefficient." In 2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA). IEEE, 2016. http://dx.doi.org/10.1109/ickea.2016.7803027.

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Prasad, J. Vijay, and Kaushik Ghosh. "Identification of fault specific key variables using a modified Bhattacharyya coefficient." In 2014 American Control Conference - ACC 2014. IEEE, 2014. http://dx.doi.org/10.1109/acc.2014.6859028.

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Khalid, M. S., and M. B. Malik. "Biased nature of Bhattacharyya coefficient in correlation of gray-scale objects." In Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis. IEEE, 2005. http://dx.doi.org/10.1109/ispa.2005.195411.

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Zheng, Yifeng, and Jianxiu Jin. "A novel image scrambling degree blind evaluation scheme based on Bhattacharyya coefficient." In 2014 9th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP). IEEE, 2014. http://dx.doi.org/10.1109/csndsp.2014.6923817.

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Zeng, Mingbin, Zichao Wei, Hu He, and Xu Yang. "Application of Improved Bhattacharyya Coefficient Based Multi-Object Detection and Tracking Integrated Strategy." In 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC). IEEE, 2018. http://dx.doi.org/10.1109/icnidc.2018.8525809.

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Fan, Shuchen, Yuhe Sun, Penglang Shui, and Zejun Zhang. "SAR image edge detection via directional Bhattacharyya coefficient with its application on image segmentation." In Eleventh International Conference on Machine Vision, edited by Dmitry P. Nikolaev, Petia Radeva, Antanas Verikas, and Jianhong Zhou. SPIE, 2019. http://dx.doi.org/10.1117/12.2522837.

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An-Qi, Huang, Hou Zhi-Qiang, Yu Wang-Sheng, and Liu Xiang. "A Visual Object Tracking Method Based on Improved Bhattacharyya Coefficient and Model Update Strategy." In 2014 Fifth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA). IEEE, 2014. http://dx.doi.org/10.1109/isdea.2014.30.

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Zhang, Hongzhe, Wei Cong, Lin Li, et al. "Voltage Waveform Comparison Longitudinal Protection Method Based on Improved Bhattacharyya Coefficient for AC / DC Hybrid Power Grid." In 2023 3rd International Conference on Electrical Engineering and Control Science (IC2ECS). IEEE, 2023. http://dx.doi.org/10.1109/ic2ecs60824.2023.10493678.

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Buscaldi, Davide, Jorge García Flores, Joseph Le Roux, Nadi Tomeh, and Belém Priego Sanchez. "LIPN: Introducing a new Geographical Context Similarity Measure and a Statistical Similarity Measure based on the Bhattacharyya coefficient." In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/s14-2069.

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Yarbrough, Allan W., Michael J. Mendenhall, and Richard K. Martin. "The effects of atmospheric mis-estimation on hyperspectral-based adaptive matched filter target detection as measured by the Bhattacharyya coefficient." In 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2010. http://dx.doi.org/10.1109/whispers.2010.5594960.

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