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Journal articles on the topic 'Distance de Bhattacharyya'

Author: Grafiati

Published: 2 February 2023

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1

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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2

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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3

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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4

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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5

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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7

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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8

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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9

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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11

Ahn, Chan-Shik, and Sang-Yeob Oh. "Phoneme Similarity Error Correction System using Bhattacharyya Distance Measurement Method." Journal of the Korea Society of Computer and Information 15, no. 6 (June 30, 2010): 73–80. http://dx.doi.org/10.9708/jksci.2010.15.6.073.

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Carvalho, Naiallen Carolyne Rodrigues Lima, Leonardo Sant’Anna Bins, and Sidnei João Siqueira Sant’Anna. "Analysis of Stochastic Distances and Wishart Mixture Models Applied on PolSAR Images." Remote Sensing 11, no. 24 (December 12, 2019): 2994. http://dx.doi.org/10.3390/rs11242994.

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This paper address unsupervised classification strategies applied to Polarimetric Synthetic Aperture Radar (PolSAR) images. We analyze the performance of complex Wishart distribution, which is a widely used model for multi-look PolSAR images, and the robustness of five stochastic distances (Bhattacharyya, Kullback-Leibler, Rényi, Hellinger and Chi-square) between Wishart distributions. Two unsupervised classification strategies were chosen: the Stochastic Clustering (SC) algorithm, which is based on the K-means algorithm but uses stochastic distance as the similarity metric, and the Expectation-Maximization (EM) algorithm for Wishart Mixture Model. With the aim of assessing the performance of all algorithms presented here, we performed a Monte Carlo simulation over a set of simulated PolSAR images. A second experiment was conducted using the study area of Tapajós National Forest and the surrounding area, in Brazilian Amazon Forest. The PolSAR images were obtained by the satellite PALSAR. The results, in both experiments, suggest that the EM algorithm and the SC with Hellinger and the SC with Bhattacharyya distance provide a better classification performance. We also analyze the initialization problem for SC and EM algorithms, and we demonstrate how the initial centroid choice influences the final classification result.

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13

Ma, Qian, Lei Cheng, and Wen Xu. "Experimental verification of the minimum Bhattacharyya distance-based source bearing estimator." JASA Express Letters 2, no. 6 (June 2022): 064801. http://dx.doi.org/10.1121/10.0011574.

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The minimum Bhattacharyya distance estimator (MBDE) for acoustic source bearing estimation was recently proposed as a promising tool to tackle the model mismatch challenge. Previous work lacks the experimental studies using the real-life array system data. Towards the practical use of MBDE, this letter proposes two practical schemes to address the unavailability of noise and source powers, presents the additional simulation results on resolving the closely spaced sources with different strengths, and performs real-world experimental studies using the SwellEx-96 data. Simulation and experimental results demonstrate the superior performance of the MBDE over other competitors.

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Baidari, Ishwar, and Nagaraj Honnikoll. "Bhattacharyya distance based concept drift detection method for evolving data stream." Expert Systems with Applications 183 (November 2021): 115303. http://dx.doi.org/10.1016/j.eswa.2021.115303.

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Ma, Qian, Wen Xu, and Yue Zhou. "Statistically robust estimation of source bearing via minimizing the Bhattacharyya distance." Journal of the Acoustical Society of America 151, no. 3 (March 2022): 1695–709. http://dx.doi.org/10.1121/10.0009677.

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Source bearing estimation is a common technique in acoustic array processing. Many methods have been developed and most of them exploit some underlying statistical model. When applied to a practical system, the robustness to model mismatch is of major concern. Traditional adaptive methods, such as the minimum power distortionless response processor, are notoriously known for their sensitivity to model mismatch. In this paper, a parameter estimator is developed via the minimum Bhattacharyya distance estimator (MBDE), which provides a measure of the divergence between the assumed and true probability distributions and is, thus, capable of statistically matching. Under a Gaussian random signal model typical of source bearing estimation, the MBDE is derived in terms of the data-based and modeled covariance matrices without involving matrix inversion. The performance of the MBDE, regarding the robustness and resolution, is analyzed in comparison with some of the existing methods. A connection with the Weiss-Weinstein bound is also discussed, which gives the MBDE an interpretation of closely approaching a large-error performance bound. Theoretical analysis and simulations of bearing estimation using a uniform linear array show that the proposed method owns a considerable resolution comparable to an adaptive method while being robust against statistical mismatch, including covariance mismatch caused by snapshot deficiency and/or noise model mismatch.

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Straka, Ondřej, and Miroslav Šimandl. "USING THE BHATTACHARYYA DISTANCE IN FUNCTIONAL SAMPLING DENSITY OF PARTICLE FILTER." IFAC Proceedings Volumes 38, no. 1 (2005): 1006–11. http://dx.doi.org/10.3182/20050703-6-cz-1902.00169.

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Bi, Sifeng, Matteo Broggi, Pengfei Wei, and Michael Beer. "The Bhattacharyya distance: Enriching the P-box in stochastic sensitivity analysis." Mechanical Systems and Signal Processing 129 (August 2019): 265–81. http://dx.doi.org/10.1016/j.ymssp.2019.04.035.

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Chang Huai You, Kong Aik Lee, and Haizhou Li. "GMM-SVM Kernel With a Bhattacharyya-Based Distance for Speaker Recognition." IEEE Transactions on Audio, Speech, and Language Processing 18, no. 6 (August 2010): 1300–1312. http://dx.doi.org/10.1109/tasl.2009.2032950.

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19

De Oliveira Moraes, Denis Altieri, and Victor Haertel. "Estabilidade de Classificadores de Decisão em Árvore Binária para Dados Imagem em Alta Dimensão." Revista de Informática Teórica e Aplicada 15, no. 2 (December 12, 2008): 27–42. http://dx.doi.org/10.22456/2175-2745.6999.

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This paper deals with the problem of classifying high-dimensional image data image data using a multiple stage classifier structured as a binary tree. The aim here consists in finding the optimal structure for the binary tree in the sense of achieving a stable accuracy. The advantage presented by a multiple stage classifier lies on the fact that only a sub-set of classes is considered at each stage, allowing a better selection of the features to be used at each node. The binary tree is a particular case of a tree structured classifier, on which only two classes are considered at each node. This peculiarity makes possible the direct use of statistical distances for feature reduction (selection or extraction). In this study the criterion used for feature reduction at each node consists in optimizing the Bhattacharyya distance separating both classes in the node. The optimization of Bhattacharyya distance was based on the covariance matrices. Once the final set of features is obtained at each particular node, the classification is performed using the Gaussian Maximum Likelihood decision rule. Tests were performed using high-dimensional image data collected by the sensor system AVIRIS covering a test area. The criteria to evaluate the performance of the classifiers are: the final accuracy yielded by the classifier, its stability, and the required computer time.

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20

Aggoun, Lakhdar, and Yahya Chetouani. "Fault detection strategy combining NARMAX model and Bhattacharyya distance for process monitoring." Journal of the Franklin Institute 358, no. 3 (February 2021): 2212–28. http://dx.doi.org/10.1016/j.jfranklin.2021.01.001.

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21

GHAEDI, Amir, Moein MOEINI-AGHTAIE, and Abuzar GHAFFARI. "Detection of online PD signals in XLPE cables using the Bhattacharyya distance." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 24 (2016): 3552–63. http://dx.doi.org/10.3906/elk-1410-10.

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22

Xu, He, Chunyue Ding, Peng Li, and Yimu Ji. "An Active Learning Algorithm Based on the Distribution Principle of Bhattacharyya Distance." Mathematics 10, no. 11 (June 4, 2022): 1927. http://dx.doi.org/10.3390/math10111927.

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Active learning is a method that can actively select examples with much information from a large number of unlabeled samples to query labeled by experts, so as to obtain a high-precision classifier with a small number of samples. Most of the current research uses the basic principles to optimize the classifier at each iteration, but the batch query with the largest amount of information in each round does not represent the overall distribution of the sample, that is, it may fall into partial optimization and ignore the whole, which will may affect or reduce its accuracy. In order to solve this problem, a special distance measurement method—Bhattacharyya Distance—is used in this paper. By using this distance and designing a new set of query decision logic, we can improve the accuracy of the model. Our method embodies the query of the samples with the most representative distribution and the largest amount of information to realize the classification task based on a small number of samples. We perform theoretical proofs and experimental analysis. Finally, we use different data sets and compare them with other classification algorithms to evaluate the performance and efficiency of our algorithm.

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23

Oh, Sang-Yeob. "Improving Phoneme Recognition based on Gaussian Model using Bhattacharyya Distance Measurement Method." Journal of Korea Multimedia Society 14, no. 1 (January 31, 2011): 85–93. http://dx.doi.org/10.9717/kmms.2011.14.1.085.

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24

S., Prasanth Vaidya, and Chandra Mouli P. V. S. S. R. "Adaptive, robust and blind digital watermarking using Bhattacharyya distance and bit manipulation." Multimedia Tools and Applications 77, no. 5 (March 1, 2017): 5609–35. http://dx.doi.org/10.1007/s11042-017-4476-5.

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25

Wang, Li, Dongming Deng, and Zhongliang He. "Information-Based Sensor Control for Multitask Planning via Multi-Bernoulli Filter." Security and Communication Networks 2022 (July 8, 2022): 1–13. http://dx.doi.org/10.1155/2022/3536618.

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In multitarget tracking, the issue of sensor control is a challenging problem in theoretical analysis and calculation. In this paper, we study the sensor control strategies for multitask planning based on information criteria and propose two sensor control strategies according to the two different task objectives. Initially, we propose a sensor control strategy to improve the overall multitarget tracking performance within the partially observed Markov decision process (POMDP) framework, where the reward function is calculated by the Bhattacharyya distance between the prior and the posterior multitarget densities. In this strategy, we present a target-oriented multi-Bernoulli (TOMB) particle sampling method to approximate the multitarget density and then derive the solution of Bhattacharyya distance in detail. Subsequently, as another important contribution of this paper, we propose a threat-based sensor control strategy, which is still solved under the information theory where the goal is to prioritize multiple threat targets and then to track preferentially the maximum threat target. These strategies are finally used to optimize the sensor trajectory for range-bearing multitarget tracking.

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Tang, Jiaxin, Jianda Cheng, Deliang Xiang, and Canbin Hu. "Large-Difference-Scale Target Detection Using a Revised Bhattacharyya Distance in SAR Images." IEEE Geoscience and Remote Sensing Letters 19 (2022): 1–5. http://dx.doi.org/10.1109/lgrs.2022.3161931.

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27

Alesaadi, Alireza, Amir Ghaedi, Aboozar Ghaffari, and Vahid Parvin. "De-noising of Online PD Signals in Power Transformers Using the Bhattacharyya Distance." Trends in Applied Sciences Research 7, no. 10 (October 1, 2012): 813–28. http://dx.doi.org/10.3923/tasr.2012.813.828.

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Yifei Lou, Andrei Irimia, Patricio A. Vela, Micah C. Chambers, John D. Van Horn, Paul M. Vespa, and Allen R. Tannenbaum. "Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes via the Bhattacharyya Distance." IEEE Transactions on Biomedical Engineering 60, no. 9 (September 2013): 2511–20. http://dx.doi.org/10.1109/tbme.2013.2259625.

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Goudail, François, Philippe Réfrégier, and Guillaume Delyon. "Bhattacharyya distance as a contrast parameter for statistical processing of noisy optical images." Journal of the Optical Society of America A 21, no. 7 (July 1, 2004): 1231. http://dx.doi.org/10.1364/josaa.21.001231.

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Zhang, Shaoqiang, Linjuan Xie, Yaxuan Cui, Benjamin R. Carone, and Yong Chen. "Detecting Fear-Memory-Related Genes from Neuronal scRNA-seq Data by Diverse Distributions and Bhattacharyya Distance." Biomolecules 12, no. 8 (August 17, 2022): 1130. http://dx.doi.org/10.3390/biom12081130.

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The detection of differentially expressed genes (DEGs) is one of most important computational challenges in the analysis of single-cell RNA sequencing (scRNA-seq) data. However, due to the high heterogeneity and dropout noise inherent in scRNAseq data, challenges in detecting DEGs exist when using a single distribution of gene expression levels, leaving much room to improve the precision and robustness of current DEG detection methods. Here, we propose the use of a new method, DEGman, which utilizes several possible diverse distributions in combination with Bhattacharyya distance. DEGman can automatically select the best-fitting distributions of gene expression levels, and then detect DEGs by permutation testing of Bhattacharyya distances of the selected distributions from two cell groups. Compared with several popular DEG analysis tools on both large-scale simulation data and real scRNA-seq data, DEGman shows an overall improvement in the balance of sensitivity and precision. We applied DEGman to scRNA-seq data of TRAP; Ai14 mouse neurons to detect fear-memory-related genes that are significantly differentially expressed in neurons with and without fear memory. DEGman detected well-known fear-memory-related genes and many novel candidates. Interestingly, we found 25 DEGs in common in five neuron clusters that are functionally enriched for synaptic vesicles, indicating that the coupled dynamics of synaptic vesicles across in neurons plays a critical role in remote memory formation. The proposed method leverages the advantage of the use of diverse distributions in DEG analysis, exhibiting better performance in analyzing composite scRNA-seq datasets in real applications.

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Zhao, Xin, and Xing Li. "Band Selection Oriented to Easy-Confused Objects for Classification of Hyperspectral Imagery." Key Engineering Materials 500 (January 2012): 355–61. http://dx.doi.org/10.4028/www.scientific.net/kem.500.355.

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As a dimensionality reduction technique, band selection is an importance pre-processing step for classifiers. In this paper, a band selection approach oriented to easy-confused objects for classification of hyper spectral imagery is presented. Firstly, an Objects Confusion Index (OCI) is established to ascertain the easy-confused objects. Then the two band selection schemes, that are two-class mode and multi-class mode, are designed by adopting Bhattacharyya distance as class reparability measure.

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Begum, Nasra, Noor Badshah, Lavdie Rada, Adela Ademaj, Muniba Ashfaq, and Hadia Atta. "An improved multi-modal joint segmentation and registration model based on Bhattacharyya distance measure." Alexandria Engineering Journal 61, no. 12 (December 2022): 12353–65. http://dx.doi.org/10.1016/j.aej.2022.06.018.

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Shah, Maqsood Hussain, and Xiaoyu Dang. "Novel Feature Selection Method Using Bhattacharyya Distance for Neural Networks Based Automatic Modulation Classification." IEEE Signal Processing Letters 27 (2020): 106–10. http://dx.doi.org/10.1109/lsp.2019.2957924.

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Chang Huai You, Kong Aik Lee, and Haizhou Li. "An SVM Kernel With GMM-Supervector Based on the Bhattacharyya Distance for Speaker Recognition." IEEE Signal Processing Letters 16, no. 1 (January 2009): 49–52. http://dx.doi.org/10.1109/lsp.2008.2006711.

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Marković, Milan, Milan Milosavljević, and Branko Kovačević. "On evaluating A class of frame-based nonstationary pattern recognition methods using bhattacharyya distance." Circuits Systems and Signal Processing 19, no. 5 (September 2000): 467–85. http://dx.doi.org/10.1007/bf01196159.

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36

Chen, Guangyuan, Guoliang Lu, Zhaohong Xie, and Wei Shang. "Anomaly Detection in EEG Signals: A Case Study on Similarity Measure." Computational Intelligence and Neuroscience 2020 (January 10, 2020): 1–16. http://dx.doi.org/10.1155/2020/6925107.

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Motivation. Anomaly EEG detection is a long-standing problem in analysis of EEG signals. The basic premise of this problem is consideration of the similarity between two nonstationary EEG recordings. A well-established scheme is based on sequence matching, typically including three steps: feature extraction, similarity measure, and decision-making. Current approaches mainly focus on EEG feature extraction and decision-making, and few of them involve the similarity measure/quantification. Generally, to design an appropriate similarity metric, that is compatible with the considered problem/data, is also an important issue in the design of such detection systems. It is however impossible to directly apply those existing metrics to anomaly EEG detection without any consideration of domain specificity. Methodology. The main objective of this work is to investigate the impacts of different similarity metrics on anomaly EEG detection. A few metrics that are potentially available for the EEG analysis have been collected from other areas by a careful review of related works. The so-called power spectrum is extracted as features of EEG signals, and a null hypothesis testing is employed to make the final decision. Two indicators have been used to evaluate the detection performance. One is to reflect the level of measured similarity between two compared EEG signals, and the other is to quantify the detection accuracy. Results. Experiments were conducted on two data sets, respectively. The results demonstrate the positive impacts of different similarity metrics on anomaly EEG detection. The Hellinger distance (HD) and Bhattacharyya distance (BD) metrics show excellent performances: an accuracy of 0.9167 for our data set and an accuracy of 0.9667 for the Bern-Barcelona EEG data set. Both of HD and BD metrics are constructed based on the Bhattacharyya coefficient, implying the priority of the Bhattacharyya coefficient when dealing with the highly noisy EEG signals. In future work, we will exploit an integrated metric that combines HD and BD for the similarity measure of EEG signals.

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Sarno, Riyanarto, Dedy Rahman Wijaya, and Muhammad Nezar Mahardika. "Music fingerprinting based on bhattacharya distance for song and cover song recognition." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (April 1, 2019): 1036. http://dx.doi.org/10.11591/ijece.v9i2.pp1036-1044.

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People often have trouble recognizing a song especially, if the song is sung by a not original artist which is called cover song. Hence, an identification system might be used to help recognize a song or to detect copyright violation. In this study, we try to recognize a song and a cover song by using the fingerprint of the song represented by features extracted from MPEG-7. The fingerprint of the song is represented by Audio Signature Type. Moreover, the fingerprint of the cover song is represented by Audio Spectrum Flatness and Audio Spectrum Projection. Furthermore, we propose a sliding algorithm and k-Nearest Neighbor (k-NN) with Bhattacharyya distance for song recognition and cover song recognition. The results of this experiment show that the proposed fingerprint technique has an accuracy of 100% for song recognition and an accuracy of 85.3% for cover song recognition.

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Yu, Chun Mei, and Sheng Bo Yang. "A Novel Feature Selection Method for Process Fault Diagnosis." Applied Mechanics and Materials 427-429 (September 2013): 2045–49. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2045.

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To increase fault classification performance and reduce computational complexity,the feature selection process has been used for fault diagnosis.In this paper, we proposed a sparse representation based feature selection method and gave detailed procedure of the algorithm. Traditional selecting methods based on wavelet package decomposition and Bhattacharyya distance methods,and sparse methods, including sparse representation classifier, sparsity preserving projection and sparse principal component analysis,were compared to the proposed method.Simulations showed the proposed selecting method gave better performance on fault diagnosis with Tennessee Eastman Process data.

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Rajput, Savita, Apurva Ware, Karan Umredkar, and Prof Jaya Jeshwani. "Digital Watermarking Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2081–85. http://dx.doi.org/10.22214/ijraset.2022.40991.

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Abstract: Digital watermarking is a technique used for the information of the images that provides security for the confidentiality. The repetitions of the multimedia objects (i.e. audio, video, text, etc.) have been protected by some of the developed digital watermarking techniques. Digital Watermarking is the process of concealing messages in digital contents in order to verify the rightful owner of the copyright protection. In this paper we have proposed a method that would assist its users to embed a watermark to the cover image based on an adaptive approach in a much robust way while maintaining the quality of the cover image. The implementation of this algorithm is based upon cascading the features of DWT and PCA using Bhattacharyya distance and Kurtosis. PCA decompose and compress the watermark, which results in better PSNR and NCC values for the tested images. The proposed algorithm uses Bhattacharyya distance and Kurtosis to detect the scaling and embedding factors making it adaptive to the input image rather than providing constant value. Also, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the performance. Keywords: Digital watermarking, DWT-PCA, PSNR, Image denoising, convolutional neural network, DnCNN, residual learning, batch normalization.

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40

Rajput, Savita, Apurva Ware, Karan Umredkar, and Prof Jaya Jeshwani. "Digital Watermarking Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2081–85. http://dx.doi.org/10.22214/ijraset.2022.40991.

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Abstract: Digital watermarking is a technique used for the information of the images that provides security for the confidentiality. The repetitions of the multimedia objects (i.e. audio, video, text, etc.) have been protected by some of the developed digital watermarking techniques. Digital Watermarking is the process of concealing messages in digital contents in order to verify the rightful owner of the copyright protection. In this paper we have proposed a method that would assist its users to embed a watermark to the cover image based on an adaptive approach in a much robust way while maintaining the quality of the cover image. The implementation of this algorithm is based upon cascading the features of DWT and PCA using Bhattacharyya distance and Kurtosis. PCA decompose and compress the watermark, which results in better PSNR and NCC values for the tested images. The proposed algorithm uses Bhattacharyya distance and Kurtosis to detect the scaling and embedding factors making it adaptive to the input image rather than providing constant value. Also, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the performance. Keywords: Digital watermarking, DWT-PCA, PSNR, Image denoising, convolutional neural network, DnCNN, residual learning, batch normalization.

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Belghini, Naouar, Arsalae Zarghili, Jamal Kharroubi, and Aicha Majda. "Learning a Backpropagation Neural Network With Error Function Based on Bhattacharyya Distance for Face Recognition." International Journal of Image, Graphics and Signal Processing 4, no. 8 (August 1, 2012): 8–14. http://dx.doi.org/10.5815/ijigsp.2012.08.02.

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Fu, Yuanhua, and Zhiming He. "Bhattacharyya distance criterion based multibit quantizer design for cooperative spectrum sensing in cognitive radio networks." Wireless Networks 25, no. 5 (April 6, 2019): 2665–74. http://dx.doi.org/10.1007/s11276-019-01986-9.

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Bonny, Sarungbam, Yambem Jina Chanu, and Khumanthem Manglem Singh. "Speckle reduction of ultrasound medical images using Bhattacharyya distance in modified non-local mean filter." Signal, Image and Video Processing 13, no. 2 (August 31, 2018): 299–305. http://dx.doi.org/10.1007/s11760-018-1357-y.

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Cheng, Jianda, Deliang Xiang, Jiaxin Tang, Yanpeng Zheng, Dongdong Guan, and Bin Du. "Inshore Ship Detection in Large-Scale SAR Images Based on Saliency Enhancement and Bhattacharyya-like Distance." Remote Sensing 14, no. 12 (June 13, 2022): 2832. http://dx.doi.org/10.3390/rs14122832.

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Abstract:

While the detection of offshore ships in synthetic aperture radar (SAR) images has been widely studied, inshore ship detection remains a challenging task. Due to the influence of speckle noise and the high similarity between onshore buildings and inshore ships, the traditional methods are unable to achieve effective detection for inshore ships. To improve the detection performance of inshore ships, we propose a novel saliency enhancement algorithm based on the difference of anisotropic pyramid (DoAP). Considering the limitations of IoU in small-target detection, we design a detection framework based on the proposed Bhattacharyya-like distance (BLD). First, the anisotropic pyramid of the SAR image is constructed by a bilateral filter (BF). Then, the differences between the finest two scales and the coarsest two scales are used to generate the saliency map, which can be used to enhance ship pixels and suppress background clutter. Finally, the BLD is used to replace IoU in label assignment and non-maximum suppression to overcome the limitations of IoU for small-target detection. We embed the DoAP into the BLD-based detection framework to detect inshore ships in large-scale SAR images. The experimental results on the LS-SSDD-v1.0 dataset indicate that the proposed method outperforms the basic state-of-the-art detection methods.

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Sriram, T. N., and S. Yaser Samadi. "A robust sequential fixed-width confidence interval for count data based on Bhattacharyya-Hellinger distance estimator." Sequential Analysis 35, no. 1 (January 2, 2016): 84–107. http://dx.doi.org/10.1080/07474946.2016.1132061.

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Yuan, J. "Active contour driven by region-scalable fitting and local Bhattacharyya distance energies for ultrasound image segmentation." IET Image Processing 6, no. 8 (November 1, 2012): 1075–83. http://dx.doi.org/10.1049/iet-ipr.2012.0120.

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Khan, Rasul A. "A note on Hammersley's inequality for estimating the normal integer mean." International Journal of Mathematics and Mathematical Sciences 2003, no. 34 (2003): 2147–56. http://dx.doi.org/10.1155/s016117120320822x.

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Abstract:

LetX1,X2,…,Xnbe a random sample from a normalN(θ,σ2)distribution with an unknown meanθ=0,±1,±2,…. Hammersley (1950) proposed the maximum likelihood estimator (MLE)d=[X¯n], nearest integer to the sample mean, as an unbiased estimator ofθand extended the Cramér-Rao inequality. The Hammersley lower bound for the variance of any unbiased estimator ofθis significantly improved, and the asymptotic (asn→∞) limit of Fraser-Guttman-Bhattacharyya bounds is also determined. A limiting property of a suitable distance is used to give some plausible explanations why such bounds cannot be attained. An almost uniformly minimum variance unbiased (UMVU) like property ofdis exhibited.

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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 (October 30, 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 linear prediction (PLP) have also been used. These two methods closely resemble the human auditory system. These feature vectors are then trained using the LSTM neural network. Then the obtained models of different phonemes are compared with different statistical tools namely Bhattacharyya Distance and Mahalanobis Distance to investigate the nature of those acoustic features.

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Clara I., Rodríguez-Casado,, Monleón-Getino, Toni, Marta Cubedo, and Martín Ríos Alcolea. "A Priori Groups Based On Bhattacharyya Distance And Partitioning Around Medoids Algorithm (PAM) With Applications To Metagenomics." IOSR Journal of Mathematics 13, no. 03 (May 2017): 24–32. http://dx.doi.org/10.9790/5728-1303032432.

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Wang, Dongmei, Lijuan Zhu, Jikang Yue, Jingyi Lu, and Gongfa Li. "Denoising method of natural gas pipeline leakage signal based on empirical mode decomposition and improved Bhattacharyya distance." Engineering Research Express 3, no. 3 (August 24, 2021): 035030. http://dx.doi.org/10.1088/2631-8695/ac09d7.

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