The Experts below are selected from a list of 3318 Experts worldwide ranked by ideXlab platform
P Mathieu - One of the best experts on this subject based on the ideXlab platform.
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pyramidal lattice vector quantization for multiscale image coding
IEEE Transactions on Image Processing, 1994Co-Authors: Michel Barlaud, Patrick Sole, Marc Antonini, T Gaidon, P MathieuAbstract:Introduces a new image coding scheme using lattice vector quantization. The proposed method involves two steps: Biorthogonal Wavelet transform of the image, and lattice vector quantization of Wavelet coefficients. In order to obtain a compromise between minimum distortion and bit rate, we must truncate and scale the lattice suitably. To meet this goal, we need to know how many lattice points lie within the truncated area. We investigate the case of Laplacian sources where surfaces of equal probability are spheres for the L/sup 1/ metric (pyramids) for arbitrary lattices. We give explicit generating functions for the codebook sizes for the most useful lattices like Z/sup n/, D/sub n/, E/sub s/, /spl and//sub 16/. >
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a pyramidal scheme for lattice vector quantization of Wavelet transform coefficients applied to image coding
International Conference on Acoustics Speech and Signal Processing, 1992Co-Authors: Patrick Sole, P MathieuAbstract:The image coding scheme involves two steps: Biorthogonal Wavelet transform of the image and pyramidal lattice vector quantization of Wavelet coefficients. In order to obtain a compromise between minimum distortion and bit rate, one must truncate and scale the lattice suitably. To meet this goal, one needs to know how many lattice points lie within the truncated area. The case of Laplacian sources where surfaces of equal probability are spheres for the L/sub 1/ metric (pyramids) for arbitrary lattices is investigated. Explicit generating functions for the codebook sizes of the most useful lattices are given. >
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Wavelet transform and lattice vector quantization for image sequence coding
Signal Processing#R##N#Theories and Applications, 1992Co-Authors: T Gaidon, P MathieuAbstract:This paper describes a new method for image sequence coding. The sequence is spatialy decomposed using a quincunx Biorthogonal Wavelet transform and temporally using recursive Biorthogonal Wavelet transform. The Wavelet coefficients are vector quantized using adapted, scaled, and truncated lattices, in order to obtain the best trade-off between distortion and entropy, and also to take into account the human psychovisual system. In this scheme every step uses fast algorithm.
Rajendra U Acharya - One of the best experts on this subject based on the ideXlab platform.
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a new method to identify coronary artery disease with ecg signals and time frequency concentrated antisymmetric Biorthogonal Wavelet filter bank
Pattern Recognition Letters, 2019Co-Authors: Manish Sharma, Rajendra U AcharyaAbstract:Abstract The extreme deposition of plaque in the inner walls of arteries causes coronary artery disease (CAD). It can be detected by the morphological changes in the electrocardiogram (ECG) signals. The manual analysis of ECG signals is inefficient, as it is laborious and vulnerable to errors. CAD diagnosis tests also demand physical work from patients, which may not be fulfilled as readily by the elderly and physically challenged patients. The use of automated coronary artery disease diagnosis system can help to overcome the aforementioned problems. In this study, ECG segments of different durations (2s and 5s) are employed for the analysis of CAD. We propose the use of a recently developed optimally time-frequency concentrated (OTFC) even-length Biorthogonal Wavelet filter bank (BWFB) for automatically identifying CAD. The fuzzy entropy (FE) and log-energy (LogE) were extracted from the OTFC decomposed coefficients. Using the Gaussian support vector machine (GSVM) classifier, an average classification accuracy of 99.53% is achieved with 10-fold cross-validation (CV). The average sensitivity and specificity obtained are 98.64% & 99.70%, respectively with the Matthews correlation coefficient(MCC) of 0.983. The classification performance of the proposed model has surpassed most of the state-of-art models. The method presented in this paper can be of great help to clinicians and cardiologists to validate their diagnosis. Our developed model is economical, robust and accurate in diagnosing the CAD.
Shuihua Wang - One of the best experts on this subject based on the ideXlab platform.
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Multiple Sclerosis Recognition by Biorthogonal Wavelet Features and Fitness-Scaled Adaptive Genetic Algorithm
'Frontiers Media SA', 2021Co-Authors: Shuihua Wang, Xianwei Jiang, Yudong ZhangAbstract:Aim: Multiple sclerosis (MS) is a disease, which can affect the brain and/or spinal cord, leading to a wide range of potential symptoms. This method aims to propose a novel MS recognition method.Methods: First, the bior4.4 Wavelet is used to extract multiscale coefficients. Second, three types of Biorthogonal Wavelet features are proposed and calculated. Third, fitness-scaled adaptive genetic algorithm (FAGA)—a combination of standard genetic algorithm, adaptive mechanism, and power-rank fitness scaling—is harnessed as the optimization algorithm. Fourth, multiple-way data augmentation is utilized on the training set under the setting of 10 runs of 10-fold cross-validation. Our method is abbreviated as BWF-FAGA.Results: Our method achieves a sensitivity of 98.00 ± 0.95%, a specificity of 97.78 ± 0.95%, and an accuracy of 97.89 ± 0.94%. The area under the curve of our method is 0.9876.Conclusion: The results show that the proposed BWF-FAGA method is better than 10 state-of-the-art MS recognition methods, including eight artificial intelligence-based methods, and two deep learning-based methods
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facial emotion recognition based on Biorthogonal Wavelet entropy fuzzy support vector machine and stratified cross validation
IEEE Access, 2016Co-Authors: Yudong Zhang, Preetha Phillips, Zhangjing Yang, Huimin Lu, Xingxing Zhou, Shuihua WangAbstract:Emotion recognition represents the position and motion of facial muscles. It contributes significantly in many fields. Current approaches have not obtained good results. This paper aimed to propose a new emotion recognition system based on facial expression images. We enrolled 20 subjects and let each subject pose seven different emotions: happy, sadness, surprise, anger, disgust, fear, and neutral. Afterward, we employed Biorthogonal Wavelet entropy to extract multiscale features, and used fuzzy multiclass support vector machine to be the classifier. The stratified cross validation was employed as a strict validation model. The statistical analysis showed our method achieved an overall accuracy of 96.77±0.10%. Besides, our method is superior to three state-of-the-art methods. In all, this proposed method is efficient.
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multiple sclerosis detection based on Biorthogonal Wavelet transform rbf kernel principal component analysis and logistic regression
IEEE Access, 2016Co-Authors: Shuihua Wang, Tianming Zha, Yi Che, Yi Zhang, Ming Yang, Haina Wang, I Liu, Preetha PhillipsAbstract:To detect multiple sclerosis (MS) diseases early, we proposed a novel method on the hardware of magnetic resonance imaging, and on the software of three successful methods: Biorthogonal Wavelet transform, kernel principal component analysis, and logistic regression. The materials were 676 MR slices containing plaques from 38 MS patients, and 880 MR slices from 34 healthy controls. The statistical analysis showed our method achieved a sensitivity of 97.12±.14%, a specificity of 98.25±0.16%, and an accuracy of 97.76±0.10%. Our method is superior to five state-of-the-art approaches in MS detection.
Meenakshi S Arya - One of the best experts on this subject based on the ideXlab platform.
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Offline Handwritten Signature based Blind Biometric Watermarking and Authentication Technique using
2016Co-Authors: Biorthogonal Wavelet Transform, Vandana S Inamdar, Priti P Rege, Meenakshi S AryaAbstract:A method for establishing the identity of an individual is essential in all transactions whether they are commercial or personal. The ability to establish identity with certainty can prevent fraud or forgery. In the midst of an electronic revolution, this remains a major concern in ecommerce, telecommunications, healthcare, and security. In this paper, we present a novel method for biometric image watermarking using the Biorthogonal Wavelet transform and authentication of the recovered signature from the image data. In proposed approach the offline signature, which is a biometric characteristics of owner is embedded in second level detailed coefficients of discrete Wavelet transform of cover image. The novelty of the proposed scheme is that, it also goes a step further wherein it extracts the features of recovered signatures and does the template matching with features of signature data base
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Offline Handwritten Signature based Blind Biometric Watermarking and Authetication Technique using
2011Co-Authors: Biorthogonal Wavelet Transform, Vandana S Inamdar, Priti P Rege, Meenakshi S AryaAbstract:A method for establishing the identity of an individual is essential in all transactions whether they are commercial or personal. The ability to establish identity with certainty can prevent fraud or forgery. In the midst of an electronic revolution, this remains a major concern in ecommerce, telecommunications, healthcare, and security. In this paper, we present a novel method for biometric image watermarking using the Biorthogonal Wavelet transform and authentication of the recovered signature from the image data. In proposed approach the offline signature, which is a biometric characteristics of owner is embedded in second level detailed coefficients of discrete Wavelet transform of cover image. The novelty of the proposed scheme is that, it also goes a step further wherein it extracts the features of recovered signatures and does the template matching with features of signature data base
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offline handwritten signature based blind biometric watermarking and authetication technique using Biorthogonal Wavelet transform
International Journal of Computer Applications, 2010Co-Authors: Vandana S Inamdar, Priti P Rege, Meenakshi S AryaAbstract:A method for establishing the identity of an individual is essential in all transactions whether they are commercial or personal. The ability to establish identity with certainty can prevent fraud or forgery. In the midst of an electronic revolution, this remains a major concern in ecommerce, telecommunications, healthcare, and security. In this paper, we present a novel method for biometric image watermarking using the Biorthogonal Wavelet transform and authentication of the recovered signature from the image data. In proposed approach the offline signature, which is a biometric characteristics of owner is embedded in second level detailed coefficients of discrete Wavelet transform of cover image. The novelty of the proposed scheme is that, it also goes a step further wherein it extracts the features of recovered signatures and does the template matching with features of signature data base.
Preetha Phillips - One of the best experts on this subject based on the ideXlab platform.
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facial emotion recognition based on Biorthogonal Wavelet entropy fuzzy support vector machine and stratified cross validation
IEEE Access, 2016Co-Authors: Yudong Zhang, Preetha Phillips, Zhangjing Yang, Huimin Lu, Xingxing Zhou, Shuihua WangAbstract:Emotion recognition represents the position and motion of facial muscles. It contributes significantly in many fields. Current approaches have not obtained good results. This paper aimed to propose a new emotion recognition system based on facial expression images. We enrolled 20 subjects and let each subject pose seven different emotions: happy, sadness, surprise, anger, disgust, fear, and neutral. Afterward, we employed Biorthogonal Wavelet entropy to extract multiscale features, and used fuzzy multiclass support vector machine to be the classifier. The stratified cross validation was employed as a strict validation model. The statistical analysis showed our method achieved an overall accuracy of 96.77±0.10%. Besides, our method is superior to three state-of-the-art methods. In all, this proposed method is efficient.
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multiple sclerosis detection based on Biorthogonal Wavelet transform rbf kernel principal component analysis and logistic regression
IEEE Access, 2016Co-Authors: Shuihua Wang, Tianming Zha, Yi Che, Yi Zhang, Ming Yang, Haina Wang, I Liu, Preetha PhillipsAbstract:To detect multiple sclerosis (MS) diseases early, we proposed a novel method on the hardware of magnetic resonance imaging, and on the software of three successful methods: Biorthogonal Wavelet transform, kernel principal component analysis, and logistic regression. The materials were 676 MR slices containing plaques from 38 MS patients, and 880 MR slices from 34 healthy controls. The statistical analysis showed our method achieved a sensitivity of 97.12±.14%, a specificity of 98.25±0.16%, and an accuracy of 97.76±0.10%. Our method is superior to five state-of-the-art approaches in MS detection.