A Hierarchical Bayesian Model for Frame Representation

In many signal processing problems, it may be fruitful to represent the signal under study in a frame. If a probabilistic approach is adopted, it becomes then necessary to estimate the hyper-parameters characterizing the probability distribution of the frame coefficients. This problem is difficult since in general the frame synthesis operator is not bijective. Consequently, the frame coefficients are not directly observable. This paper introduces a hierarchical Bayesian model for frame representation. The posterior distribution of the frame coefficients and model hyper-parameters is derived. Hybrid Markov Chain Monte Carlo algorithms are subsequently proposed to sample from this posterior distribution. The generated samples are then exploited to estimate the hyper-parameters and the frame coefficients of the target signal. Validation experiments show that the proposed algorithms provide an accurate estimation of the frame coefficients and hyper-parameters. Application to practical problems of image denoising show the impact of the resulting Bayesian estimation on the recovered signal quality

Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers

Human activity recognition (HAR) by wearable sensor devices embedded in the Internet of things (IOT) can play a significant role in remote health monitoring and emergency notification, to provide healthcare of higher standards. The purpose of this study is to investigate a human activity recognition method of accrued decision accuracy and speed of execution to be applicable in healthcare. This method classifies wearable sensor acceleration time series data of human movement using efficient classifier combination of feature engineering-based and feature learning-based data representation. Leave-one-subject-out cross-validation of the method with data acquired from 44 subjects wearing a single waist-worn accelerometer on a smart textile, and engaged in a variety of 10 activities, yields an average recognition rate of 90%, performing significantly better than individual classifiers. The method easily accommodates functional and computational parallelization to bring execution time significantly down

Building Robust Wavelet Estimators for Multicomponent Images Using Stein's Principle

International audienc

A SURE approach for image deconvolution in an orthonormal wavelet basis

International audienc

An Effective 3D ResNet Architecture for Stereo Image Retrieval

While recent stereo images retrieval techniques have been developed based mainly on statistical approaches,this work aims to investigate deep learning ones. More precisely, our contribution consists in designing a two-branch neural networks to extract deep features from the stereo pair. In this respect, a 3D residual networkarchitecture is first employed to exploit the high correlation existing in the stereo pair. This 3D model is thencombined with a 2D one applied to the disparity maps, resulting in deep feature representations of the textureinformation as well as the depth one. Our experiments, carried out on a large scale stereo image dataset, haveshown the good performance of the proposed approach compared to the state-of-the-art methods

Building Robust Wavelet Estimators for Multicomponent Images Using Stein's Principle

International audienc

Multichannel image deconvolution in the wavelet transform domain

electronic version (5 pp.)International audienc

Adaptive lifting schemes using variable-size block segmentation

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Depth-based color stereo images retrieval using joint multivariate statistical models

International audienceThe growing interest in using the three dimensional information in various application fields has led to the generation of huge color stereo image databases. As a result, it becomes necessary to design efficient content-based image retrieval systems well adapted to the indexing of such large databases. To this end, we propose in this paper different statistical-based retrieval approaches where the associated estimated model parameters are considered as a feature vector in the indexing process. More precisely, the Gaussian copula based multivariate Generalized Gaussian model will be used to capture the different correlations existing in color stereo images. While the first strategy aims at exploiting the cross-view as well as the cross-color channel redundancies, the second one resorts to a more general joint statistical model exploiting the correlation between the texture and depth information. Experimental results, performed on various datasets, confirm the benefits that can be drawn from the proposed approaches