In this paper, we examine an important problem in the context of image processing which is image ... more In this paper, we examine an important problem in the context of image processing which is image denoising. Although conventional Gaussian distributions have been widely used, they fail to fit the shape of heavy-tailed data produced by the presence of noise. In this paper, we propose an unsupervised algorithm based on finite mixtures of bounded generalized Gaussian distributions (BGGMD) to achieve smooth denoising results. The proposed framework has the flexibility to fit different shapes of observed data and bounded support data in the case of noisy images. Experimental results demonstrate that the proposed method has superior performance than some conventional approaches.
In this paper we propose to improve image and video sequences segmentation through the integratio... more In this paper we propose to improve image and video sequences segmentation through the integration of feature selection process into an unsupervised learning approach based on a finite mixture of bounded generalized Gaussian distributions (BGGMD). The proposed algorithm is less sensitive to over-segmentation, more flexible to data modeling and leading to better characterization and localization of object of interest in high-dimensional spaces since it is able to automatically reject irrelevant visual features. In order to determine adequately and automatically the number of regions in each image or frame, spatial information is incorporated as a prior information between neighboring pixels. Experimental results which are performed on a several real world images and videos demonstrate the effectiveness of the proposed framework with respect to other conventional Gaussian-based mixture models.
We present a novel method for color image segmentation based on an unsupervised learning model an... more We present a novel method for color image segmentation based on an unsupervised learning model and feature selection. Our focus here is to develop an expectation maximization algorithm based on a mixture of bounded generalized Gaussian model combined with a feature selection mechanism. The developed statistical model offers more flexibility in data modeling than the Gaussian distribution and the feature selection mechanism aims at eliminating irrelevant features and then improving the segmentation performances. Obtained results performed on a large dataset of real world color images confirm the effectiveness of the proposed approach.
Giovanni a. travaglino 9 , COVIDiStRESS Global Survey Consortium* & andreas Lieberoth 7 ✉ this N ... more Giovanni a. travaglino 9 , COVIDiStRESS Global Survey Consortium* & andreas Lieberoth 7 ✉ this N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey-an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available.
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)
In this paper, we address the problem of human activities and facial expression recognition by in... more In this paper, we address the problem of human activities and facial expression recognition by investigating the effectiveness of Bayesian inference methods. Indeed, a novel method termed as Bayesian learning for finite multivariate generalized Gaussian mixture model is developed. The multivariate generalized Gaussian distribution is encouraged by its ability to model a large range of data and its shape flexibility. Our main contribution in this work is to develop a Markov Chain Monte Carlo within Metropolis-Hastings algorithm for proposed generative model. In this research, we tackle also some key issues related to machine learning and pattern recognition such as the statistical model’s parameters estimation. We demonstrate the merits of our developed learning framework over two challenging applications that concern human activity recognition and facial expression recognition.
In this paper, we examine an important problem in the context of image processing which is image ... more In this paper, we examine an important problem in the context of image processing which is image denoising. Although conventional Gaussian distributions have been widely used, they fail to fit the shape of heavy-tailed data produced by the presence of noise. In this paper, we propose an unsupervised algorithm based on finite mixtures of bounded generalized Gaussian distributions (BGGMD) to achieve smooth denoising results. The proposed framework has the flexibility to fit different shapes of observed data and bounded support data in the case of noisy images. Experimental results demonstrate that the proposed method has superior performance than some conventional approaches.
We propose in this work to improve the tasks of image segmentation and modeling through an unsupe... more We propose in this work to improve the tasks of image segmentation and modeling through an unsupervised flexible learning approach. Our focus here is to develop an alternative mixture model based on a bounded generalized Gaussian distribution, which is less sensitive to over-segmentation and offers more flexibility in data modeling than the Gaussian distribution which is certainly not the best approximation for image segmentation. A maximum likelihood- (ML) based algorithm is applied for estimating the resulted model parameters. We investigate here the integration of both a spatial information (a prior information between neighboring pixels) and a minimum description length (MDL) principle into the model learning step in order to deal with the major problems of finding the optimal number of classes and also selecting the best model that describes accurately the dataset. Therefore, the proposed model has the advantage to maintain the balance between model complexity and goodness of f...
In this paper we propose to improve image and video sequences segmentation through the integratio... more In this paper we propose to improve image and video sequences segmentation through the integration of feature selection process into an unsupervised learning approach based on a finite mixture of bounded generalized Gaussian distributions (BGGMD). The proposed algorithm is less sensitive to over-segmentation, more flexible to data modeling and leading to better characterization and localization of object of interest in high-dimensional spaces since it is able to automatically reject irrelevant visual features. In order to determine adequately and automatically the number of regions in each image or frame, spatial information is incorporated as a prior information between neighboring pixels. Experimental results which are performed on a several real world images and videos demonstrate the effectiveness of the proposed framework with respect to other conventional Gaussian-based mixture models.
2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2018
We present a novel method for color image segmentation based on an unsupervised learning model an... more We present a novel method for color image segmentation based on an unsupervised learning model and feature selection. Our focus here is to develop an expectation maximization algorithm based on a mixture of bounded generalized Gaussian model combined with a feature selection mechanism. The developed statistical model offers more flexibility in data modeling than the Gaussian distribution and the feature selection mechanism aims at eliminating irrelevant features and then improving the segmentation performances. Obtained results performed on a large dataset of real world color images confirm the effectiveness of the proposed approach.
Giovanni a. travaglino 9 , COVIDiStRESS Global Survey Consortium* & andreas Lieberoth 7 ✉ this N ... more Giovanni a. travaglino 9 , COVIDiStRESS Global Survey Consortium* & andreas Lieberoth 7 ✉ this N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey-an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available.
In recent years, a great deal of effort has been expended on developing robust solutions for imag... more In recent years, a great deal of effort has been expended on developing robust solutions for images quality degradation caused mainly by noise. In this paper, we explore this area of research and we propose a new unsupervised algorithm for both image and video denoising. Our solution is based on a flexible statistical mixture model driven by a finite mixtures of bounded generalized Gaussian distributions (BGGMD) which offers more flexibility in data modeling than the well known classical gaussian distributions which fail to fit the shape of heavy-tailed data produced by the presence of noise or outliers. The proposed framework takes into account also spatial information between neighboring pixels to be more robust and flexible, and able to provide smooth and accurate denoising results. For model's parameters estimation, we investigate the unsupervised expectation-maximization (EM) algorithm. In order to evaluate the performance of the proposed model, we conducted a series of extensive experiments. Obtained results are more encouraging than those obtained using similar approaches. These results show the robustness and flexibility of the
2016 5th International Conference on Multimedia Computing and Systems (ICMCS), 2016
The purpose of this paper is to present an overview of existing active contours-based object trac... more The purpose of this paper is to present an overview of existing active contours-based object tracking methods. This restriction to recent active contours-based approaches is voluntary for two main reasons. Firstly, developed active contours models are heterogeneous that we can divide them into several sub-categories which are designed with different information and secondly, an exhaustive review of all object tracking techniques is not possible in a single article.
In this paper, we examine an important problem in the context of image processing which is image ... more In this paper, we examine an important problem in the context of image processing which is image denoising. Although conventional Gaussian distributions have been widely used, they fail to fit the shape of heavy-tailed data produced by the presence of noise. In this paper, we propose an unsupervised algorithm based on finite mixtures of bounded generalized Gaussian distributions (BGGMD) to achieve smooth denoising results. The proposed framework has the flexibility to fit different shapes of observed data and bounded support data in the case of noisy images. Experimental results demonstrate that the proposed method has superior performance than some conventional approaches.
In this paper we propose to improve image and video sequences segmentation through the integratio... more In this paper we propose to improve image and video sequences segmentation through the integration of feature selection process into an unsupervised learning approach based on a finite mixture of bounded generalized Gaussian distributions (BGGMD). The proposed algorithm is less sensitive to over-segmentation, more flexible to data modeling and leading to better characterization and localization of object of interest in high-dimensional spaces since it is able to automatically reject irrelevant visual features. In order to determine adequately and automatically the number of regions in each image or frame, spatial information is incorporated as a prior information between neighboring pixels. Experimental results which are performed on a several real world images and videos demonstrate the effectiveness of the proposed framework with respect to other conventional Gaussian-based mixture models.
We present a novel method for color image segmentation based on an unsupervised learning model an... more We present a novel method for color image segmentation based on an unsupervised learning model and feature selection. Our focus here is to develop an expectation maximization algorithm based on a mixture of bounded generalized Gaussian model combined with a feature selection mechanism. The developed statistical model offers more flexibility in data modeling than the Gaussian distribution and the feature selection mechanism aims at eliminating irrelevant features and then improving the segmentation performances. Obtained results performed on a large dataset of real world color images confirm the effectiveness of the proposed approach.
Giovanni a. travaglino 9 , COVIDiStRESS Global Survey Consortium* & andreas Lieberoth 7 ✉ this N ... more Giovanni a. travaglino 9 , COVIDiStRESS Global Survey Consortium* & andreas Lieberoth 7 ✉ this N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey-an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available.
2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)
In this paper, we address the problem of human activities and facial expression recognition by in... more In this paper, we address the problem of human activities and facial expression recognition by investigating the effectiveness of Bayesian inference methods. Indeed, a novel method termed as Bayesian learning for finite multivariate generalized Gaussian mixture model is developed. The multivariate generalized Gaussian distribution is encouraged by its ability to model a large range of data and its shape flexibility. Our main contribution in this work is to develop a Markov Chain Monte Carlo within Metropolis-Hastings algorithm for proposed generative model. In this research, we tackle also some key issues related to machine learning and pattern recognition such as the statistical model’s parameters estimation. We demonstrate the merits of our developed learning framework over two challenging applications that concern human activity recognition and facial expression recognition.
In this paper, we examine an important problem in the context of image processing which is image ... more In this paper, we examine an important problem in the context of image processing which is image denoising. Although conventional Gaussian distributions have been widely used, they fail to fit the shape of heavy-tailed data produced by the presence of noise. In this paper, we propose an unsupervised algorithm based on finite mixtures of bounded generalized Gaussian distributions (BGGMD) to achieve smooth denoising results. The proposed framework has the flexibility to fit different shapes of observed data and bounded support data in the case of noisy images. Experimental results demonstrate that the proposed method has superior performance than some conventional approaches.
We propose in this work to improve the tasks of image segmentation and modeling through an unsupe... more We propose in this work to improve the tasks of image segmentation and modeling through an unsupervised flexible learning approach. Our focus here is to develop an alternative mixture model based on a bounded generalized Gaussian distribution, which is less sensitive to over-segmentation and offers more flexibility in data modeling than the Gaussian distribution which is certainly not the best approximation for image segmentation. A maximum likelihood- (ML) based algorithm is applied for estimating the resulted model parameters. We investigate here the integration of both a spatial information (a prior information between neighboring pixels) and a minimum description length (MDL) principle into the model learning step in order to deal with the major problems of finding the optimal number of classes and also selecting the best model that describes accurately the dataset. Therefore, the proposed model has the advantage to maintain the balance between model complexity and goodness of f...
In this paper we propose to improve image and video sequences segmentation through the integratio... more In this paper we propose to improve image and video sequences segmentation through the integration of feature selection process into an unsupervised learning approach based on a finite mixture of bounded generalized Gaussian distributions (BGGMD). The proposed algorithm is less sensitive to over-segmentation, more flexible to data modeling and leading to better characterization and localization of object of interest in high-dimensional spaces since it is able to automatically reject irrelevant visual features. In order to determine adequately and automatically the number of regions in each image or frame, spatial information is incorporated as a prior information between neighboring pixels. Experimental results which are performed on a several real world images and videos demonstrate the effectiveness of the proposed framework with respect to other conventional Gaussian-based mixture models.
2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2018
We present a novel method for color image segmentation based on an unsupervised learning model an... more We present a novel method for color image segmentation based on an unsupervised learning model and feature selection. Our focus here is to develop an expectation maximization algorithm based on a mixture of bounded generalized Gaussian model combined with a feature selection mechanism. The developed statistical model offers more flexibility in data modeling than the Gaussian distribution and the feature selection mechanism aims at eliminating irrelevant features and then improving the segmentation performances. Obtained results performed on a large dataset of real world color images confirm the effectiveness of the proposed approach.
Giovanni a. travaglino 9 , COVIDiStRESS Global Survey Consortium* & andreas Lieberoth 7 ✉ this N ... more Giovanni a. travaglino 9 , COVIDiStRESS Global Survey Consortium* & andreas Lieberoth 7 ✉ this N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey-an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences. A global consortium from 39 countries and regions worked together to build and translate a survey with variables of shared interests, and recruited participants in 47 languages and dialects. Raw plus cleaned data and dynamic visualizations are available.
In recent years, a great deal of effort has been expended on developing robust solutions for imag... more In recent years, a great deal of effort has been expended on developing robust solutions for images quality degradation caused mainly by noise. In this paper, we explore this area of research and we propose a new unsupervised algorithm for both image and video denoising. Our solution is based on a flexible statistical mixture model driven by a finite mixtures of bounded generalized Gaussian distributions (BGGMD) which offers more flexibility in data modeling than the well known classical gaussian distributions which fail to fit the shape of heavy-tailed data produced by the presence of noise or outliers. The proposed framework takes into account also spatial information between neighboring pixels to be more robust and flexible, and able to provide smooth and accurate denoising results. For model's parameters estimation, we investigate the unsupervised expectation-maximization (EM) algorithm. In order to evaluate the performance of the proposed model, we conducted a series of extensive experiments. Obtained results are more encouraging than those obtained using similar approaches. These results show the robustness and flexibility of the
2016 5th International Conference on Multimedia Computing and Systems (ICMCS), 2016
The purpose of this paper is to present an overview of existing active contours-based object trac... more The purpose of this paper is to present an overview of existing active contours-based object tracking methods. This restriction to recent active contours-based approaches is voluntary for two main reasons. Firstly, developed active contours models are heterogeneous that we can divide them into several sub-categories which are designed with different information and secondly, an exhaustive review of all object tracking techniques is not possible in a single article.
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Papers by Ines Channoufi