Mohand Said Allili

Finite general Gaussian mixture modeling and application to image and video foreground segmentation

In this paper, we propose a finite mixture model of generalized Gaussian distributions (GDD) for robust segmentation and data modeling in the presence of noise and outliers. The model has more flexibility to adapt the shape of data and less sensibility for over-fitting the number of classes than the Gaussian mixture. In a first part of the present work, we propose a derivation of the maximum-likelihood estimation of the parameters of the new mixture model and we propose an information-theory based approach for the selection of the number of classes. In a second part, we propose some applications relating to image, motion and foreground segmentation to measure the performance of the new model in image data modeling with comparison to the Gaussian mixture.

A Robust Video Foreground Segmentation by Using Generalized Gaussian Mixture Modeling

In this paper, we propose a robust video foreground modeling by using a finite mixture model of generalized Gaussian distributions (GDD). The model has a flexibility to model the video background in the presence of sudden illumination changes and shadows, allowing for an efficient foreground segmentation. In a first part of the present work, we propose a derivation of the online estimation of the parameters of the mixture of GDDS and we propose a Bayesian approach for the selection of the number of classes. In a second part, we show experiments of video foreground segmentation demonstrating the performance of the proposed model.

Object tracking in videos using adaptive mixture models and active contours

In this paper, we propose a novel object tracking algorithm for video sequences, based on active contours. The tracking is based on matching the object appearance model between successive frames of the sequence using active contours. We formulate the tracking as a minimization of an objective function incorporating region, boundary and shape information. Further, in order to handle variation in object appearance due to self-shadowing, changing illumination conditions and camera geometry, we propose an adaptive mixture model for the object representation. The implementation of the method is based on the level set method. We validate our approach on tracking examples using real video sequences, with comparison to two recent state-of-the-art methods.

Finite Generalized Gaussian Mixture Modeling and Applications to Image and Video Foreground Segmentation

In this paper, we propose a finite mixture model of generalized Gaussian distributions (GDD) for robust segmentation and data modeling in the presence of noise and outliers. The model has more flexibility to adapt the shape of data and less sensibility for over-fitting the number of classes than the Gaussian mixture. In a first part of the present work, we propose a derivation of the maximum-likelihood estimation of the parameters of the new mixture model and we propose an information-theory based approach for the selection of the number of classes. In a second part, we propose some applications relating to image, motion and foreground segmentation to measure the performance of the new model in image data modeling with comparison to the Gaussian mixture.

Globally adaptive region information for automatic color-texture image segmentation

In this paper, we propose an automatic segmentation of color-texture images with arbitrary numbers of regions. The approach combines region and boundary information and uses active contours to build a partition of the image. The segmentation algorithm is initialized automatically by using homogeneous region seeds on the image domain. The partition of the image is formed by evolving the region contours and adaptively updating the region information formulated using a mixture of pdfs. We show the performance of the proposed method on examples of color-texture image segmentation, with comparison to two state-of-the-art methods.

Active contours for video object tracking using region, boundary and shape information

In this paper, we propose a robust model for tracking in video sequences with non-static backgrounds. The object boundaries are tracked on each frame of the sequence by minimizing an energy functional that combines region, boundary and shape information. The region information is formulated by minimizing the symmetric Kullback–Leibler (KL) distance between the local and global statistics of the objects versus the background. The boundary information is formulated using a color and texture edge map of the video frames. The shape information is calculated adaptively to the dynamic of the moving objects and permits tracking that is robust to background distractions and occlusions. Minimization of the energy functional is implemented using the level set method. We show the effectiveness of the approach for object tracking in color, infrared (IR), and fused color-infrared sequences.

An approach for dynamic combination of region and boundary information in segmentation

Image segmentation combining boundary and region information has been the subject of numerous research works in the past. This combination is usually subject to arbitrary weighting parameters (hyper-parameters) that control the contribution of boundary and region features during segmentation. In this work, we investigate a new approach for estimating the hyper-parameters adaptively to segmentation. The approach takes its roots from the physical properties of the energy functional controlling segmentation and a Bayesian formulation of segmentation and hyper-parameters estimation.

Texture Modeling Using Contourlets and Finite Mixtures of Generalized Gaussian Distributions and Applications

In this paper, we develop a new framework for contourlet-based statistical modeling using finite Mixtures of Generalized Gaussian distributions ( MoGG). On the one hand, given the rich directional information provided by the contourlet transform (CT), we propose to use a redundant version of the CT, which describes texture structures more accurately. On the other hand, we use MoGG modeling of contourlet coefficients distribution, which allows for precise capturing of a wide range of histogram shapes and provides better description and discrimination of texture than single probability density functions (pdfs). Moreover, we propose three applications for the proposed approach, namely: (1) texture retrieval, (2) fabric texture defect detection, and 3) infrared (IR) face recognition. We compare two implementations of the CT: standard CT ( SCT) and redundant CT ( RCT). We show that the proposed approach yields better results in the applications studied compared to recent state-of-the-art methods.

Finite Gamma mixture modelling using minimum message length inference: application to SAR image analysis

This paper discusses the unsupervised learning problem for finite mixtures of Gamma distributions. An important part of this problem is determining the number of clusters which best describes a set of data. We apply the Minimum Message Length (MML) criterion to the unsupervised learning problem in the case of finite mixtures of Gamma distributions. The MML and other criteria in the literature are compared in terms of their ability to estimate the number of clusters in a data set. The comparison utilizes synthetic and RADARSAT SAR images. The performance of our method is also tested by contextual evaluations involving SAR image segmentation and change detection.

Object of Interest segmentation and Tracking by Using Feature Selection and Active Contours

Most image segmentation algorithms in the past are based on optimizing an objective function that aims to achieve the similarity between several low-level features to build a partition of the image into homogeneous regions. In the present paper, we propose to incorporate the relevance (selection) of the grouping features to enforce the segmentation toward the capturing of objects of interest. The relevance of the features is determined through a set of positive and negative examples of a specific object defined a priori by the user. The calculation of the relevance of the features is performed by maximizing an objective function defined on the mixture likelihoods of the positive and negative object examples sets. The incorporation of the features relevance in the object segmentation is formulated through an energy functional which is minimized by using level set active contours. We show the efficiency of the approach on several examples of object of interest segmentation and tracking where the features relevance is used.