Layachi Bentabet

Road vectors update using SAR imagery: a snake-based method

The paper presents an approach for roads detection based on synthetic aperture radar (SAR) images and road databases. The vectors provided by the database are refined using active contours (snakes). In this framework, we firstly develop a restoration filter based on the frost filter achieving an acceptable compromise between speckle elimination and lines preserving. This is followed by a line plausibility calculation step which is used to deform the snake from its initial location toward the final solution. The snake is reformulated using finite elements method. The setting of the snake parameters is not an obvious problem especially when they are tuned by trial-and-error process. We propose a new automatic computational rule for the snake parameters. Our approach is validated by a series of tests on synthetic and SAR images.

Dynamic Cardiac PET Imaging: Extraction of Time-Activity Curves Using ICA and a Generalized Gaussian Distribution Model

Kinetic modeling of metabolic and physiologic cardiac processes in small animals requires an input function (IF) and a tissue time-activity curves (TACs). In this paper, we present a mathematical method based on independent component analysis (ICA) to extract the IF and the myocardiums TACs directly from dynamic positron emission tomography (PET) images. The method assumes a super-Gaussian distribution model for the blood activity, and a sub-Gaussian distribution model for the tissue activity. Our appreach was applied on 22 PET measurement sets of small animals, which were obtained from the three most frequently used cardiac radiotracers, namely: desoxy-fluoro-glucose (18F-FDG), [13 N]-ammonia, and [11C]-acetate. Our study was extended to PET human measurements obtained with the Rubidium-82 (82 Rb) radiotracer. The resolved mathematical IF values compare favorably to those derived from curves extracted from regions of interest (ROI), suggesting that the procedure presents a reliable alternative to serial blood sampling for small-animal cardiac PET studies.

Extraction of time activity curves from gated FDG-PET images for small animals’ heart studies

We introduce a new approach to extract the input function and the tissue time activity curve from dynamic ECG-gated (18)F-FDG PET images. These curves are mandatory to model the myocardium metabolic rate of glucose for heart studies. The proposed method utilizes coupled active contours to track the myocardium and the blood pool deformations. Furthermore, a statistical approach is developed to model the blood and tissue activities and to correct for spillovers. The developed algorithm offers a reliable alternative to serial blood sampling for small animal cardiac PET studies. Indeed, the calculated MMRG value differs by 1.54% only from the reference value.

A Particle Filtering and DSmT Based Approach for Conflict Resolving in case of Target Tracking with Multiple Cues

In this paper, we propose an efficient and robust method for multiple targets tracking in cluttered scenes using multiple cues. Our approach combines the use of Monte Carlo sequential filtering for tracking and Dezert-Smarandache theory (DSmT) to integrate the information provided by the different cues. The use of DSmT provides the necessary framework to quantify and overcome the conflict that might appear between the cues due to the occlusion. Our tracking approach is tested with color and location cues on a cluttered scene where multiple targets are involved in partial or total occlusion.

Pedestrian tracking using color, thermal and location cue measurements: a DSmT-based framework

In this paper, we address the problem of pedestrians tracking in cluttered scenes using location, color and thermal cues. The Dezert–Smarandache (DSm) theoretical framework is used to combine the measurements provided by the sensors into a single and unified frame. The use of DSm Theory allows modeling the conflicts that might arise between the sensors due to the presence of clutter and high levels of occlusion. The location cue is integrated as a prior knowledge, which increases the robustness of the tracking. A belief measure is derived and used as a step in a particle filtering algorithm. Finally, experimental results are given, where the developed approach is used to track walking persons in indoor scenes with high levels of occlusion and clutter.

A combined Markovian and Dirichlet sub-mixture modeling for evidence assignment: Application to image fusion

The estimation of Mass functions is a key issue in evidence theory. In this paper, we propose an algorithmic framework to achieve this task using a statistical modeling of the data. The confidence level of each component in the frame of discernment is represented and quantified using a sub-mixture model, where each data cluster is approximated by a Dirichlet distribution. We discuss and show the interest of using the Dirichlet distribution to model sensors corrupted by non-Gaussian noise. The contextual relationship is integrated within the fusion scheme using Markov fields. In this context, we propose an adaptation of the iterated conditional modes (ICM) algorithm which permits to deal with compound hypotheses as defined by Dempster-Shafer theory. The experiments are conducted, in the context of image segmentation using multiple sensors, on synthetic, radar and optical (SPOT) images.

A DSmT-Based approach for data association in the context of multiple target tracking

This paper presents a multiple target tracking method that uses the Dezert-Smarandache Theory (DSmT) for data association. A detailed framework is developed to show how the DSmT can be used to associate measurements with the corresponding correct targets. We will discuss the choices of the tracking hypotheses in the DSmT and we will demonstrate the effectiveness of the developed approach on simulated and real tracking scenarios that uses color and infrared cues.

A sequential Monte-Carlo and DSmT based approach for conflict handling in case of multiple targets tracking

In this paper, we propose an efficient and robust multiple targets tracking method based on particle filtering and Dezert-Smarandache theory. A model of cue combination is designed with plausible and paradoxical reasoning. The proposed model can resolve the conflict and paradoxes that arise between measured cues due to the particle or total occlusion. Experimental results demonstrate the efficiency and accuracy of the model in case of tracking with multiple cues.

Iterative Markovian estimation of mass functions in Dempster Shafer evidence theory: application to multisensor image segmentation

Mass functions estimation is a key issue in evidence theory-based segmentation of multisensor images. In this paper, we generalize the statistical mixture modeling and the Bayesian inference approach in order to quantify the confidence level in the context of Dempster-Shafer theory. We demonstrate that our model assigns confidence levels in a relevant manner. Contextual information is integrated using a Markovian field that is adapted to handle compound hypotheses. The multiple sensors are assumed to be corrupted by different noise models. In this case, we show the interest of using a flexible Dirichlet distribution to model the data. The effectiveness of our method is demonstrated on synthetic and radar and SPOT images.

Bayesian image matting using infrared and color cues

In this paper, we propose a new matting solution that combines the use of color and infrared cameras for matting applications involving human actors. The infrared camera facilitates the extraction of the initial trimap and provides additional information for the matte estimation. The approach proposed in this paper differs from the techniques proposed in the literature in many aspects. It employs thermal information for human actors, which proves to be useful and effective for matting when combined with color information. It also introduces a new technique for automatic trimap construction that is based on the temperatures difference between the foreground actor and the background objects. Finally, the matting step is carried out using a Bayesian approach which combines the color and the infrared inputs into a single criterion. The matting results accuracy shows that our approach is capable of tackling digital image and video matting problems.