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When dealing with the registration of information from different image sources, the de facto similarity measure used is Mutual Information (MI). Although MI gives good performance in many image registration applications, recent works in thermal-visible registration have shown that other similarity measures can give results that are as accurate, if not more(More)
This paper presents a novel multiple object tracking framework based on multiple visual cues. To build tracks by selecting the best matching score between several detections, a set of probability maps is estimated by a function integrating templates using a sparse representation and color information using locality sensitive histograms. All people detected(More)
This paper addresses multiple object tracking which still remains a challenging problem because of factors like frequent occlusions, unknown number of targets and similarity in objects' appearance. We propose a novel approach for multiple object tracking using a multiple feature framework. The main focus of the proposed method is to build a robust(More)
We provide an experimental analysis of competitive insurance markets with adverse selection. Our parameterized version of the lemons’ model (Akerlof 1970) in the insurance context predicts total crowding out of low-risks when insurers offer a single full insurance contract. The therapy proposed by Rothschild and Stiglitz (1976) to solve this major(More)
This paper presents a robust online multiple object tracking (MOT) approach based on multiple features. Our approach is able to handle MOT problems, like long-term and heavy occlusions and close similarity between target appearance models. The proposed MOT algorithm is based on the concept of multi-feature fusion. It selects the best position of the tracked(More)
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