Jean-Baptiste Bordes

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The large number of tasks one may expect from a driver assistance system leads to consider many object classes in the neighborhood of the so-called intelligent vehicle. In order to get a correct understanding of the driving scene, one has to fuse all sources of information that can be made available. In this paper, an original fusion framework working on(More)
For its simplicity and efficiency, the bag-of-words representation based on appearance features is widely used in image and text classification. Its drawback is that shape patterns of the image are neglected. This paper presents a novel image classification approach using a bag-of-words representation of textons while taking into account spatial(More)
This paper addresses the problem of scene understanding for driver assistance systems. To recognize the large number of objects that may be found on the road, several sensors and decision algorithms have to be used. The proposed approach is based on the representation of all available information in over-segmented image regions. The main novelty of the(More)
In this paper, an original method for traffic scene images understanding based on the theory of belief functions is presented. Our approach takes place in a multi-sensors context and decomposes a scene into objects through the following steps: at first, an over-segmentation of the image is performed and a set of detection modules provides for each segment a(More)
Humans can learn word-object associations from ambiguous data using cross-situational learning and have been shown to be more efficient when actively choosing the learning sample order. Implementing such a capacity in robots has been performed using several models, among which are the latent-topic learning models based on Non-Negative Matrix Factorization(More)