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Visually estimating a robot's own motion has been an active field of research within the last years. Though impressive results have been reported, some application areas still exhibit huge challenges. Especially for car-like robots in urban environments even the most robust estimation techniques fail due to a vast portion of independently moving objects. Hence, we move one step further and propose...
Vision systems are nowadays very promising for many on-board vehicles perception functionalities, like obstacles detection/recognition and ego-localization. In this paper, we present a 3D visual odometric method that uses a stereo-vision system to estimate the 3D ego-motion of a vehicle in outdoor road conditions. In order to run in real-time, the studied technique is sparse meaning that it makes...
This paper presents a content-based approach for temporal segmentation of videos. Tracked objects are characterized by their 2D trajectories which are used in a meaningful way to model visual semantics, i.e., the observed single video object activities and their interactions. To this end, hierarchical semi-Markov chains (SMCs) are computed in order to take into account the temporal causalities of...
This paper deals with perceptually inspired image segmentation for the purpose of generic image classification or object detection. Indeed, in our algorithm we will try to stay true to human perception and more specifically Gestalt Theory. Input images are processed in a three-step framework: a pre-processing step where the image is filtered and where perceptually similar colors are grouped as per...
In computer vision, the bag-of-visual words image representation has been shown to yield good results. Recent work has shown that modeling the spatial relationship between visual words further improves performance. Previous work extracts higher-order spatial features exhaustively. However, these spatial features are expensive to compute. We propose a novel method that simultaneously performs feature...
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