Antoine Manzanera

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Motion detection using a stationary camera can be done by estimating the static scene (background). In that purpose, we propose a new method based on a simple recursive non linear operator, the R–D filter. Used along with a spatiotemporal regularization algorithm, it allows robust, computationally efficient and accurate motion detection. To deal with(More)
This article introduces a new hierarchical version of a set of motion detection algorithms called ΣΔ. These new algorithms are designed to preserve as much as possible the computational efficiency of the basic ΣΔ estimation, in order to target real-time implementation for low power consumption processors and embedded systems.
Reducing the dimension of local descriptors in images is useful to perform pixels comparison faster. We show here that, for computing the NL-means denoising filter, image patches can be favourably replaced by a vector of spatial derivatives (local jet), to calculate the similarity between pixels. First, we present the basic, limited range implementation,(More)
We present a new algorithm to compute the watershed transform on a massively parallel machine. Logic minimization, together with a novel approach to the notion of dynamics, allow the implementation of the watershed algorithm on a cellular automata grid, such as the programmable retina. We then investigate the profit acquired from computing some parts of the(More)
This paper describes a vision-based ground-plane classification system for autonomous indoor mobile-robot that takes advantage of the synergy in combining together multiple visual-cues. A priori knowledge of the environment is important in many biological systems, in parallel with their reactive systems. As such, a learning model approach is taken here for(More)