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Condat L.,GREYC
IEEE Transactions on Image Processing | Year: 2011

Digital color cameras acquire color images by means of a sensor on which a color filter array (CFA) is overlaid. The Bayer CFA dominates the consumer market, but there has recently been a renewed interest for the design of CFAs . However, robustness to noise is often neglected in the design, though it is crucial in practice. In this paper, we present a new 2 × 3-periodic CFA which provides, by construction, the optimal tradeoff between robustness to aliasing, chrominance noise and luminance noise. Moreover, a simple and efficient linear demosaicking algorithm is described, which fully exploits the spectral properties of the CFA. Practical experiments confirm the superiority of our design, both in noiseless and noisy scenarios. © 2011 IEEE. Source

Bretto A.,GREYC | Bretto A.,Telecom ParisTech | Ducournau A.,ENISE DIPI | Rital S.,Telecom ParisTech
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

In this paper, we introduce a novel hypergraph reduction algorithm, and we evaluate it in an innovative method for joint segmentation and classification of satellite image content. It operates in 3 steps. First, we compute an Image Neighborhood Hypergraph representation (INH). Second, we reduce the INH model and we exploit a morphism from INH to Reduced INH (RINH) to generate superpixels. Then, we perform a superpixels supervised classification according to their features. Our approach is very fast and can deal with great sized images. Its reliability has been tested on several satellite images with comparison to single pixelwise classification. © 2010 Springer-Verlag. Source

Condat L.,GREYC
Image and Vision Computing | Year: 2010

We propose two new types of random patterns with R, G, B colors, which allow to design color filter arrays (CFAs) with good spectral properties. Indeed, the chrominance channels have blue noise characteristics, a property which maximizes the robustness of the acquisition system to aliasing. With these new CFAs, the demosaicking artifacts appear as incoherent noise, which is less visually disturbing than the moiré structures characteristic of CFAs with periodic patterns. © 2009 Elsevier B.V. All rights reserved. Source

Lozenguez G.,GREYC | Lozenguez G.,University Blaise Pascal | Adouane L.,University Blaise Pascal | Beynier A.,University Pierre and Marie Curie | And 2 more authors.
Advances in Intelligent and Soft Computing | Year: 2011

In this paper, we present an approach to automatically allocate a set of exploration tasks between a fleet of mobile robots. Our approach combines a RoadMap technique and Markovian Decision Processes (MDPs).We are interested in the problem of exploring an area where several robots need to visit a set of points of interest. This problem induces a long term horizon motion planning with a combinatorial explosion. The RoadMap allows us to represent spatial knowledge as a graph of paths. It can be modified during the exploration mission requiring the robots to use on-line computation. By decomposing the RoadMap into regions, an MDP allows the leader robot to evaluate the interest of each robot in every single region. Using those values, the leader can assign the exploration tasks to the robots. © 2011 Springer-Verlag Berlin Heidelberg. Source

Lozenguez G.,GREYC | Adouane L.,CNRS Pascal Institute | Beynier A.,University Pierre and Marie Curie | Mouaddib A.-I.,GREYC | Martinet P.,CNRS Pascal Institute
Advances in Intelligent Systems and Computing | Year: 2013

This paper presents a robot solution that allows to automatically reach a set of goals attributed to a robot. The challenge is to design autonomous robots assigned to perform missions without a predefined plan. We address the stochastic salesman problem where the goal is to visit a set of points of interest. A stochastic Road-Map is defined as a topological representation of an unstructured environment with uncertainty on the path achievement. The Road-Map allows us to split deliberation and reactive control. The proposed decision making uses a computation of Markov Decision Processes (MDPs) in order to plan all the reactive tasks to perform while there are goals not yet reached. Finally, from a brief explanation on how the approach could be extend to multi-robot missions, experiments in real conditions permit to evaluate the proposed architecture for multi-robot stochastic salesmen missions. © 2013 Springer-Verlag. Source

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