Une technique d'accélération pour la compression fractale d'images

Les techniques fractales de compression d'images souffrent encore aujourd'hui de temps de codage très importants. Nous proposons ici un nouvel algorithme récursif d'optimisation, couplé à un algorithme de compression de type Jacquin. Un prédicat numérique est utilisé pour déterminer quel bloc candidat peut correspondre à un bloc initial donné, ce qui permet d'éviter des comparaisons coûteuses en temps de calcul. La méthode permet d'obtenir des accélérations considérables pour un prédicat simple comme l'inclusion d'histogrammes des niveaux de gris. Elle est de surcroît compatible avec d'autres méthodes d'accélération

Underwater target detection with hyperspectral data : solutions for both known and unknown water quality", S. Jay, M. Guillaume, J. Blanc-Talon, , IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5 :1213-1221, 2012. IF 2.87

International audienceIn this paper, we present various bathymetric filters, based on the well-known MF, AMF and ACE detectors, for underwater target detection from hyperspectral remote-sensing data. In the case of unknown water characteristics, we also propose the GBF, a GLRT-based filter that estimates these parameters and detects at the same time. The results of this estimation process, performed on both simulated and real data, are encouraging, since under regular conditions of depth, water quality and SNR, the accuracy is quite good. We show that these new detectors outperform the usual ones, obtained by detecting after correction of the water column effect by a classical method. We also show that the estimation errors do not impact much the detection performances, and therefore, this underwater target detection method is self-sufficient and can be implemented without any a priori knowledge on the water column

Compressive Template Matching on Multispectral Data

International audienceThis paper adapts a new template matching and target detection algorithm in multispectral images to a compressive sensing strategy. That template matching algorithm found in [1] relies on particular properties of L1 minimization algorithms to succeed. We propose a new algorithm that is reconstructing in a single step the location of a given signature of interest bypassing the image reconstruction and the template matching algorithm on that image. For that purpose, we use a modified split Bregman algorithm with various regularizers. We conduct numerical experiments on real-world multispectral image

Restoration of Videos Degraded by Local Isoplanatism Effects in the Near-Infrared Domain

When observing a scene horizontally at a long distance in the near-infrared domain, degradations due to atmospheric turbulence often occur. In our previous work, we presented two hybrid methods to restore videos degraded by such local perturbations. These restoration algorithms take advantages of a space-time Wiener filter and a space-time regularization by the Laplacian operator. Wiener and Laplacian regularization results are mixed differently depending on the distance between the current pixel and the nearest edge point. It was shown that a gradation between Wiener and Laplacian areas improves results quality, so that only the algorithm using a gradation will be used in this article. In spite of a significant improvement in the obtained images quality, our restoration results greatly depend on the segmentation image used in the video processing. We then propose a method to select automatically the best segmentation image

Advanced concepts for intelligent vision systems, 19th international conference, ACIVS 2018, proceedings

International audienceThis book constitutes the refereed proceedings of the 19th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2018, held in Poitiers, France, in September 2018. The 52 full papers presented in this volume were carefully reviewed and selected from 91 submissions. They were organized in topical sections named: video analysis; segmentation and classification; remote sending; biometrics; deep learning; coding and compression; and image restauration and reconstruction

A multi-layer `gas of circles' Markov random field model for the extraction of overlapping near-circular objects

We propose a multi-layer binary Markov random field (MRF) model that assigns high probability to object configurations in the image domain consisting of an unknown number of possibly touching or overlapping near-circular objects of approximately a given size. Each layer has an associated binary field that specifies a region corresponding to objects. Overlapping objects are represented by regions in different layers. Within each layer, long-range interactions favor connected components of approximately circular shape, while regions in different layers that overlap are penalized. Used as a prior coupled with a suitable data likelihood, the model can be used for object extraction from images, e.g. cells in biological images or densely-packed tree crowns in remote sensing images. We present a theoretical and experimental analysis of the model, and demonstrate its performance on various synthetic and biomedical images

Approche structurelle du controle du reseau d'interconnexion d'une machine multiprocesseurs pour les algorithmes de reconnaissance des formes, et applications

SIGLEINIST T 77131 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc