Optimal stopping condition for iterative image deconvolution by new orthogonality criterion

The stopping condition is a common problem for non-regularised deconvolution methods. Introduced is an automatic procedure for estimating the ideal stopping point based on a new measure of independence, checking an orthogonality criterion of the estimated signal and its gradient at a given iteration. An effective lower bound estimate than the conventional ad hoc methods is provided, proving its superiority to the others at a wide range of different noise models

3D reconstruction and object recognition from 2D SONAR data

Accurate and meaningful representations of the environment are required for autonomy in underwater applications. Thanks to favourable propagation properties in water, acoustic sensors are commonly preferred to video cameras and lasers but do not provide direct 3D information. This thesis addresses the 3D reconstruction of underwater scenes from 2D imaging SONAR data as well as the recognition of objects of interest in the reconstructed scene. We present two 3D reconstruction methods and two model-based object recognition methods. We evaluate our algorithms on multiple scenarios including data gathered by an AUV. We show the ability to reconstruct underwater environments at centimetre-level accuracy using 2D SONARs of any aperture. We demonstrate the recognition of structures of interest on a medium-sized oil-field type environment providing accurate yet low memory footprint semantic world models. We conclude that accurate 3D semantic representations of partially-structured marine environments can be obtained from commonly embedded 2D SONARs, enabling online world modelling, relocalisation and model-based applications

Video event detection and visual data pro cessing for multimedia applications

Cette thèse (i) décrit une procédure automatique pour estimer la condition d'arrêt des méthodes de déconvolution itératives basées sur un critère d'orthogonalité du signal estimé et de son gradient à une itération donnée; (ii) présente une méthode qui décompose l'image en une partie géométrique (ou "cartoon") et une partie "texture" en utilisation une estimation de paramètre et une condition d'arrêt basées sur la diffusion anisotropique avec orthogonalité, en utilisant le fait que ces deux composantes. "cartoon" et "texture", doivent être indépendantes; (iii) décrit une méthode pour extraire d'une séquence vidéo obtenue à partir de caméra portable les objets de premier plan en mouvement. Cette méthode augmente la compensation de mouvement de la caméra par une nouvelle estimation basée noyau de la fonction de probabilité de densité des pixels d'arrière-plan. Les méthodes présentées ont été testées et comparées aux algorithmes de l'état de l'art.This dissertation (i) describes an automatic procedure for estimating the stopping condition of non-regularized iterative deconvolution methods based on an orthogonality criterion of the estimated signal and its gradient at a given iteration; (ii) presents a decomposition method that splits the image into geometric (or cartoon) and texture parts using anisotropic diffusion with orthogonality based parameter estimation and stopping condition, utilizing the theory that the cartoon and the texture components of an image should be independent of each other; (iii) describes a method for moving foreground object extraction in sequences taken by wearable camera, with strong motion, where the camera motion compensated frame differencing is enhanced with a novel kernel-based estimation of the probability density function of the background pixels. The presented methods have been thoroughly tested and compared to other similar algorithms from the state-of-the-art.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Dual Range Deringing for non-blind image deconvolution

The popular Richardson-Lucy (RL) image deconvolution algorithm often produces undesirable ringing artifacts. In this paper, we propose a novel Dual Range Deringing (DRD) algorithm to address this problem. As a post-deconvolution scheme, the proposed approach follows RL deconvolution and removes ringing artifacts by utilizing information from both the input blurred image and the RL-deblurred image. DRD first marks smooth regions in the input blurred image that are likely to be subjected to ringing artifacts far away from any strong edge. It then identifies short-range ringing artifacts from the regions that surround strong edges in the RL-deblurred image. Once marked, both long- and short-range ringing artifacts are then suppressed by an edge-preserving deringing filter. We demonstrate the effectiveness of this procedure by performing experiments on a set of images blurred with various Point Spread Functions (PSFs). We compare DRD with state-of-the-art non-blind deconvolution algorithms and show that our results are virtually free of ringing artifacts with only minor detail losses. Moreover, DRD consists of computationally efficient local operations and is suitable for parallelization on modern GPUs. ? 2010 IEEE.EI