Analyse de texture et visualisation scientifique

AIX-MARSEILLE2-BU Sci.Luminy (130552106) / SudocSudocFranceF

Generating linear combination of spectral images with mutually exclusive specific information

Nous proposons et etudions dans cette these des algorithmes de s eparation de sources, permettant de combiner lin eairement les bandes spectrales en imagerie multi-spectrale afin de g en erer des images scalaires contenant de l information sur des objets int eressants d une fa con mutuellement exclusive. Nous avons con cu des optimiseurs locaux, globaux et hybrides utilis es avec des objets de Lie afin de minimiser l information mutuelle parmi les bandes spectrales. L optimiseur local avec des objets de Lie produit des estimations pr ecises des Composantes Ind ependantes (CI) en comparaison avec le fastICA, pourvu que les deux algorithmes soient initialis es avec le m eme vecteur al eatoire. Afin d am eliorer la pr ecision des estimations des CI, les optimiseurs globaux les plus connus ont et e test es, avec des objets de Lie. Pour assurer des estimations robustes des minimums presque-globaux, un optimiseur hybride est propos e. N eanmoins, il est int eressant d observer les projections de l image multi-spectrale sur les CI qui sont les vecteurs-colonnes de la matrice de poids non orthogonale et non carr ee estim ee. Pour cela, une approche utilisant le BGP (Band Generation Process) et le scree graph de kurtosis est sugg er ee afin de produire une matrice de poids non carr ee. Nous avons d emontr e une autre approche dans laquelle les contraintes du modele classique d ACI sur la matrice de poids sont completement rel ach ees en calculant les centro ıdes des amas des CI g en er ees localement. Enfin une id ee th eorique est propos ee afin d obtenir des images scalaires portant de l information sp ecifique mutuellement exclusive. Les r esultats exp erimentaux concluent que les algorithmes d evelopp es au cours de cette these sont mieux adapt es que les algorithmes classiques d ACP ou d ACI aux t aches de s eparation de sources impliquant de l imagerie multi-spectrale.Source separation algorithms for linearly combining the spectral bands in the multispectral imagery to generate scalar images that contain information about interesting objects in a mutually exclusive manner are proposed and investigated. We have designed local, global and hybrid optimizers in conjunction with Lie objects to minimize the mutual information among the spectral bands. The local optimizer with Lie objects yields accurate IC estimates compared to the fastICA, provided both the algorithms are supplied with the same initial random vector. To further improve the accuracy of the IC estimates, the popular global optimizers with Lie objects are attempted. For ensuring consistent near-global minimum estimates, a hybrid optimizer is proposed. However, it is interesting to observe the projections of the multi-spectral image onto the ICs which are the column vectors of the non-square and non-orthogonal estimated weight matrix. Towards this, an approach using the band generation process and the kurtosis scree graph is suggested to produce non-square weight matrix. We have demonstrated another approach wherein the basic ICA model constraints on the weight matrix are completely relaxed by computing the cluster centroids of locally-generated ICs. Finally a theoretical idea is put forward to obtain scalar images carrying mutually exclusive specific information. The experimental results conclude that the algorithms developed in this thesis are more suitable for the source separation tasks involving multi-spectral imagery compared to classical PCA or ICA algorithms.AIX-MARSEILLE2-BU Sci.Luminy (130552106) / SudocSudocFranceF

Fast Visual Odometry for a Low-Cost Underwater Embedded Stereo System †

This paper provides details of hardware and software conception and realization of a stereo embedded system for underwater imaging. The system provides several functions that facilitate underwater surveys and run smoothly in real-time. A first post-image acquisition module provides direct visual feedback on the quality of the taken images which helps appropriate actions to be taken regarding movement speed and lighting conditions. Our main contribution is a light visual odometry method adapted to the underwater context. The proposed method uses the captured stereo image stream to provide real-time navigation and a site coverage map which is necessary to conduct a complete underwater survey. The visual odometry uses a stochastic pose representation and semi-global optimization approach to handle large sites and provides long-term autonomy, whereas a novel stereo matching approach adapted to underwater imaging and system attached lighting allows fast processing and suitability to low computational resource systems. The system is tested in a real context and shows its robustness and promising future potential