Le but de cette thèse est de proposer et d’évaluer une représentation 3D de collections non structurées de documents visuels. L'objectif est d’accroître le confort visuel, de réduire le temps de recherche et de favoriser la mémorisation des positions des documents, par rapport aux logiciels actuels de gestion de collections d'images. Avec une représentation sous forme d'un cylindre vertical, nous avons conçu 2 visualisations : les photographies sont plaquées sur la face interne ou externe du cylindre, pour implémenter 2 métaphores courantes d’interaction, respectivement locomotion et manipulation. 3 études expérimentales ont été réalisées, avec la même méthodologie : la réalisation par les participants de 2 tâches courantes de recherche visuelle. La première étude est exploratoire, la seconde compare les 2 vues 3D plus finement et la dernière compare les vues 3D à une vue 2D classique. Dans cette dernière étude, l’usage d’un oculomètre permet de comprendre les relations entre les caractéristiques géométriques des vues et les stratégies visuelles des participants. Les participants préfèrent majoritairement les vues 3D. Les performances n'influent pas sur les préférences ; les caractéristiques géométriques statiques ont principalement été mentionnées pour justifier les préférences. Les stratégies de parcours visuel sont davantage influencées par la caractéristique dynamique des vues (i.e., défilement horizontal), ce qui avantage la vue interne. Enfin, les vues 3D garantissent un meilleur confort visuel, en offrant un meilleur guidage du regard (principalement la vue interne) et en minimisant les saccades désordonnées observées dans la vue 2D. / The goal of this thesis is to propose and to assess a 3D representation of large unstructured collections of images. The purpose is to improve comfort of visual search, to reduce search times and to better support memorization of documents’ locations, compared with standard photo browsers. With a representation as a vertical cylinder, we designed two visualizations: photos are plastered on the inner or the outer side of the cylinder, to implement 2 common interaction metaphors, respectively locomotion and manipulation. 3 experimental studies were performed, with the same methodology: subjects performed 2 common types of visual search tasks. The first study is exploratory, the second one compares both 3D views in-depth and the last one compares 3D views with a standard 2D view. In this study, an eye-tracker helps us to understand relations between geometrical features and visual exploration strategies. 3D views are preferred by a majority of participants. Preferences are not influenced by performances; static visual features were mainly used to justified preferences. Visual exploration strategies are mainly influenced by the common dynamic visual feature (i.e., horizontal scrolling), which is an advantage for the inner view. At last, 3D views ensure a better comfort of visual search, with a better gaze guidance (especially the inner view) and by minimizing uncoordinated saccades which were observed in the 2D view.

This paper presents a bio-inspired approach for optical flow data interpretation based on fuzzy inference decision making for visual mobile robot navigation. The interpretation results of regionally averaged optical flow patterns with pyramid segmentation of the optical flow field deliver fuzzy topological and topographic information of the surrounding environment (topological structure from motion). It allows a topological localization in a global map as well as controlled locomotion (obstacle avoidance, goal seeking) in a changing and dynamic environment. The topological optical flow processing is embedded in a behavior based mobile robot navigation system which uses only a mono-camera as primary navigation sensor. The paper discusses the optical flow processing approach as well as the rule based fuzzy inference algorithms used. The implemented algorithms have been tested successfully with synthetic image data for a first verification and parameter tuning as well as in a real office environment with real image data.

This paper presents a bio-inspired approach for optical flow data interpretation based on fuzzy inference decision making for visual mobile robot navigation. The interpretation results of regionally averaged optical flow patterns with pyramid segmentation of the optical flow field deliver fuzzy topological and topographic information of the surrounding environment (topological structure from motion). It allows a topological localization in a global map as well as controlled locomotion (obstacle avoidance, goal seeking) in a changing and dynamic environment. The topological optical flow processing is embedded in a behavior based mobile robot navigation system which uses only a mono-camera as primary navigation sensor. The paper discusses the optical flow processing approach as well as the rule based fuzzy inference algorithms used. The implemented algorithms have been tested successfully with synthetic image data for a first verification and parameter tuning as well as in a real office environment with real image data.