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parlab publications > thesesTHÈSE PRÉSENTÉE À LA FACULTÉ DES SCIENCES POUR OBTENIR LE GRADE DE DOCTEUR ÈS SCIENCES
Visual attention is the ability of a vision system, be it biological or artificial, to rapidly select the most salient and thus the most relevant data about the environment in which the system is operating. The main goal of this visual mechanism is to drastically reduce the amount of visual information that must be processed by high level and thus complex tasks, such as object recognition, which leads to a considerable speed up of the entire vision process.
This thesis copes with various aspects related to visual attention, ranging from biologically inspired computational modeling of this visual behavior to its real- time realization on dedicated hardware, and its successful application to solve real computer vision tasks. Indeed, the contributions brought together in this thesis can be divided into four main parts.
The first part deals with the computational modeling of visual attention by assessing the significance of novel features like depth and motion to the visual attention mechanism. Thereby, two models have been conceived and validated, namely the 3D- and the dynamic models of visual attention.
In the second part, the biological plausibility of various versions of the visual attention model is evaluated. Therefore, the performance of our visual attention model is compared with human visual attention behavior, assuming that the human visual attention is intimately linked to the eye movements.
The third part of the thesis covers our contribution on the realization of a real-time operating system of visual attention. Indeed, the computational model of visual attention is implemented on a highly parallel architecture conceived for general purpose image processing, which allows to reach real-time requirements.
Last but not least, the visual attention model has been successfully applied to speed up but also to increase the performance of various real tasks related to computer vision. Thereby, image compression, color image segmentation, visual object tracking, and automatic traffic sign detection and recognition largely ben- efit from the salient scene information provided by the proposed visual attention algorithm. Specifically, they use this information to automatically adjust their internal parameters according to scene contents, thus, considerably enhancing the quality of the achieved results.