Active and selective perception seeks regions of interest in an image in order to reduce the computational complexity associated with time-consuming processes such as object recognition. We describe in this paper a visual attention system that extracts regions of interest by integrating multiple image cues. Bottom-up cues are detected by decomposing the image into a number: of feature and conspicuity maps, while a-priori knowledge (i.e. models) about objects is used to generate top-down attention cues. Bottom-up and top-down information is combined through a non-linear relaxation process using energy minimization-like procedures. The functionality of the attention system is expanded by the introduction of an alerting (motion-based) system able to explore and avoid obstacles. Experimental results are reported, using cluttered and noisy scenes.>

The potato operation is a project belonging to the domain of agricultural robotics, or agrotics. It aims to automate pulp sampling of potatoes in order to detect viral diseases. The difficulty of this problem lies in the high variability (shape, texture, colour) of natural objects. Three approaches have been studied, based on contour classification, on thermometry and on active vision and image analysis. Cooperation between robotics, active 3D computer vision and robust image analysis leads to a performant solution.>