Attention mechanisms extract regions of interest from image data, in order to reduce the amount of information to be analyzed by time{consuming processes such as image transmission, robot navigation, and object recognition. In this paper two such m e c hanisms are described. The rst one is an alerting system which extracts moving objects in a sequence through the use of multiresolution representations.The second one detects regions in still images which are likely to contain objects of interest. Two t ypes of cues are used and integrated to compute the measure of interest. First, bottom{up cues result from the decomposition of the input image into a number of feature and conspicuity maps. The second type of cues is top{down, and is obtained from a{priori knowledge about target objects, represented through invariant m o d e l s . Results are reported for both the alerting and the attention mechanisms, using cluttered and noisy scenes.

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In this paper, the problem of combining estimates provided by multiple models is considered, with application to vehicle tracking. Two tracking systems, based on the bounding-box and on the 2-D pattern of the targets, provide individual motion parameters estimates to the combining method, which in turn produces a global estimate. Two methods are proposed to combine the estimates of these tracking systems: one is based on their covariance matrix, while the other one employs a Kalman filter model. Results are provided on three image sequences taken under different viewpoints, weather conditions and varying vehicle/road contrasts. Two evaluations are made. First, the performances of individual and global estimates are compared. Second, the two global estimates are compared and the superiority of the second method is assessed over the first one.

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Each year at harvest time millions of seed potatoes are checked for the presence of viruses, by means of an Elisa test. The Potato Operation aims at automatizing the potato manipulation and pulp sampling procedure, starting from bunches of harvested potatoes and ending with the deposit of potato pulp into Elisa containers. Automatizing these manipulations addresses several issues, linking robotic and computer vision. The paper reports on the current status of this project. It first summarizes the robotic aspects, which consist of locating a potato in a bunch, grasping it, positioning it into the camera field of view, pumping the pulp sample and depositing it into a container.