Attention mechanisms extract regions of interest from image data to reduce the amount of information to be analyzed by time-consuming processes such as image transmission, robot navigation, and object recognition. Two such mechanisms are described. The first one is an alerting system that extracts moving objects in a sequence through the use of multiresolution representations. The second one detects regions in still images that are likely to contain objects of interest. Two types 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 models. Results are reported for both the alerting and the attention mechanisms using cluttered and noisy scenes.