Perceptual organization and artificial attention for visual landmarks detection.

In biological vision systems, attention mechanisms are responsible for selecting the relevant information from the sensed field of view, so that the complete scene can be analyzed using a sequence of rapid eye saccades. In recent years, efforts have been made to imitate such attention behavior in artificial vision systems, because it allows optimizing the computational resources as they can be focused on the processing of a set of selected regions. In the framework of mobile robotics navigation, this work proposes an artificial model where attention is deployed at the level of objects (visual landmarks) and where new processes for estimating bottom-up and top-down (target-based) saliency maps are employed.