One of the first steps of any visual system is that of locating suitable interest points, 'salient regions', in the scene, to detect events, and eventually to direct gaze toward these locations. In the last few years, object-based visual attention models have received an increasing interest in computational neuroscience and in computer vision, the problem, in this case, being that of creating a model of 'objecthood' that eventually guides a saliency mechanism. We present here an model of visual attention based on the definition of 'proto-objects' and show its instantiation on a humanoid robot. Moreover we propose a biological plausible way to learn certain Gestalt rules that can lead to proto-objects. © Springer-Verlag Berlin Heidelberg 2007.
|Title of host publication
|Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
|Number of pages
|Published - Jan 1 2007