Vision system for fast 3D obstacle detection via stereovision matching.

Image matching is one of the fundamental problems of computer vision. Various approaches exist. They differ essentially by extracted primitives, by the best match search strategy, and by final applications. Feature based dense matching methods use such geometric primitives as raw pixels, edges, interest points, etc. Some of the correlation based matching methods involve a distance calculation, a time consuming operations. Its enhancement adds pixel complex photometric characteristics such as gradient direction, local curvature and luminosity local disparity, what increases the matching time, but they are usually very noisy. The matching method noise dependency and data volume can be reduced when improving the 'interest point' robustness. This paper proposes to add to 'interest point primitive' a set (vector) of simple characteristics (geometric and photometric), which are invariant to geometric plan transforms. A matching method based upon these enriched pixels and accumulation array concept is presented as well. These elements are useful for 3D obstacle detection in the on-going project "Intelligent glasses", our final application. The intelligent glasses is a vision system for humanoid robot and for blinds/visually impaired persons under joint development by Rouen University and Robotics Laboratory in Paris.