Abstract
We propose novel tools that reduce complexity and improve performances of visual detection and identification of known objects randomly located in complex cluttered environments. Generally, the propose mechanisms are based on local shape features (interest points, visual saliencies) detected in images and characterized by compact descriptors invariant to geometric and photometric transformations. In particular, a novel invariant for intensity changes is proposed, and the problem of over-exposed and under-exposed images is discussed. Both models of known objects and images of real scenes are represented using interest points, though in different scales (reference scale for models and relative scale for images). By matching interest point detected in images to interest points from the model database, known objects present in the scene can be detected and identified. The methodology can be used both for robot-mounted navigation modules and for distributed visual surveillance systems since the proposed mechanisms minimize the amount of visual data to be transmitted, thus preventing communicational saturation of such systems. In the paper, we focus on the image processing aspects of the problems. Image acquisition issues and high-level identification algorithms are only briefly mentioned.