LEARNING INVARIANT TEXTURE CHARACTERISTICS IN DYNAMIC ENVIRONMENTS: A Model evolution approach

The paper presents an approach to the acquisition of texture models of specific objects under the following assumptions: (I) the system has to recognize objects on a sequence of images. (2) images of a sequence demonstrate the variability of conditions under which objects are perceived (e.g., resolution. lighting, surface positioning), (3) an observer or objects can move, (4) the extraction of texture attributes and training events can be imperfect, and (5) the system has to work autonomously (Le .• without teacher help). In order to recognize textured objects under such assumptions. the system has to adapt to the environment through the evolution of texture models. We propose to apply an incrementalleaming methodology to acquire texture descriptions from a sequence of images. The closed-loop system architecture integrates recognition and leaming processes allowing the system to evolve texture models. While the initial acquisition of texture models is driven by a teacher. the evolution of these models is performed over a sequence of images without teacher help. The texture descriptions initially acquired are applied to recognizing and to extracting objects on the next images. The effectiveness of such recognition and object extraction is monitored over time (a sequence of images) automatically. When this effectiveness decreases. the system activates learning processes to improve its models. Such improvement is performed by applying an incrementalleaming methodology. In order to evolve models, the recognition and control systems have to prepare new training examples for the next learning phase. and they have to choose the most suitable evolution strategy.