Data- and model-driven multiresolution processing

We introduce a technique for multiresolution processing which elegantly fits in our framework for visual recognition, described in earlier papers, The input is processed simultaneously at a coarse resolution throughout the image and at finer resolution within a small window (fovea), We introduce an approach for controlling the movement of the high-resolution window which allows for both data- and model-driven selection of fixation points, Three fixation modes have been implemented, one based on large unexplained areas in the data, one on conflicts in the object-model database, and one on a 2D ''space filling'' algorithm, We argue that this kind of multiresolution processing is not only useful in limiting the computational time, as has been widely recognized, but also can be a deciding factor in making the entire vision problem a tractable and stable one, To demonstrate the approach, we introduce a class of 3D surface textures as a feature for recognition in our system, Surface texture recognition typically requires higher-resolution processing than that required for the extraction of the underlying surface, As examples, surface texture is used to discriminate between a ping-pong ball and a golf ball, and ''curve texture'' is used to recognize different types of gears, Other experimental results also are included to show the advantages and the implications of our approach. (C) 1996 Academic Press, Inc.