Fast parallel image processing for robot vision for the purpose of extracting information about objects

In order to realize robust real-time 3-D object recognition and object location and orientation estimation, a 2-D image is partitioned into small square windows which are processed in parallel. It is assumed that a 3-D object can be approximated by chunks of planes, spheres, and cylinders and that one such surface type is seen within a small window. Our solutions are largely for matte surfaces (i.e. diffusely reflecting); we do touch on specular (i.e, mirror) surfaces. Two kinds of results have been obtained in this work. For the purpose of 3-D object type recognition, a simple 2-D polynomial approximation is made to the image data in a window. Based on the relationships between polynomial coefficients and 3-D surface shape-types, a reliable decision can be made as to whether the object surface seen is that of a plane, a cylinder or sphere. In addition to providing the appropriate observables for surface-type recognition, these polynomial fits act as remarkably effective nonlinear lowpass filters.