SHAPE RECOGNITION BY HUMAN-LIKE TRIAL AND ERROR RANDOM PROCESSES

Abstract

Pattern recognition and object detection systems so far developed required the algorithmic description of every detail of the objects to be recognized by bottom-up process from pixel-to-pixel relation to line, corner, and structural description. Because this low-level process does not see global information, feature detection is highly sensitive to noise. To overcome this problem and to give human-like flexibility to machine recognition process, we developed a new system which had non-algorithmic feature detection functions by seeing a comparatively large area at once. It uses a variable size window which is applied to the most plausible parts in an image by a top-down command from an object model, and obtains characteristic features of object parts. This window application is realized mostly in hardware, and has some autonomic ability to detect the best features by a sort of random trial and error search. The system has some other hardware functions such as mutual correlation of one- and two-dimensional patterns, which are also flexible according to the variable size window. The system interprets user's declarative description of objects, and activates the window application functions to obtain characteristic features of the description. This new flexible approach of object detection can be used as a robot eye to recognize many simple two-dimensional shapes.