Image processing system for direction detection of an object using neural network

The purpose of this study is to develop a high-speed and robust image processing system composed of neural network for detecting and discriminating twenty directions of an object which is randomly laid and piled up on a horizontal plane. Our proposed direction detection system was designed to consist of two modules of neural networks; one is an edge detection module and the other is a direction detection module. An edge image is extracted from an original image taken with a CCD camera and the object direction is detected from the edge image. The edge detection module could detect edges when differences of gray level between the object and the background were larger than thirteen at any gray level of the object. Discrimination ratio of object direction was successful at 93.5%, when one object was laid on an arbitrary place in the area.