A robust eigenspace method for high-speed massively parallel vision systems

Image processing systems, each consisting of massively parallel photodetectors and digital processing elements on a monolithic circuit, are being developed by several researchers. This approach is promising for applications in robot vision and visual inspection. Some early vision-like processing algorithms, such as edge-detection, smoothing and thinning, are installed in the vision systems. However, they are not sufficient for applications because their output is in the form of pattern information, so that, in order to respond to input, some feature values are required to be extracted from the pattern. We propose a method for extracting feature values associated with images in a massively parallel vision system. The feature can be a complex one, such as the rotation angle of an object, if the feature value changes continuously as the image changes. The method is based on the eigenspace method, but modified to be robust. In addition, we propose some computation accelerating methods and report some experimental results