| This thesis describes using parallel processing within a hierarchy of hexagonal grids to achieve rapid recognition of patterns. A seven-pixel basic hexagonal neighborhood, a sixty-one-pixel superneighborhood and pyramids of a 2-to-4 area ratio are employed. The hexagonal network achieves improved accuracy over the square network for object boundaries. The hexagonal grid with less directional sensitivity is a better approximation of the human vision grid, is more suited to natural scenes than the square grid and avoids the 4-neighbor/8-neighbor problem. Parallel processing in image analysis saves considerable time versus the traditional line-by-line method. Hexagonal parallel processing combines the optimum hexagonal geometry with the parallel structure. Our work has surveyed behavior and internal properties to construct the image on the different level of hexagonal pixel grids in a parallel computation scheme. A computer code has been developed to detect edges of digital images of real objects taken with a CCD camera within a hexagonal grid at any level. The algorithm uses the differences of the local gray level and those of its six neighbors, and is able to determine the boundary of a digital image in parallel. Also a series of algorithms and techniques have been built up to manage edge linking, feature extraction, etc. The digital images obtained from the improved CRS digital image processing system are a good approximation to the images which would be obtained with a real physical hexagonal grid. We envision that our work done within this little-known area will have some important applications in real-time machine vision. A parallel two-layer hexagonal-array retina has been designed to do pattern recognition using simple operations such as differencing, rationing, thresholding, etc. which may occur in the human retina and other biological vision systems. |
