Image thinning using pulse coupled neural network
PCNN-pulse coupled neural network, based on the phenomena of synchronous pulse bursts in the animal visual cortex, is different from traditional artificial neural networks. This paper first introduces a new approach for binary image thinning by using the pulse parallel transmission characteristic of PCNN. The thinning result obtains when pulses emitted by background meet. The criterion of pulse meeting and the criterion of thinning completion are proposed. The computer simulation results of applying the method to thin binary image are present. Comparisons of skeleton structure and execution time with results from other thinning methods are present too. The PCNN skeleton retains more information of original binary image, such as the size of a quadrate, than the result from Zhang and Suen method. The procedure is faster than Arcelli et al. thinning method when the image resolution is from 600 to1800 dpi. Combining with PCNN restoration algorithm (namely PCNN noise-reducing algorithm), the skeletons of the objects in a noisy binary image can be obtained with the accuracy. This paper also expands the application field of PCNN.
pcnn binary image thinning skeleton