Color Image Segmentation Method for Corn Diseases Based on Parallelized Firing PCNN

Abstract:

A kind of unsupervised segmentation processing method based on parallelized firing PCNN algorithm was proposed. The color images of corn disease were segmented by improved parallelized firing PCNN which the normalized L+U as external stimulus input, the integrated information of the geometric distance and the color difference between neighboring pixels as the PCNN coupling value, the minimum color contrast of color vector as the criteria of the best segmentation results, in parallel with improved disease of maize PCNN to segment color images. The segmentation experiments which 100 images of four kinds of diseases showed that the method could better segment the diseased regions with high fitness and low complexity parameters.