An image segmentation algorithm based on double-layer pulse-coupled neural network model for kiwifruit detection

摘要

Finding a universal and accurate image segmentation algorithm for kiwifruit detection under varying illumination and complex background has become one of the most challenging problems in machine vision research. In this study, a robust segmentation algorithm based on a double-layer pulse-coupled neural network (PCNN) model is proposed. First of all, an improved PCNN merged with the image frequency-tuned saliency is devised as a basic structure. Secondly, in the red-green-blue color mode, the optimal color-difference information of a kiwifruit image is determined in the first layer of this double-layer PCNN. Then, enhanced hue features are fused with these optimal color-difference features by the total variation model. Finally, the target regions are built by the re-segmentation of the second layer of this double-layer PCNN. Experimental results demonstrate that the proposed algorithm significantly outperforms the typical existing algorithms in terms of the subjective visual effect and the objective quantitative evaluation. (C) 2019 Elsevier Ltd. All rights reserved.