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

Abstract 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.

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