A Simplified Pulse-Coupled Neural Network for Cucumber Image Segmentation

The pulse-coupled neural network (PCNN) algorithm is an efficient method widely used in image segmentation. Parameters adjusting is usually difficult in a classic model of PCNN. In this study the pulse-coupled neural network model was simplified for optimal segmentation by reducing the number of parameters of PCNN. In addition, the local standard deviation was utilized for adjusting the connection strength coefficient adaptively. The simplified PCNN was used for separating the cucumber from complex background in a cucumber image effectively. To evaluate the performance of this algorithm, a simple evaluation method was designed for evaluating the segmentation image. The experimental results show that the average rate of correct segmentation reaches up to 82.4%.

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