Level set methods: an overview and some recent results
Journal of Computational Physics
A Simplified pulse-coupled neural network for adaptive segmentation of fabric defects
Machine Vision and Applications
Image and Vision Computing
An adaptive image segmentation method based on a modified pulse coupled neural network
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Perfect image segmentation using pulse coupled neural networks
IEEE Transactions on Neural Networks
Region growing with pulse-coupled neural networks: an alternative to seeded region growing
IEEE Transactions on Neural Networks
New Spiking Cortical Model for Invariant Texture Retrieval and Image Processing
IEEE Transactions on Neural Networks
A New Automatic Parameter Setting Method of a Simplified PCNN for Image Segmentation
IEEE Transactions on Neural Networks
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This paper presents a novel iterative thresholding segmentation method based on a modified pulse coupled neural network (PCNN) for partitioning pixels carefully into a corresponding cluster. In the modified model, we initially simplify the two inputs of the original PCNN, and then construct a global neural threshold instead of the original threshold under the specified condition that the neuron will keep on firing once it begins. This threshold is shown to be the cluster center of a region in which corresponding neurons fire, and which can be adaptively updated as soon as neighboring neurons are captured. We then propose a method for automatically adjusting the linking coefficient based on the minimum weighted center distance function. Through iteration, the threshold can be made to converge at the possible real center of object region, thus ensuring that the final result will be obtained automatically. Finally, experiments on several infrared images demonstrate the efficiency of our proposed model. Moreover, based on comparisons with two efficient thresholding methods, a number of PCNN-based models show that our proposed model can segment images with high performance.