NDLI: An Iterative Thresholding Segmentation Model Using a Modified Pulse Coupled Neural Network

Content Provider	Springer Nature Link
Author	Gao, Chao Zhou, Dongguo Guo, Yongcai
Copyright Year	2013
Abstract	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.
Starting Page	81
Ending Page	95
Page Count	15
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	39
Issue Number	1
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2013-02-27
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Pulse coupled neural network Image segmentation Neural threshold Linking coefficient Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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