An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Image Processing Using Pulse-Coupled Neural Networks
Image Processing Using Pulse-Coupled Neural Networks
Review article: Review of pulse-coupled neural networks
Image and Vision Computing
Characteristic analysis of Otsu threshold and its applications
Pattern Recognition Letters
Dynamic Measurement of Computer Generated Image Segmentations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perfect image segmentation using pulse coupled neural networks
IEEE Transactions on Neural Networks
A New Automatic Parameter Setting Method of a Simplified PCNN for Image Segmentation
IEEE Transactions on Neural Networks
Review: Pulse coupled neural networks and its applications
Expert Systems with Applications: An International Journal
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The pulse-coupled neural network (PCNN) is widely used in image segmentation. However, the determination of parameter values in the PCNN framework is an unavoidable and trivial task that may cause neurons to behave unexpectedly, thus affecting segmentation performance. Therefore, this paper presents an efficient iterative algorithm using a modified PCNN for automatic image segmentation. In contrast to existing PCNN models, a new neural threshold was first established for the modified PCNN instead of a general dynamic threshold, allowing for greater efficiency in controlling the pulse output. Besides, a varying linking coefficient value was constructed for efficiently adjusting the neural behavior. By incorporating the Bayes clustering method, it thereby extends the feasibility of the model for the extraction of targets with inhomogeneous brightness, thus resulting in a simpler iterative algorithm for segmentation. Experiments on real-world infrared images demonstrate the efficiency of our proposed model. Moreover, compared with simplified PCNN models and classic segmentation methods, the proposed model shows fewer misclassification errors and higher segmentation performance.