Grayscale Image Segmentation Using Pulse Coupled Neural Networks
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To accomplish grayscale image segmentation, we can employ Pulse Coupled Neural Networks (PCNN). This approach can be developed and implemented using MATLAB version 6.5 or later development environments. The PCNN algorithm operates through neural pulse synchronization where neurons corresponding to similar intensity regions fire simultaneously, creating segmentation boundaries. Key implementation components include setting neuron linking parameters (β), decay time constants, and threshold adjustments. The MATLAB implementation typically involves matrix operations for efficient neighbor connections and iterative pulse propagation calculations. Through this network architecture, we can precisely locate and extract objects or regions within images by analyzing pulse-coupled activation patterns. Therefore, utilizing Pulse Coupled Neural Networks for grayscale image segmentation represents an effective methodology with biologically-inspired computational advantages.
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