A Comprehensive Study on Image Segmentation Using Pulse-Coupled Neural Networks

A Comprehensive Study on Image Segmentation Using Pulse-Coupled Neural Networks

Jayalakshmi K P1, K Aarya Shri2, Priya Seema Miranda3

123Department of ECE, St Joseph Engineering College

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Abstract - Pulse-Coupled Neural Network (PCNN) operates as a matrix of neurons, each uniquely corresponding to a pixel in the image being processed. Unlike traditional neural networks, PCNN does not require any training, making it highly suitable for image segmentation tasks. However, the complexity of PCNN arises from its numerous parameters, making parameter selection a challenging endeavor. In this work, we introduce a simplified PCNN architecture that includes an automatic parameter determination method. This approach is specifically designed for binary image segmentation and has been tested on various images, yielding results with distinct and desirable features. The proposed network iterates only four times, enhancing its efficiency. During pre-processing, RGB images are converted to HSV color space, and the V component undergoes further processing. This component is first filtered using an averaging filter, followed by a sharpening filter. The parameters for the PCNN are then generated automatically, eliminating the need for manual selection and making the network highly suitable for real-time image processing. The performance of the proposed network has been verified through tests on a variety of images.

Key Words:  Neural Networks, Pulse Coupled Neural Networks, Image Processing