Pulse Coupled Neural Networks for Image Processing – A Review

  • T. Kalaiselvi, K. Rahimunnisa, A. Sumaiya Begum

Abstract

The Pulse Coupled Neural Network (PCNN)  is a neural network algorithm that produces a series of binary pulse images when stimulated with a gray scale or color image. The PCNN is fundamentally unique from many of the standard techniques being used today. Pulse coupled neural networks has been proven as a highly effective technique for various applications. PCNN applications within the field of image processing like image segmentation, image enhancement, image fusion, object and edge detection etc., then applications in other fields within the previous decade are reviewed in this paper. The present status of the PCNN and a few modified models are briefly introduced. Subsequently, some prevailing problems are summarized then the trend of the PCNN is acknowledged. Considering there are ample amount of publications about the pulse-coupled neural networks, we summarize main approaches and means interesting parts of the PCNN researches instead of contemplate to travel into details of specific algorithms or describe results of comparative experiments.

Published
2020-06-01
How to Cite
T. Kalaiselvi, K. Rahimunnisa, A. Sumaiya Begum. (2020). Pulse Coupled Neural Networks for Image Processing – A Review. International Journal of Advanced Science and Technology, 29(7s), 4411 - 4416. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/25646