A Review of Pulse-Coupled Neural Network Applications in Computer Vision and Image Processing
It provides a comprehensive overview of PCNNs for researchers in computer vision and image processing, but it is incremental as it reviews existing work without introducing new methods.
This paper reviews pulse-coupled neural networks (PCNNs), covering their mathematical formulation, variants, and applications in computer vision and image processing, such as image segmentation and edge detection, with results indicating they generate useful perceptual information for various tasks.
Research in neural models inspired by mammal's visual cortex has led to many spiking neural networks such as pulse-coupled neural networks (PCNNs). These models are oscillating, spatio-temporal models stimulated with images to produce several time-based responses. This paper reviews PCNN's state of the art, covering its mathematical formulation, variants, and other simplifications found in the literature. We present several applications in which PCNN architectures have successfully addressed some fundamental image processing and computer vision challenges, including image segmentation, edge detection, medical imaging, image fusion, image compression, object recognition, and remote sensing. Results achieved in these applications suggest that the PCNN architecture generates useful perceptual information relevant to a wide variety of computer vision tasks.