A Review of Pulse-Coupled Neural Network Applications in Computer Vision and Image Processing
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929369259180032 |
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| author | Rafi, Nurul Rivas, Pablo |
| author_facet | Rafi, Nurul Rivas, Pablo |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_00239 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Review of Pulse-Coupled Neural Network Applications in Computer Vision and Image Processing Rafi, Nurul Rivas, Pablo Computer Vision and Pattern Recognition Machine Learning Neural and Evolutionary Computing I.4.6 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. |
| title | A Review of Pulse-Coupled Neural Network Applications in Computer Vision and Image Processing |
| topic | Computer Vision and Pattern Recognition Machine Learning Neural and Evolutionary Computing I.4.6 |
| url | https://arxiv.org/abs/2406.00239 |