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. These models are oscillating%2C spatio-temporal models stimulated with images to produce several time-based responses. This paper reviews PCNN's state of the art%2C covering its mathematical formulation%2C variants%2C 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%2C including image segmentation%2C edge detection%2C medical imaging%2C image fusion%2C image compression%2C object recognition%2C 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.?quality=80&w=800)
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