What is it about?
Image edge detection is an important task in image processing and pattern recognition. Edges in digital images signify image discontinuities and traditionally gradient information is utilized in finding possible edge pixels. By using multiscale gradient maps we obtain better edge localization and robust edge maps and local thresholding with Fisher information helps obtain better detection.
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Why is it important?
Edge detection is an integral part in various image processing, computer vision, and pattern recognition tasks. Edges visually represent image discontinuities whereby object boundaries can be discerned. Automatic edge detection is an important research area which is still open. Majority of the automatic image edge detection methods rely on derivatives and gradients, in this work we combine multiscale gradients along with non-parametric Fisher information measure.
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This page is a summary of: Multiscale Gradient Maps Augmented Fisher Information-Based Image Edge Detection, IEEE Access, January 2020, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/access.2020.3013888.
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