Edges as outliers: Anisotropic smoothing using local image statistics
Edges are viewed as statistical outliers with respect to local image gradient magnitudes. Within local image regions we compute a robust statistical measure of the gradient variation and use this in an anisotropic diffusion framework to determine a spatially varying edge- stopping" parameter σ. We show how to determine this parameter for two edge-stopping functions described in the literature (Perona-Malik and the Tukey biweight). Smoothing of the image is related the local texture and in regions of low texture, small gradient values may be treated as edges whereas in regions of high texture, large gradient magni- tudes are necessary before an edge is preserved. Intuitively these results have similarities with human perceptual phenomena such as masking and popout. Results are shown on a variety of standard images.
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- Artificial Intelligence & Image Processing
- 46 Information and computing sciences
Citation
Published In
DOI
EISSN
ISSN
ISBN
Publication Date
Volume
Start / End Page
Related Subject Headings
- Artificial Intelligence & Image Processing
- 46 Information and computing sciences