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Edge-based method of sharp region extraction from low depth of filed images

Abstract : This paper presents a method for extracting blur/sharp regions of interest (ROI) that benefits of using a combination of edge and region based approaches. It can be considered as a preliminary step for many vision applications tending to focus only on the most salient areas in low depth-of-field images. To localize focused regions, we first classify each edge as either sharp or blurred based on gradient profile width estimation. Then a mean shift oversegmentation allows to label each region using the density of marked edge pixels inside. Finally, the proposed algorithm is tested on a dataset of high resolution images and the results are compared with the manually established ground truth. It is shown that the given method outperforms known state-of-the-art techniques in terms of F-measure. The robustness of the method is confirmed by means of additional experiments on images with different values of defocus degree.
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Submitted on : Tuesday, December 11, 2012 - 12:36:28 PM
Last modification on : Saturday, June 25, 2022 - 10:54:19 AM
Long-term archiving on: : Tuesday, March 12, 2013 - 5:10:17 AM


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  • HAL Id : ujm-00763704, version 1



Nathalia Neverova, Hubert Konik. Edge-based method of sharp region extraction from low depth of filed images. Visual Communications and Image Processing (VCIP2012), Nov 2012, San Diego, United States. 6 p. ⟨ujm-00763704⟩



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