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Contour detection refined by a sparse reconstruction-based discrimination method

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalSignal, Image and Video Processing
Early online date21 Jul 2017
Accepted/In press11 Jul 2017
E-pub ahead of print21 Jul 2017


King's Authors


Sparse representations have been widely used for many image processing tasks. In this paper, a sparse reconstruction-based discrimination (SRBD) method, which was previously proposed for the classification of image patches, is utilized to improve boundary detection in colour images. This method is applied to refining the results generated by three different algorithms: a biologically inspired method, and two state-of-the-art algorithms for contour detection. All of the contour detection results are evaluated by the BSDS300 and BSDS500 benchmarks using the quantitative measures: F-score, ODS, OIS and AP. Evaluation results shows that the performance of each algorithm is improved using the proposed method of refinement with at least one of the quantitative measures increased by 0.01. In particularly, even two state-of-the-art algorithms are slightly improved by applying the SRBD method to refine their contour detection results.

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