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Robust Shape Regularity Criteria for Superpixel Evaluation

Abstract : Regular decompositions are necessary for most superpixel-based object recognition or tracking applications. So far in the literature, the regularity or compactness of a superpixel shape is mainly measured by its circularity. In this work, we first demonstrate that such measure is not adapted for super-pixel evaluation, since it does not directly express regularity but circular appearance. Then, we propose a new metric that considers several shape regularity aspects: convexity, balanced repartition, and contour smoothness. Finally, we demonstrate that our measure is robust to scale and noise and enables to more relevantly compare superpixel methods.
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Contributor : Rémi Giraud Connect in order to contact the contributor
Submitted on : Saturday, May 20, 2017 - 3:40:43 PM
Last modification on : Tuesday, March 8, 2022 - 9:26:02 AM


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  • HAL Id : hal-01510062, version 2



Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis. Robust Shape Regularity Criteria for Superpixel Evaluation. IEEE International Conference on Image Processing (ICIP'17), Sep 2017, Beijing, China. pp.3455-3459. ⟨hal-01510062⟩



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