Skip to Main content Skip to Navigation
Conference papers

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.
Complete list of metadatas

Cited literature [22 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01510062
Contributor : Rémi Giraud <>
Submitted on : Saturday, May 20, 2017 - 3:40:43 PM
Last modification on : Monday, July 22, 2019 - 11:00:09 AM

File

Giraud_SRC_ICIP17.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution - NonCommercial 4.0 International License

Identifiers

  • HAL Id : hal-01510062, version 2

Collections

Citation

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

Share

Metrics

Record views

519

Files downloads

537