Eikonal-Based region growing for efficient clustering - Archive ouverte HAL Access content directly
Journal Articles Image and Vision Computing Year : 2014

Eikonal-Based region growing for efficient clustering

(1) , (1) , (1) , (2)
1
2

Abstract

We describe an Eikonal-based algorithm for computing dense oversegmentation of an image, often called superpixels. This oversegmentation respects local image boundaries while limiting undersegmentation. The proposed algorithm relies on a region growing scheme, where the potential map used is not fixed and evolves during the diffusion. Refinement steps are also proposed to enhance at low cost the first oversegmentation. Quantitative comparisons on the Berkeley dataset show good performance on traditional metrics over current state-of-the art superpixel methods.
Fichier principal
Vignette du fichier
IVC_2014.pdf (9.22 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01134406 , version 1 (30-03-2015)

Identifiers

Cite

Pierre Buyssens, Isabelle Gardin, Su Ruan, Abderrahim Elmoataz. Eikonal-Based region growing for efficient clustering. Image and Vision Computing, 2014, 32 (12), pp.1045-1054. ⟨10.1016/j.imavis.2014.10.002⟩. ⟨hal-01134406⟩
199 View
267 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More