Statistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation - Archive ouverte HAL Access content directly
Conference Papers Year :

Statistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation

Jesus Angulo

Abstract

Stochastic watershed is an image segmentation technique based on mathematical morphology which produces a probability density function of image contours. Estimated probabilities depend mainly on local distances between pixels. This paper introduces a variant of stochastic watershed where the probabilities of contours are computed from a Gaussian model of image regions. In this framework, the basic ingredient is the distance between pairs of regions, hence a distance between normal distributions. Hence several alternatives of statistical distances for normal distributions are compared, namely Bhattacharyya distance, Hellinger metric distance and Wasserstein metric distance.
Fichier principal
Vignette du fichier
GaussianModelStochasticWatershed_GSI15_final.pdf (2.18 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01134047 , version 1 (21-03-2015)
hal-01134047 , version 2 (17-01-2016)

Identifiers

Cite

Jesus Angulo. Statistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation. Second International Conference on Geometric Science of Information, Oct 2015, Palaiseau, France. pp.396-405, ⟨10.1007/978-3-319-25040-3_43⟩. ⟨hal-01134047v2⟩
4717 View
190 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More