Statistical Gaussian Model of Image Regions in Stochastic Watershed Segmentation

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.
Document type :
Conference papers
Liste complète des métadonnées

Cited literature [20 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01134047
Contributor : Jesus Angulo <>
Submitted on : Sunday, January 17, 2016 - 2:33:09 PM
Last modification on : Monday, November 12, 2018 - 11:00:18 AM
Document(s) archivé(s) le : Monday, April 18, 2016 - 10:11:36 AM

File

GaussianModelStochasticWatersh...
Files produced by the author(s)

Identifiers

Citation

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⟩

Share

Metrics

Record views

123

Files downloads

150