Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, EpiSciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Conference papers

Getting a morphological tree of shapes for multivariate images: Paths, traps, and pitfalls

Abstract : The tree of shapes is a morphological tree that provides an high-level hierarchical representation of the image suitable for many image processing tasks. This structure has the desirable properties to be self-dual and contrast-invariant and describes the organization of the objects through level lines inclusion. Yet it is defined on gray-level while many images have multivariate data (color images, multispectral images...) where information are split across channels. In this paper, we propose some leads to extend the tree of shapes on colors with classical approaches based on total orders, more recent approaches based on graphs and also a new distance-based method. Eventually, we compare these approaches through denoising to highlight their strengths and weaknesses and show the strong potential of the new methods compared to classical ones.
Document type :
Conference papers
Complete list of metadata

Cited literature [29 references]  Display  Hide  Download
Contributor : Thierry Géraud Connect in order to contact the contributor
Submitted on : Friday, February 24, 2017 - 4:25:52 PM
Last modification on : Saturday, January 15, 2022 - 3:56:39 AM
Long-term archiving on: : Thursday, May 25, 2017 - 1:54:51 PM


Files produced by the author(s)



Edwin Carlinet, Thierry Géraud. Getting a morphological tree of shapes for multivariate images: Paths, traps, and pitfalls. 21st IEEE International Conference on Image Processing (ICIP), Oct 2017, Paris, France. pp.615 - 619, ⟨10.1109/ICIP.2014.7025123⟩. ⟨hal-01476227⟩



Record views


Files downloads