Getting a morphological tree of shapes for multivariate images: Paths, traps, and pitfalls - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2014

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.
Fichier principal
Vignette du fichier
carlinet.2014.icip.pdf (594.87 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01476227 , version 1 (24-02-2017)

Identifiers

Cite

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⟩
140 View
290 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More