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Chan-Vese Reformulation for Selective Image Segmentation.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Roberts, Michael 

Abstract

Selective segmentation involves incorporating user input to partition an image into foreground and background, by discriminating between objects of a similar type. Typically, such methods involve introducing additional constraints to generic segmentation approaches. However, we show that this is often inconsistent with respect to common assumptions about the image. The proposed method introduces a new fitting term that is more useful in practice than the Chan-Vese framework. In particular, the idea is to define a term that allows for the background to consist of multiple regions of inhomogeneity. We provide comparative experimental results to alternative approaches to demonstrate the advantages of the proposed method, broadening the possible application of these methods.

Description

Keywords

cs.CV, cs.CV, cs.NA, math.NA

Journal Title

J Math Imaging Vis

Conference Name

Journal ISSN

0924-9907
1573-7683

Volume Title

61

Publisher

Springer Science and Business Media LLC
Sponsorship
Engineering and Physical Sciences Research Council (EP/K032208/1)