Chan-Vese Reformulation for Selective Image Segmentation
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.
- Publication:
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Journal of Mathematical Imaging and Vision
- Pub Date:
- October 2019
- DOI:
- 10.1007/s10851-019-00893-0
- arXiv:
- arXiv:1811.08751
- Bibcode:
- 2019JMIV...61.1173R
- Keywords:
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- Computer Science - Computer Vision and Pattern Recognition;
- Mathematics - Numerical Analysis
- E-Print:
- To appear in the Journal of Mathematical Imaging and Vision 2019. (23 pages, 19 figures)