Chan-Vese Reformulation for Selective Image Segmentation

Autor:	Roberts, Michael, Spencer, Jack
Rok vydání:	2018
Předmět:	Computer Science - Computer Vision and Pattern Recognition Mathematics - Numerical Analysis
Druh dokumentu:	Working Paper
DOI:	10.1007/s10851-019-00893-0
Popis:	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 comparitive experimental results to alternative approaches to demonstrate the advantages of the proposed method, broadening the possible application of these methods. Comment: To appear in the Journal of Mathematical Imaging and Vision 2019. (23 pages, 19 figures)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1811.08751 Zobrazit plný text záznamu View this record from Arxiv