Automatic Prior Shape Selection for Image Segmentation

Weihong Guo; Jing Qin; Sibel Tari

doi:10.1007/978-3-319-16348-2_1

Automatic Prior Shape Selection for Image Segmentation

Weihong Guo, Jing Qin, Sibel Tari

Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

3 Scopus citations

Abstract

Segmenting images with occluded and missing intensity information is still a difficult task. Intensity based segmentation approaches often lead to wrong results. High vision prior information such as prior shape has been proven to be effective in solving this problem. Most existing shape prior approaches assume known prior shape and segmentation results rely on the selection of prior shape. In this paper, we study how to do simultaneous automatic prior shape selection and segmentation in a variational scheme.

Original language	English
Title of host publication	Association for Women in Mathematics Series
Pages	1-8
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-319-16348-2_1
State	Published - 2015

Publication series

Name	Association for Women in Mathematics Series
Volume	1
ISSN (Print)	2364-5733
ISSN (Electronic)	2364-5741

Bibliographical note

Publisher Copyright:
© 2015, Springer International Publishing Switzerland & The Association for Women in Mathematics.

Keywords

Base Segmentation Model
Data Fidelity Term
Image Segmentation
Prior Shape
Sparse Optimization

ASJC Scopus subject areas

General Mathematics
Gender Studies

Access to Document

10.1007/978-3-319-16348-2_1

Cite this

@inbook{3b86a2455c6046e7ba7d3959e2558938,

title = "Automatic Prior Shape Selection for Image Segmentation",

abstract = "Segmenting images with occluded and missing intensity information is still a difficult task. Intensity based segmentation approaches often lead to wrong results. High vision prior information such as prior shape has been proven to be effective in solving this problem. Most existing shape prior approaches assume known prior shape and segmentation results rely on the selection of prior shape. In this paper, we study how to do simultaneous automatic prior shape selection and segmentation in a variational scheme.",

keywords = "Base Segmentation Model, Data Fidelity Term, Image Segmentation, Prior Shape, Sparse Optimization",

author = "Weihong Guo and Jing Qin and Sibel Tari",

note = "Publisher Copyright: {\textcopyright} 2015, Springer International Publishing Switzerland & The Association for Women in Mathematics.",

year = "2015",

doi = "10.1007/978-3-319-16348-2_1",

language = "English",

series = "Association for Women in Mathematics Series",

pages = "1--8",

booktitle = "Association for Women in Mathematics Series",

}

TY - CHAP

T1 - Automatic Prior Shape Selection for Image Segmentation

AU - Guo, Weihong

AU - Qin, Jing

AU - Tari, Sibel

PY - 2015

Y1 - 2015

N2 - Segmenting images with occluded and missing intensity information is still a difficult task. Intensity based segmentation approaches often lead to wrong results. High vision prior information such as prior shape has been proven to be effective in solving this problem. Most existing shape prior approaches assume known prior shape and segmentation results rely on the selection of prior shape. In this paper, we study how to do simultaneous automatic prior shape selection and segmentation in a variational scheme.

AB - Segmenting images with occluded and missing intensity information is still a difficult task. Intensity based segmentation approaches often lead to wrong results. High vision prior information such as prior shape has been proven to be effective in solving this problem. Most existing shape prior approaches assume known prior shape and segmentation results rely on the selection of prior shape. In this paper, we study how to do simultaneous automatic prior shape selection and segmentation in a variational scheme.

KW - Base Segmentation Model

KW - Data Fidelity Term

KW - Image Segmentation

KW - Prior Shape

KW - Sparse Optimization

UR - http://www.scopus.com/inward/record.url?scp=84991979709&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84991979709&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-16348-2_1

DO - 10.1007/978-3-319-16348-2_1

M3 - Chapter

AN - SCOPUS:84991979709

T3 - Association for Women in Mathematics Series

SP - 1

EP - 8

BT - Association for Women in Mathematics Series

ER -

Automatic Prior Shape Selection for Image Segmentation

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this