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

Producción científica: Chapter › revisión exhaustiva

3 Citas (Scopus)

Resumen

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.

Idioma original	English
Título de la publicación alojada	Association for Women in Mathematics Series
Páginas	1-8
Número de páginas	8
DOI	https://doi.org/10.1007/978-3-319-16348-2_1
Estado	Published - 2015

Serie de la publicación

Nombre	Association for Women in Mathematics Series
Volumen	1
ISSN (versión impresa)	2364-5733
ISSN (versión digital)	2364-5741

Nota bibliográfica

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

Financiación

Acknowledgements The authors would like to thank Luminita Vese from the Department of Mathematics at the University of California, Los Angeles for insightful discussions. The joint research is partially funded via US NIH 1R21EB016535-01 to W.G. and TUBITAK 112E208 to S.T.

Financiadores	Número del financiador
TUBITAK	112E208
National Institutes of Health (NIH)	1R21EB016535-01

ASJC Scopus subject areas

General Mathematics
Gender Studies

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Automatic Prior Shape Selection for Image Segmentation

Resumen

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Nota bibliográfica

Financiación

ASJC Scopus subject areas

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