Fast automatic saliency map driven geometric active contour model for color object segmentation

Nguyen Tran Lan Anh, Vo Quang Nhat, Kodirov Elyor, Soo Hyung Kim, Guee Sang Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Segmenting objects from color images to obtain useful information is a challenging research area recently. In this paper, a novel algorithm by combining a saliency map with an extension of a geometric active contour model is proposed to automatically segment the object of interest. The saliency map is first generated from the input image by a histogram based contrast method. The most salient regions are then detected as dominant parts of the object. After that, a contour is initialized using salient regions determined. Finally, by applying a geometric active contour model, the contour starts evolving iteratively to segment object boundaries. Experimental results attained on various natural scene images have shown that our proposed method is able to not only replace manual initialized contour and improve the accuracy, noise robustness of segmentation but converge to an optimal solution earlier than recent active contour models as well.

Original languageEnglish
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Pages2557-2560
Number of pages4
StatePublished - 2012
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: Nov 11 2012Nov 15 2012

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference21st International Conference on Pattern Recognition, ICPR 2012
Country/TerritoryJapan
CityTsukuba
Period11/11/1211/15/12

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