Paper Info
Title
Fast automatic saliency map driven geometric active contour model for color object segmentation
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.
Year
Venue
Keywords
2012
ICPR
object boundary segmentation,natural scene images,image segmentation,color images,salient regions,automatic object segmentation,histogram based contrast method,color object segmentation,geometric active contour model,natural scenes,fast automatic saliency map driven geometric active contour model,geometry,iterative methods,image colour analysis
Field
DocType
ISSN
Active contour model,Histogram,Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Segmentation-based object categorization,Image segmentation,Robustness (computer science),Artificial intelligence,Salient
Conference
1051-4651
ISBN
Citations 
PageRank 
978-1-4673-2216-4
1
0.38
References 
Authors
3
5
Name
Order
Citations
PageRank
Nguyen Tran Lan Anh161.47
Nhat Quang Vo2134.83
Elyor Kodirov31448.15
Soo-Hyung Kim419149.03
Gueesang Lee520852.71