NDLI: AntSeg: The Application of Ant Colony Optimization to Interactive Image Segmentation

Content Provider	IEEE Xplore Digital Library
Author	Beraldi Versuti, T.A. Flores, F.C. Mulati, M.H. Polidorio, A.M.
Copyright Year	2012
Description	Author affiliation: Dept. of Inf., State Univ. of Maringa (UEM), Maringa, Brazil (Beraldi Versuti, T.A.; Flores, F.C.; Polidorio, A.M.) \|\| Dept. of Comput. Sci., Midwest State Univ. (UNICENTRO), Guarapuava, Brazil (Mulati, M.H.)
Abstract	The computational process of image segmentation performs the partitioning of it in disjoint regions by the specification and application of specific criteria. This process can be automatic or interactive. The interactive process allows the user interference in this process by means of the insertion of new criteria at any instant of the process execution. This new criterion can lead to the reaching of more appropriate results for the solution to the problem. However, interactive process application requires the user to have a high ability, context knowledge and accuracy executing the tasks. The interactive process can include facilities by allowing that some user actions may be executed with relative accuracy. Using computational resources, such task is made easier, since a computer can perform with more accuracy and speed, from the guides interactively defined by the user. The literature presents many methods that perform this task, among them the live wire, intelligent scissors and the interactive watershed with markers. The first two use a minimum cost system, in other words, optimization, to their working. This paper proposes an interactive method applied to image segmentation that combines the artificial ant colonies behavior and the image gradient technique. The gradient, supplied by heuristic rules, is the surface the ants search the path to the desired solution. The segmentation starts with the imposition of an initial point at the border of the object of interest and, by the user adding new points along its border, new segments belonged to object border are conquered. The proposed method shows a high repeatability of the segmentation task.
Starting Page	105
Ending Page	113
File Size	446672
Page Count	9
File Format	PDF
ISSN	15224902
e-ISBN	9781479929382
DOI	10.1109/SCCC.2012.20
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-12
Publisher Place	Chile
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Image segmentation Ant colony optimization interactive image segmentation Accuracy morphological gradient Image color analysis Computational modeling ant colony optimization Color
Content Type	Text
Resource Type	Article

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