Date of Award


Publication Type

Master Thesis

Degree Name



Computer Science

First Advisor

Boubakeur Boufama (Computer Science)


Computer Science.



Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.


Image segmentation is responsible for partitioning an image into sub-regions based on a preferred feature. Active contour models have widely been used for image segmentation. The use of level set theory has enriched the implementation of active contours with more flexibility and simplicity. The past models of active contours rely on a gradient based stopping function to stop the curve evolution. However, when using gradient information for noisy and textured images, the evolving curve may pass through, or stop far from the salient object boundaries. We propose using a polarity based stopping function. Comparing to the gradient information, the polarity information accurately distinguishes the boundaries or edges of the salient objects more precisely. With combining the polarity information with the active contour model, we obtain an efficient active contour model for salient object detection. Experiments are performed on several images to show the advantage of the polarity based active contour.