Image segmentation using level set and local linear approximations

Authors:
David Rivest-Hénault;Mohamed Cheriet
Affiliations:
Laboratory for Imagery, Vision and Artificial Intelligence, École de technologie supérieure, Montréal, Québec, Canada;Laboratory for Imagery, Vision and Artificial Intelligence, École de technologie supérieure, Montréal, Québec, Canada
Venue:
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Year:
2007

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Abstract

In this article, a new image segmentation model using the level set framework is introduced. The new model is region based, but it uses a new kind of region representative: local linear approximations. When compared to global image segmentation models, not only our new model manages best with local information, it is also more general. Local linear approximations allow for an excellent approximation of smooth region at global scope because they are not restricted to be linear as a whole. Our new model also takes into account the source image gradient direction which can be a good cue toward the desired segmentation. Meanwhile, our model stays simple and relatively fast to compute. The proposed technique has been applied with promising results to both synthetic and real images.