On active contour models and balloons
CVGIP: Image Understanding
A fast algorithm for active contours and curvature estimation
CVGIP: Image Understanding
Object Matching Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Flow: A Framework of Boundary Detection and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Affine Invariant Detection: Edges, Active Contours, and Segments
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
A nonlinear model for fractal image coding
IEEE Transactions on Image Processing
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There have been many attempts to improve the original Snake algorithm by Kass et al. to enhance its ability to locate object boundaries with sharp corners or concave parts. But most of these variants of the Snake model require introducing additional external forces or modifying internal energy terms, all of which necessitate cumbersome fine-tuning by users for optimal performance. In this paper, we present a mathematical formulation for a new algorithm that embeds a domain transformation mapping within the Snake algorithm. The domain transformation step serves to render the object contour more convex and hence is more amenable to be better represented by the Snake contour. Analysis of the new algorithm is carried out which facilitated further enhancements to our technique, rendering a final algorithm that is computationally efficient and is easy and flexible to use. Our approach has been tested with very encouraging experimental results.