Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Prior Shapes in Geometric Active Contours in a Variational Framework
International Journal of Computer Vision
Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Interactive Graph Cut Based Segmentation with Shape Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Level Set Based Shape Prior Segmentation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation
International Journal of Computer Vision
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Prior-based Segmentation and Shape Registration in the Presence of Perspective Distortion
International Journal of Computer Vision
A Variational Model for Interactive Shape Prior Segmentation and Real-Time Tracking
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Converting level set gradients to shape gradients
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
One-Shot integral invariant shape priors for variational segmentation
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
IEEE Transactions on Signal Processing
An FFT-based technique for translation, rotation, and scale-invariant image registration
IEEE Transactions on Image Processing
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Image segmentation with one shape prior is an important problem in computer vision. Most algorithms not only share a similar energy definition, but also follow a similar optimization strategy. Therefore, they all suffer from the same drawbacks in practice such as slow convergence and difficult-to-tune parameters. In this paper, by reformulating the energy cost function, we establish an important connection between shape-prior based image segmentation with intensity-based image registration. This connection enables us to combine advanced shape and intensity modeling techniques from segmentation society with efficient optimization techniques from registration society. Compared with the traditional regularization-based approach, our framework is more systematic and more efficient, able to converge in a matter of seconds. We also show that user interaction (such as strokes and bounding boxes) can easily be incorporated into our algorithm if desired. Through challenging image segmentation experiments, we demonstrate the improved performance of our algorithm compared to other proposed approaches.