New geodesic distance transforms for gray-scale images
Pattern Recognition Letters
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation and comparison of different segmentation algorithms
Pattern Recognition Letters
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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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
ACM SIGGRAPH 2005 Papers
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Image Analysis and Mathematical Morphology
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CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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IEEE Transactions on Computers
2D Euclidean distance transform algorithms: A comparative survey
ACM Computing Surveys (CSUR)
GeoS: Geodesic Image Segmentation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Geodesic Matting: A Framework for Fast Interactive Image and Video Segmentation and Matting
International Journal of Computer Vision
Geodesic image and video editing
ACM Transactions on Graphics (TOG)
Soft Color Segmentation and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive Image Segmentation via Adaptive Weighted Distances
IEEE Transactions on Image Processing
Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking
IEEE Transactions on Image Processing
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We present a shape prior embedded geodesic distance transform for image and video segmentation. Whilst the existing segmentation algorithms have achieved impressive performance in many examples, they may fail in cases where the quality of the likelihood images is not satisfactory, or where multiple similar objects are in close proximity to one another. To deal with these problems, we embed shape prior knowledge in an image segmentation algorithm based on geodesic distance transform. Different from other segmentation methods, the proposed geodesic distance transform morphology operators consider three factors simultaneously: the geometric distance, weighted gradients, and the distance to the boundary of shape priors. As a result, it provides segmentation in line with the real shape of a particular kind of object. We also propose an effective shape prior extraction method that compute shape priors automatically. The proposed algorithm demonstrates positive results for many challenging images and video sequences in our experiments.