Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional
International Journal of Computer Vision
Dynamical Statistical Shape Priors for Level Set-Based Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prior Knowledge, Level Set Representations & Visual Grouping
International Journal of Computer Vision
An Experimental Comparison of Discrete and Continuous Shape Optimization Methods
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Total variation minimization and a class of binary MRF models
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
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In this work we present a novel method for detecting multiple objects of interest in one image, when the only available information about these objects are their shape and color. To solve this task we use a global optimal variational approach based on total variation. The presented energy functional can be minimized locally due its convex formulation. To improve the runtime of our algorithm we show how this approach can be scheduled in parallel.Our algorithm works fully automatically and does not need any user interaction. In experiments we show the capabilities in non-artificial images, e.g. aerial or bureau images.