Using Dynamic Programming for Solving Variational Problems in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Priors for Level Set Representations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representation and Detection of Deformable Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Strike a Pose: Tracking People by Finding Stylized Poses
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Body Localization in Still Images Using Hierarchical Models and Hybrid Search
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Representation and matching of articulated shapes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Active shape model based on sparse representation
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
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Nonlinear shape models have been shown to improve the robustness and flexibility of contour-based object segmentation when there are appearance ambiguities between the object and the background. In this paper, we focus on a new search strategy for the shape regularized active contour (ShRAC) model, which adopts existing nonlinear shape models to segment objects that are similar to a set of training shapes. The search for optimal contour is performed by a coarse-to-fine algorithm that iterates between combinatorial search and gradient-based local optimization. First, multi-solution dynamic programming (MSDP) is used to generate initial candidates by minimizing only the image energy. In the second step, a combination of image energy and shape energy is minimized starting from these initial candidates using a local optimization method and the best one is selected. To generate diverse initial candidates while reducing invalid shapes, we apply two pruning methods to the search space of MSDP. Our search strategy combines the advantages of global combinatorial search and local optimization, and has shown excellent robustness to local minima caused by distracting suboptimal solutions. Experimental results on segmentation of different anatomical structures using ShRAC, as well as preliminary results on human silhouette segmentation are provided.