Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Game-Theoretic Integration for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The 3L Algorithm for Fitting Implicit Polynomial Curves and Surfaces to Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformable Shape Detection and Description via Model-Based Region Grouping
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching and Retrieval of Distorted and Occluded Shapes Using Dynamic Programming
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
Nonlinear Shape Statistics in Mumford-Shah Based Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Elastic Model Based Non-rigid Registration Incorporation Statistical Shape Information
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
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A novel approach for object segmentation and recognition is presented. The aim of this approach is to select a proper shape model from a model set to guide object segmentation. The process of model selection, which is based on the shape similarity between the target object and shape models, is then used for object recognitions. The integrated process of object segmentation and recognition is formulated as a constrained contour energy minimisation problem. The solution derived from this formulation produces an integrated searching process consisting of two iteratively alternating procedures of contour evolution and shape matching. The process stops at a final contour together with a shape distance measure to an object model for recognition. Successful illustrative results on both segmentation and recognition are reported.