Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape registration by simultaneously optimizing representation and transformation
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
A new benchmark for shape correspondence evaluation
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Open-Curve shape correspondence without endpoint correspondence
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Pre-organizing Shape Instances for Landmark-Based Shape Correspondence
International Journal of Computer Vision
| Hi-index | 0.00 |
The growing usage of statistical shape analysis in medical imaging calls for effective methods for highly accurate shape correspondence. This paper presents a novel landmark-based method to correspond a set of 2D shape instances in a nonrigid fashion. Different from prior methods, the proposed method combines three important factors in measuring the shape-correspondence error: landmark-correspondence error, shape-representation error, and shape-representation compactness. In this method, these three important factors are explicitly handled by the landmark sliding, insertion, and deletion operations, respectively. The proposed method is tested on several sets of structural shape instances extracted from medical images. We also conduct an empirical study to compare the developed method to the popular Minimum Description Length method.