Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Construction of 3D Shape Models of Femoral Articular Cartilage Using Harmonic Maps
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
A Noise Robust Statistical Texture Model
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
Statistical 2D and 3D Shape Analysis Using Non-euclidean Metrics
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
Probabilistic generative modelling
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Localized maximum entropy shape modelling
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
| Hi-index | 0.00 |
This paper address the problems of generating a low dimensional representation of the shape variation present in a set of shapes represented by a number of landmark points. First, we will present alternatives to the featured Least-Squares Procrustes alignment based on the L驴-norm and the L1-norm. Second, we will define a new shape decomposition based on the Maximum Autocorrelation Factor (MAF) analysis, and investigate and compare its properties to the Principal Components Analysis (PCA). It is shown that Molgedey-Schuster algorithm for Independent Component Analysis (ICA) is equivalent to the MAF analysis. The shape MAF analysis utilises the natural order of landmark points along shape contours.