ASM (Active Shape Model) - PCA-Based Deformable Models

This article discusses key concepts including ASM (Active Shape Model), PCA (Principal Component Analysis), and deformable models. Using hand deformation as a case study, the author collected 18 different hand shapes, each annotated with 72 landmarks. The implementation involves performing Procrustes alignment to normalize the shapes' position, scale, and rotation, followed by PCA analysis to extract principal modes of shape variation.

Notably, ASM represents a statistical shape model algorithm that performs statistical analysis on sample data to learn a model of shape variations. The Active Shape Model, as an enhanced version of ASM, provides greater flexibility in adapting to diverse shape changes through an iterative matching process where the model deforms to fit new images. PCA serves as a fundamental dimensionality reduction technique that identifies orthogonal components capturing the maximum variance in shape data, typically implemented using eigenvalue decomposition of the covariance matrix.

In summary, the concepts and methodologies presented in this article provide essential frameworks for understanding and analyzing shape variations. The author's research makes significant contributions to this field by demonstrating practical applications of statistical shape models in computer vision and pattern recognition tasks.