MATLAB Implementation of ASM Algorithm

This article explores the ASM algorithm and its MATLAB implementation details. ASM is a widely used computer vision algorithm for modeling shapes of specific regions such as faces, eyes, and mouths. We provide a comprehensive breakdown of the complete ASM workflow, covering initialization, iterative optimization, and implementation principles of each subroutine. The MATLAB code implementation includes key components like statistical shape model generation using Principal Component Analysis (PCA), landmark point initialization, and iterative pose estimation through Procrustes analysis. Additionally, we discuss ASM's strengths and limitations, along with practical performance optimization techniques for real-world applications. The code structure demonstrates how to handle shape normalization, parameter constraints, and convergence checking during the fitting process. This article aims to help readers deepen their understanding of ASM and provide valuable insights for researchers advancing in computer vision.