Active Shape Model Implementation

In this documentation, we describe a MATLAB-based implementation of the Active Shape Model. Let's explore how this program operates in detail. The Active Shape Model represents a computer vision technique designed for automatic object identification and localization within images. The program implementation involves training on sample datasets and employs Principal Component Analysis (PCA) for statistical shape modeling. During execution, the Active Shape Model algorithm analyzes new input images and attempts to match them against learned shape variations. Key implementation aspects include landmark point initialization, Procrustes analysis for shape alignment, PCA dimensionality reduction for shape parameterization, and iterative gradient descent search for optimal contour fitting. This technique finds extensive applications in medical image processing, facial recognition systems, and other computer vision domains where precise shape matching is required. The MATLAB implementation typically utilizes functions like pca() for statistical modeling, imgradient() for edge detection, and optimization routines for contour convergence.