Active Shape Model: Implementation and Applications in Computer Vision

Active Shape Model (ASM) is a technique used in image processing and computer vision, primarily for localizing and tracking the shape of target objects. It combines statistical methods with image feature matching to effectively detect and adjust target shapes within images.

Fundamental Principles of ASM Training Phase: First, collect a set of training images with annotated points that define the target shape. By performing Principal Component Analysis (PCA) on these shapes, the main modes of shape variation can be extracted. Search Phase: On test images, ASM adjusts the shape points within local neighborhoods based on the trained model, enabling better alignment with image features (such as edges or gradients).

MATLAB Implementation Approach Data Preparation: Load training data including annotated point coordinates and perform normalization preprocessing. PCA Modeling: Apply PCA to analyze shape variation patterns and extract principal eigenvectors representing shape deformations. Image Matching: Initialize an approximate shape on the test image and iteratively adjust point positions toward high-gradient regions using local profile matching. Convergence Criteria: Set stopping conditions such as maximum iteration count or shape change threshold to terminate optimization.

Extended Applications Medical image analysis (e.g., organ contour detection) Facial landmark localization Industrial inspection (component shape matching)

ASM's strength lies in combining prior knowledge (trained models) with image features, making it suitable for object localization problems with consistent shape patterns.