active shape model

Active Shape Model (ASM) is a statistical approach used in computer vision for image segmentation and object recognition. It models deformable shapes, such as anatomical structures in medical imaging or facial features, by learning variations from a training dataset.

### Core Concept ASM works by defining a shape as a set of landmark points that outline an object. These landmarks are trained on multiple annotated images to learn the permissible deformations. The model captures two key aspects: Shape Variation – Uses Principal Component Analysis (PCA) to represent allowable shape changes. Local Appearance – At each landmark, texture profiles (e.g., gradients) are modeled to guide the fitting process.

### Workflow Training Phase: Annotate landmarks across a dataset of images. Align shapes to remove pose variations (scaling, rotation). Use PCA to extract principal modes of shape variation.

Fitting Phase: Initialize the model near the target object in a new image. Iteratively adjust landmarks by searching for the best local texture match. Constrain deformations using the learned statistical shape model.

### Applications Medical Imaging: Segmenting organs (e.g., heart, lungs). Facial Analysis: Detecting facial landmarks for recognition or animation.

### Strengths & Limitations Pros: Robust to noise, handles non-rigid deformations. Cons: Sensitive to initialization; struggles with occlusions or extreme shape outliers.

ASM laid the foundation for advanced techniques like Active Appearance Models (AAMs) and remains relevant for tasks requiring interpretable shape constraints.