MATLAB Implementation of Snake Algorithm for Contour Extraction

The snake algorithm is a contour extraction technique that simulates a snake-like curve evolution to detect object boundaries. It operates by minimizing an energy function comprising internal forces (controlling curve smoothness) and external forces (driving the curve toward image edges). In MATLAB implementation, key functions typically include: - Gradient calculation using imgradient or edge detection filters - Energy minimization through iterative optimization methods - Constraint handling for boundary conditions and curve continuity The algorithm initiates with a starting point as the snake's head, then progressively extends the curve according to predefined energy minimization rules. Its advantages include adaptability to various object shapes and sizes, with particularly high accuracy for shapes featuring gentle edge transitions (angles ≤90°). This makes snake algorithms valuable in computer vision applications for precise boundary detection in medical imaging, object recognition, and industrial inspection systems. The MATLAB implementation often utilizes active contour functions (activecontour) with customizable parameters for elasticity, stiffness, and convergence criteria.