Snake Algorithm or Active Contour Models

In the field of computer vision, the Snake Algorithm (also known as Active Contour Models) is a widely-used image segmentation technique. The core algorithm operates by establishing an energy field around an initial contour, then iteratively deforming the contour along the energy gradient to progressively approximate target boundaries. A typical implementation involves defining energy functions comprising internal energy (contour continuity and curvature constraints) and external energy (image gradient magnitude). The minimization process often employs optimization methods like greedy algorithm or gradient descent. For enhanced accuracy and stability, practical implementations frequently integrate preprocessing techniques such as morphological operations for noise removal and Gaussian filtering for gradient computation. Key functions in libraries like OpenCV (e.g., cv2.contourArea()) may assist in evaluating convergence criteria. As a versatile segmentation tool, this algorithm finds extensive applications in medical image analysis, object tracking, and machine vision systems where boundary precision is critical.