Image Segmentation Using Active Contour Models (Snake Algorithm)

This document provides a comprehensive explanation of the code and methodology for image segmentation using active contour models, commonly referred to as "snakes". This computer vision technique enables precise object boundary delineation through iterative energy minimization. The algorithm works by initializing a contour around the target object and progressively adjusting it by minimizing energy functionals composed of internal energy (maintaining contour smoothness via elasticity and bending constraints) and external energy (driving the contour toward image gradients using edge-detection filters). Key implementation aspects include gradient calculation using Sobel or Canny operators, energy term weighting parameters, and convergence criteria for the iterative optimization process. The code demonstrates practical applications through functions handling contour initialization, energy computation, and gradient descent optimization. By tuning energy coefficients and iteration parameters, the method achieves robust segmentation resistant to noise and partial occlusions. The documentation further covers required libraries (like OpenCV or MATLAB's Image Processing Toolbox), core functions for matrix operations and visualization, and integration techniques for real-world image processing pipelines. This thorough exploration equips researchers and developers to implement active contour models for advanced image analysis in projects spanning medical imaging, autonomous systems, and material science.