Boundary-Free Active Contour Model (CV Model)

This text describes the Chan-Vese (CV) model technique, which automatically segments image boundaries while maintaining independence from initial level set positioning. The algorithm operates by minimizing an energy functional based on regional intensity means, typically implemented through partial differential equations and level set evolution. Key computational steps include initializing a level set function, computing regional statistics, and iteratively evolving the contour until convergence. This technology finds applications across multiple domains including medical image processing, autonomous driving systems, and industrial quality inspection. It significantly enhances image analysis efficiency by automating segmentation processes and reducing manual intervention requirements. The CV model's implementation typically involves MATLAB or Python coding with functions for energy computation, level set updates, and regional mean calculations. Consequently, research and application of the CV model present broad prospects and substantial significance in computer vision applications.