An Image Segmentation Method Combining Graph Cut (GC) and Active Contour Model (ACM)
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Resource Overview
An integrated image segmentation approach combining Graph Cut (GC) and Active Contour Model (ACM), capable of performing both local and global image segmentation with practical implementation simplicity and efficiency.
Detailed Documentation
This image segmentation method integrates Graph Cut (GC) and Active Contour Model (ACM) to achieve comprehensive segmentation performance. The algorithm leverages GC's global optimization capabilities for energy minimization while utilizing ACM's curve evolution for precise boundary localization. Through iterative energy function optimization - where GC handles regional term minimization and ACM manages boundary term refinement - the method achieves accurate segmentation for both localized regions and entire images. The implementation typically involves initializing contours using ACM's level set method, followed by GC-based graph construction with appropriate data and smoothness terms. This combined approach demonstrates robustness across various segmentation tasks with straightforward parameter tuning and computational efficiency.
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