Explore MATLAB source code curated for "snake算法" with clean implementations, documentation, and examples.
A MATLAB-based Snake algorithm implementation designed for image segmentation and edge detection applications, featuring customizable energy minimization functions and active contour modeling.
Snake Algorithm or Active Contour Models are image segmentation techniques requiring an initial contour input that iteratively deforms along energy minimization directions to converge toward optimal boundaries. Implementation typically involves calculating internal (contour smoothness) and external (image gradient) energy terms using functions like cv2.SnakeImage() in OpenCV.
Implementation of snake algorithm using MATLAB's original approach for detecting eyes and mouth - MATLAB snake algorithm with code demonstration and morphological processing techniques
MATLAB-based implementation of the snake algorithm using the original approach for detecting eyes and mouth features in facial images
Implementation of snake algorithm for image processing, including practical examples and code-related explanations of key functions and edge detection techniques.
A collection of eight MATLAB M-files demonstrating snake algorithm variations, including balloon segmentation, distance-based edge mapping, and other image processing applications with detailed code implementations
A snake algorithm implementation for contour tracking that can be applied in image processing fields such as image segmentation and object recognition, featuring dynamic curve evolution through energy minimization.
Implementation of snake algorithm for contour extraction with dialog interface generation, featuring button-activated processing functionalities
Complete MATLAB source code implementation of the snake algorithm from William & Shah's seminal paper, including detailed documentation and the original research paper. This resource provides excellent reference value for understanding active contour models, featuring implementation of key functions like energy minimization, gradient descent optimization, and contour evolution.