Explore MATLAB source code curated for "边缘" with clean implementations, documentation, and examples.
This MATLAB-based program achieves subpixel edge localization with precision of 0.1-0.2 pixels, featuring complete source code and comprehensive documentation. The implementation utilizes advanced interpolation algorithms and gradient-based edge detection methods for enhanced accuracy.
MATLAB-coded programs featuring morphological gradient-based edge detection for binary images and watershed thresholding method for image segmentation, with implementation details on key functions and algorithmic approaches.
A versatile edge detection function capable of implementing Sobel, Prewitt, Roberts, Marr, and Canny operators through parameter configuration. Includes comprehensive design documentation with algorithm explanations and implementation details.
Edge detection using bit consistency followed by image segmentation through K-means clustering and region growing algorithms, with referenced research papers and implementation insights.
Fourier descriptors are used to describe and recognize the shapes of edged objects in images. This MATLAB-based program has been tested and verified as functional, implementing edge detection and Fourier transform algorithms for shape analysis.
A robust video edge detection algorithm implemented in MATLAB for detecting edges in video sequences. This implementation demonstrates effective edge detection with good stability, using techniques like frame-by-frame processing and edge detection operators such as Sobel or Canny. The code structure and algorithm approach provide valuable learning references for video processing applications.
This program employs wavelet transform methodology to extract image contours for registration purposes. The implementation first detects edges using wavelet decomposition, identifies the longest continuous contour, computes curvature at sampled points, and performs data matching to determine optimal transformation parameters for precise image alignment.
Iterative optimal threshold segmentation algorithm for edge detection by finding the best threshold value, with implementation-focused explanations of key computational steps and evaluation metrics.
An effective edge detection method called CORNER for finding corners in intensity images. The algorithm operates through these key steps: 1) Apply Canny edge detector to grayscale image to generate binary edge map, 2) Extract edge contours from the edge map and fill gaps in contours. Implementation typically involves using edge detection functions like edge() with 'Canny' option and contour tracing algorithms.
The snake algorithm, used for extracting object contours, demonstrates high recognition rates for general shapes (as long as edge angles do not exceed 90 degrees). This implementation includes energy minimization functions and iterative curve evolution techniques.