Explore MATLAB source code curated for "边缘" with clean implementations, documentation, and examples.
The Curvelet Toolbox enables ridgelet-based transformations for edge extraction, noise reduction, and enhancement of digital images through optimized multiscale directional analysis algorithms.
Image extraction from background for edge detection and background separation using computer vision algorithms
This program utilizes wavelet transformation for image analysis, applies noise filtering techniques, and ultimately enhances image edge effects through dedicated processing algorithms.
This MATLAB implementation demonstrates a two-stage image processing approach: first applying the Sobel operator for edge detection on binary images, then utilizing Hough transform to identify circular objects by determining their center coordinates and radii. The package includes sample images, detailed code explanations, and visual results illustrating the complete workflow.
Edges represent the most fundamental features in images and serve as the first step in image segmentation. Classical edge detection methods—such as Roberts, Sobel, Prewitt, Kirsch, and Laplace—generally operate by constructing small neighborhood operators to compute first or second-order derivatives, identifying gradient maxima or zero-crossings of the second derivative, and finally applying a suitable threshold to extract boundaries. These gradient-based methods, however, are noise-sensitive and computationally intensive. In contrast, the SUSAN (Smallest Univalue Segment Assimilating Nucleus) algorithm relies solely on grayscale comparisons of surrounding pixels without any gradient calculations, making it highly robust to noise and computationally efficient. SUSAN has been effectively applied in edge detection for various types of images.
This program implements edge-based image registration by utilizing image edges for alignment. It first detects image edges, selects the longest edge in the image, detects corner points along the edge, generates vectors using curvature information at corner points, and performs feature matching to achieve image registration through automated vector matching.
Extract an edge histogram from an image for robust image matching applications, featuring algorithm explanations and code-oriented implementation guidance