Explore MATLAB source code curated for "二值图像" with clean implementations, documentation, and examples.
Refining the edges of binary images acquired through shape edge detection to extract single-pixel edges, resulting in clear and distinct single-pixel boundaries. This process typically involves iterative thinning algorithms such as Zhang-Suen or morphological operations to reduce edge width while preserving connectivity.
This MATLAB code implements standard Hough Transform for detecting lines in binary images, demonstrating the working mechanism of this fundamental computer vision technique. The implementation uses polar coordinate representation and accumulator arrays to identify linear patterns.
Implement bacterial image segmentation in MATLAB to obtain binary images and perform automated bacterial counting through image processing techniques.
This MATLAB-based simulation for object tracking has been personally validated and verified as functional. The implementation enables target marking with rectangular bounding boxes, converts grayscale images to binary images, and achieves robust object tracking through practical algorithm application.
This corner detection program can identify all convex and concave points in images and display them on binary images with excellent results. Simply run corner.m directly - the implementation utilizes feature detection algorithms to locate key points where image intensity changes significantly in multiple directions.
Compute the minimum rectangular bounding box containing target regions in a binary image, extract the top, bottom, left, and right boundaries, and visualize the target area by drawing the rectangle based on boundary coordinates using image processing techniques.
Implementing Hough transform on binary images to detect circles by identifying center coordinates and radii, followed by circle visualization using programming techniques
Image segmentation using Support Vector Machine (SVM) combined with OTSU thresholding method for precise binary image generation and optimized separation of target regions from background.