Explore MATLAB source code curated for "hough变换" with clean implementations, documentation, and examples.
This MATLAB program detects circles in binary images using the standard Hough transform method with user-specified circle radius parameters, implementing a robust circle detection algorithm for computer vision applications.
This article demonstrates effective iris localization in MATLAB using Hough transform, providing comprehensive code-related descriptions and implementation details for educational purposes.
Algorithm for detecting parabolas using Hough Transform with implementation details. Demonstrates how to apply standard Hough Transform to detect parabolic curves from binary (black and white) images, including parameter space mapping and mathematical foundations.
Experimental program for HOUGH transform in image processing, implemented using MATLAB with visual image support and code demonstration
MATLAB source code for implementing the Hough transform algorithm for geometric shape detection in digital images.
Implementation of circle detection through Hough Transform methodology. This function employs Standard Hough Transform to identify circular patterns in binary images. Based on the Hough Transform principle for circles, each pixel in image space corresponds to a circular surface in Hough parameter space and vice versa. The upper-left corner serves as the coordinate system origin. The implementation involves accumulator array management and peak detection algorithms for optimal circle identification.
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.
A self-developed MATLAB implementation for estimating motion blur direction using Hough transform, containing one well-documented M-file with detailed comments. This resource provides practical insights for image restoration learners, featuring algorithm implementation details and key function explanations for motion blur analysis.
Load an image, perform preprocessing, detect edges, binarize the resulting image, apply Hough transform to obtain circular boundaries, and overlay this image with the original to enhance edges.
This program implements the Hough Transform, a fundamental technique in image processing for detecting geometric shapes. The algorithm utilizes point-line duality to convert curves from the image space into points in the parameter space, transforming global shape detection into local peak identification. The implementation includes edge detection preprocessing, parameter space accumulation, and peak detection algorithms to identify lines, circles, ellipses, and arcs with configurable parameters for accuracy and sensitivity control.