Explore MATLAB source code curated for "直线" with clean implementations, documentation, and examples.
The Hough line detection algorithm provides a straightforward and efficient approach for identifying straight lines in static images, featuring optimized parameter configuration and practical implementation techniques.
The standard Hough algorithm struggles to detect all lines in datasets with thick sample points and often identifies false positives. This implementation allows customizable precision calibration. This MATLAB function accepts a grayscale image matrix as input and returns the slope of the longest detected line, primarily designed for image tilt correction with practical code enhancements.
Processing images by directly drawing lines, rectangles, polygons, and writing text on RGB images with code implementation details.
Utilizing Radon transform for line detection and inclination angle calculation, followed by image rotation based on the computed angle
Least squares fitting method, linear regression algorithm using least squares, MATLAB implementation of linear least squares fitting with code examples and mathematical foundations.
Utilizing beamlet's superior straight-line detection capabilities for bridge identification in images achieves 80% accuracy through multi-scale line segment analysis and geometric feature extraction.
This algorithm primarily employs Radon transformation to achieve automated extraction of regular curves such as straight lines, ellipses, and parabolas, with detailed code implementation approaches for curve detection and parameter estimation.
A line detection algorithm utilizing Hough transform that leverages point-line duality between image space and Hough parameter space. The implementation transforms detection problems from image space to parameter space, where simple accumulation statistics are performed. By identifying accumulator peaks in Hough parameter space, the program effectively detects lines. The core algorithm involves mapping edge points to sinusoidal curves in parameter space and detecting intersections through voting mechanisms.
This program implements image processing techniques to detect lines in images, marking detected lines in green with '+' symbols indicating endpoints, while highlighting the longest line using a distinct color for better visual differentiation.
The Hough Transform method can be used to extract lines in the transform domain by leveraging collinearity and line intersection relationships, converting line extraction problems into counting problems through parameter space accumulation.