Line Detection Based on Hough Transform

This article introduces line, circle, and ellipse detection based on Hough Transform methodology. The line detection algorithm is implemented using VC++ code, while circle and ellipse detection utilize MATLAB code implementations. Hough Transform represents a fundamental image processing technique that effectively identifies various shapes within digital images. Lines, circles, and ellipses constitute common geometric shapes with extensive applications in image processing and computer vision systems. The article provides detailed explanations of Hough Transform principles and practical applications, accompanied by specific code implementations. The VC++ implementation for line detection employs accumulator arrays to transform image points from Cartesian coordinates to Hough parameter space, while the MATLAB implementations for circle and ellipse detection utilize optimized voting mechanisms for parameter identification. Through studying this material, readers will gain comprehensive understanding of shape detection methods based on Hough Transform and develop proficiency in applying these techniques to real-world image analysis scenarios. Key implementation aspects include parameter space discretization for line detection using theta-rho representations, circle detection through gradient information and center accumulation, and ellipse detection employing constrained voting schemes with five-dimensional parameter spaces. Each implementation includes optimization techniques for computational efficiency and accuracy enhancement in shape recognition tasks.