Hough Transform-Based Ellipse Detection Program

The Hough transform represents a robust methodology for identifying geometric shapes within digital images, with particular effectiveness in ellipse detection applications. MATLAB provides comprehensive built-in functions and toolboxes for implementing Hough transform operations, specifically through functions like hough, houghpeaks, and houghlines for basic shape detection, while ellipse detection typically requires customized implementation. For optimal ellipse detection, image preprocessing constitutes a critical initial phase. This involves utilizing MATLAB's image processing functions such as imfilter for noise reduction, edge for boundary enhancement using operators like Canny or Sobel, and imbinarize for segmentation. The preprocessing stage aims to amplify elliptical features while suppressing irrelevant image noise. The core implementation employs a parameter space transformation where each edge point in the image votes for potential ellipse parameters (center coordinates, major/minor axes lengths, and orientation). In MATLAB, this can be implemented through accumulator arrays using multidimensional matrices, with functions like accumarray for efficient voting management. The algorithm typically involves: 1. Edge point extraction using edge detection algorithms 2. Parameter space initialization with defined ranges for ellipse properties 3. Voting mechanism implementation through nested loops or vectorized operations 4. Peak detection in parameter space using find or custom thresholding functions 5. Ellipse validation and parameter extraction Post-detection analysis enables further image manipulation through functions like viscircles for visualization, regionprops for geometric property extraction, or imellipse for interactive ellipse handling. The Hough transform methodology proves particularly valuable in computer vision applications including industrial inspection, medical imaging analysis, and autonomous systems where elliptical object detection is required. Key MATLAB functions for enhancement include: - imfindcircles (for circular detection, extensible to ellipses) - regionprops with 'Ellipse' properties - Custom implementations using parametric equations and voting schemes This approach demonstrates significant robustness against partial occlusions and noise interference, making it suitable for practical computer vision systems.