Ellipse Detection Implementation

Ellipse detection is a fundamental task in computer vision and image processing, used to identify elliptical shapes within images. MATLAB provides powerful tools and function libraries to implement this functionality efficiently.

The ellipse detection process typically involves several key stages. Edge detection serves as the foundation, where methods like Canny edge detection are commonly employed to effectively extract edge information from images. In MATLAB, this can be implemented using the edge() function with the 'canny' operator, which applies gradient-based thresholding to identify strong and weak edges.

Following edge detection, ellipse fitting from edge points is required. MATLAB's Hough transform approach can detect elliptical shapes through a parameter space voting mechanism that identifies potential ellipse parameters. The implementation involves using functions like hough() and houghpeaks() to accumulate votes in a five-dimensional parameter space (center coordinates, major/minor axes, and orientation).

An alternative approach involves least-squares ellipse fitting, which uses mathematical optimization to find the ellipse equation that best fits given edge points. MATLAB's Optimization Toolbox facilitates this process through functions like lsqnonlin() for nonlinear least-squares optimization, minimizing the algebraic distance between points and the ellipse model.

To enhance detection accuracy, preprocessing steps such as image denoising and morphological operations can be implemented using functions like medfilt2() for median filtering and imopen() for morphological opening. These operations reduce noise impact on ellipse fitting. Additionally, post-processing techniques like non-maximum suppression, implemented through custom algorithms comparing overlapping detections, help eliminate duplicate ellipse detections.

When implementing ellipse detection in MATLAB, parameters can be adjusted according to specific requirements. This includes modifying edge detection thresholds using the threshold argument in edge() or adjusting ellipse fitting tolerances through optimset() options, ensuring optimal results for different application scenarios.