MATLAB Code Implementation for Circle Detection Methods

Circle detection is a common task in computer vision and image processing used to identify circular objects or structures within images. The Hough Transform-based circle detection method is widely adopted due to its robustness and accuracy.

The Hough Transform is a classical parameter space voting algorithm that effectively detects geometric shapes in images. For circle detection, the Hough Transform parameterizes the circle equation and utilizes gradient information to vote for circle centers and radii. This process identifies peaks in the parameter space to determine the center coordinates and radius values of circles.

This method optimizes the detection process using geometric features of circles. Specifically, it first performs edge detection on the image to extract edge points. Then, it leverages gradient direction information to narrow down potential circle center ranges, reducing computational complexity. Through an accumulative voting mechanism, it identifies candidate circle centers and filters genuine circles based on voting strength.

In MATLAB, this process can be implemented using built-in Hough Transform functions or through custom code. Key functions include imfindcircles for automated circle detection, edge for Canny edge detection, and hough/houghpeaks for custom Hough implementations. The optimized method enables fast and accurate extraction of circle center coordinates, making it suitable for industrial inspection, medical image analysis, and other application scenarios.