Circle Detection in Images with Center Coordinate Calculation

In the field of image processing, detecting geometric shapes in images is a fundamental task, particularly for identifying circles and lines. This is commonly achieved through techniques like Hough Transform.

### Basic Approach to Circle Detection Preprocessing: Begin by converting the image to grayscale and performing edge detection using algorithms like Canny edge detector, which enhances edge information in the image. Hough Circle Transform: Apply Hough Circle Transform on the edge detection results to detect potential circular contours. This algorithm employs a voting mechanism in parameter space to identify optimal circle centers and radii. Center Coordinate Calculation: Once circles are detected, their center coordinates can be directly obtained from the parameters returned by Hough transform, typically represented as (x, y) pixel coordinates.

### Additional Note on Line Detection Similarly, Hough Transform can be applied for line detection. Through Hough Line Transform, linear segments in images can be extracted, and their slopes and intercepts calculated.

### Practical Applications Industrial Inspection: Verifying the accuracy of holes in mechanical components. Medical Imaging: Analyzing circular structures like cells or blood vessels. Autonomous Driving: Identifying road markings and traffic signs.

The key to this method lies in adjusting Hough transform parameters (such as minimum radius, maximum radius, accumulator threshold) to suit detection requirements across different scenarios.