Circle Detection in Images Using Hough Transform

In this process, the image first undergoes edge detection to enhance contour extraction using operators like Sobel or Canny edge detection algorithms. This initial step creates a binary edge map that highlights significant transitions in pixel intensity. Subsequently, we implement the Hough Transform algorithm - specifically the Circular Hough Transform (CHT) variant - which mathematically maps edge points to parameter space (accumulator array) representing potential circle centers and radii. The implementation typically involves gradient-based approaches using edge direction information or probabilistic Hough methods for computational efficiency. Key functions include accumulating votes in Hough space, applying thresholding to identify prominent circles, and employing peak detection algorithms. By integrating these techniques with parameter optimization (minimum/maximum radius constraints, sensitivity thresholds), we achieve robust circle detection and enable precise geometric analysis of detected circular features including their positions, sizes, and spatial relationships.