Detecting Circle Radius in Digital Images
Methods for detecting circle radius, center coordinates, and implementation programs in digital images with algorithmic explanations
Edge Detection in Binary Images Using Sobel Operator Followed by Circle Detection via Hough Transform
This MATLAB implementation demonstrates a two-stage image processing approach: first applying the Sobel operator for edge detection on binary images, then utilizing Hough transform to identify circular objects by determining their center coordinates and radii. The package includes sample images, detailed code explanations, and visual results illustrating the complete workflow.
Circle Detection Program Using Hough Transform
The input image must be grayscale. Usage: [accumulator_values, circle_centers, circle_radii] = CircularHough_Grd(gray_image, [min_diameter max_diameter]); This method can detect multiple circles within the same image, implementing gradient-based Hough transform for robust circle detection.
Hough Transform for Circle Detection in Binary Images
Implementing Hough transform on binary images to detect circles by identifying center coordinates and radii, followed by circle visualization using programming techniques
Circle Fitting Program Using Least Squares Method
Graduation Project: Circle Fitting Program Implementing Least Squares Algorithm to Compute Center Coordinates and Radius
Hough Transform: Circle Radius Detection Algorithm
Hough Transform for circle detection with radius identification, capable of determining both unknown circle centers and radii through parameter space analysis