Canny Edge Detection Algorithm

In computer vision, Canny edge detection represents a widely adopted image processing technique for identifying edges within digital images. The algorithm comprises multiple computational stages including Gaussian filtering, gradient calculation, and non-maximum suppression. A key advantage of the Canny edge detector lies in its ability to precisely locate image edges while offering adjustable parameters for result optimization. From an implementation perspective, the algorithm typically begins with Gaussian blurring using a 5x5 kernel to reduce noise. Subsequent steps involve calculating gradient magnitude and orientation using Sobel or Prewitt operators. The non-maximum suppression phase then thins edges by preserving only local maxima in the gradient direction. Finally, hysteresis thresholding with dual thresholds (high/low) distinguishes strong and weak edges while eliminating false positives. Beyond computer vision applications, this algorithm finds utility in digital signal processing for feature extraction and machine learning pipelines for preprocessing image data. The method's reliability makes it suitable for real-time systems when optimized with techniques like separable convolution for Gaussian filtering and parallel gradient computation.