Program for Reading Images and Performing Edge Detection

Image edge detection represents one of the fundamental tasks in computer vision, designed to identify regions of significant intensity changes that typically correspond to object boundaries in images. A standard edge detection pipeline generally involves the following key computational stages: First, during the image preprocessing phase, Gaussian filtering is applied to the original image for smoothing and noise reduction. In code implementation, this involves convolving the image with a Gaussian kernel using functions like cv2.GaussianBlur() in OpenCV or imgaussfilt() in MATLAB. This convolution operation effectively eliminates high-frequency noise while preserving essential edge information, preventing noise interference during subsequent gradient calculations. Next, gradient computation using operators such as Sobel or Canny calculates the magnitude and direction of gradients for each pixel in the image. Programmatically, this is achieved through kernels that approximate horizontal and vertical derivatives. The gradient magnitude reflects the intensity of pixel changes, while the direction indicates edge orientation, serving as crucial information for precise edge localization. During the non-maximum suppression stage, the algorithm compares adjacent pixels along the gradient direction, retaining only pixels with local maximum gradient values. This computational step effectively thins edge lines and eliminates redundant responses in edge detection, resulting in clearer, single-pixel-wide edges. Implementation typically involves comparing each pixel with its neighbors in the gradient direction using interpolation techniques. Finally, double threshold detection distinguishes strong edges, weak edges, and background by setting high and low threshold values. In programming terms, strong edges are directly preserved, while weak edges are included in the final result only when connected to strong edges, thereby ensuring edge continuity while suppressing noise. This hysteresis thresholding is implemented through connected component analysis. By comparing output images from different stages, one can visually observe the impact of each step on edge detection results. For instance, Gaussian-filtered images appear smoother, gradient maps show preliminary edge responses, non-maximum suppression produces thinner edges, and double thresholding further optimizes edge completeness and accuracy. Such stage-by-stage comparison provides valuable insights for understanding edge detection principles and parameter tuning in practical implementations.