Edge Detection in Images Using Otsu's Method and Gradient-Based Algorithms

Otsu's method and gradient-based algorithms are two widely used techniques for image edge extraction, each based on distinct principles to achieve the objective. Otsu's method is a global thresholding segmentation technique primarily used to divide an image into foreground and background regions. Its core principle involves calculating the grayscale histogram of the image to automatically determine an optimal threshold that maximizes the inter-class variance between foreground and background. This method is particularly effective for high-contrast images, efficiently extracting prominent edge contours. In code implementation, Otsu's algorithm typically iterates through all possible threshold values (0-255 for 8-bit images) to compute the variance metric, selecting the threshold that yields maximum separation between classes using functions like `graythresh()` in MATLAB or `cv2.threshold()` with `THRESH_OTSU` flag in OpenCV. Gradient-based algorithms represent local edge detection approaches that identify edges by calculating the gradient (rate of change in pixel intensity) across the image. Common gradient operators include Sobel, Prewitt, and Canny, which capture local variations to precisely locate edges. The strength of gradient methods lies in their ability to detect fine edges and details, though they exhibit higher sensitivity to noise. Implementation typically involves convolution operations with specific kernels - for example, Sobel uses 3x3 derivative kernels for horizontal and vertical gradient computation (`cv2.Sobel()`), while Canny incorporates Gaussian smoothing, gradient calculation, non-maximum suppression, and hysteresis thresholding for refined edge maps. In practical applications, Otsu's method is better suited for global edge extraction in simple scenes, whereas gradient algorithms excel with complex images requiring detailed edge information. Combining both methods can sometimes yield enhanced edge detection results, such as using Otsu's threshold to preprocess images before applying gradient-based edge detection for noise-resistant performance.