edgedetection

Edge detection is a fundamental operation in image processing and computer vision that aims to locate points in an image where brightness changes sharply. These points often lie on object boundaries or texture transitions and form the basis for higher-level analysis.

Classic detectors estimate image gradients to identify edges. Finite-difference operators such as Sobel, Prewitt, and Scharr compute horizontal and vertical gradients (Gx, Gy). The edge strength is typically the gradient magnitude sqrt(Gx^2 + Gy^2), and the local orientation is arctan(Gy/Gx).

To produce clean, thin edges, many methods apply non-maximum suppression and thresholding. A prominent example is the Canny edge detector, which first smooths the image with a Gaussian, computes the gradient, thins edges by suppression, and uses hysteresis with two thresholds to connect edge segments.

Second-derivative detectors, such as the Laplacian of Gaussian (LoG) and DoG (Difference of Gaussians), locate edges as zero-crossings of the second derivative or through approximations of it. These can be more sensitive to texture and noise.

Edge detection can operate on grayscale or color images; some approaches compute edge strength across color channels or use vector gradients in color spaces to capture chromatic transitions.

Applications include feature extraction for object recognition, image segmentation, contour detection, and medical imaging. Common challenges are sensitivity to noise and blur, dependence on scale, and the need for appropriate pre-processing and threshold selection for reliable results.

Recent developments include learning-based edge detectors that use neural networks to predict edges directly from data. Examples include Holistically-Nested Edge Detection (HED) and other CNN-based models, which can handle complex textures but require annotated training data.