Edge detection in machine vision refers to the technique of identifying boundaries within images by detecting discontinuities in pixel intensity. An edge detection algorithm is used to locate these transitions, which typically correspond to object boundaries, texture changes, or abrupt shifts in the scene. One of the most commonly used edge detection algorithms is the Canny edge detector. It works by first applying a Gaussian filter to smooth the image, then calculating gradients to detect changes in intensity, followed by non-maximum suppression to refine the edges, and finally, edge tracing by hysteresis. Other algorithms include the Sobel operator, which highlights edges in a specific direction, and the Prewitt operator, which works similarly but uses different convolution masks. These edge detection techniques are widely used in various applications, such as identifying objects in images, aiding in image segmentation, or processing images for object tracking. Edge detection plays a critical role in simplifying image analysis by focusing only on the most important features—edges—making it easier to process the image further in computer vision tasks.
What is a machine vision edge detection algorithm?
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