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Edge detection is a fundamental tool in image processing, machine vision and computer vision, particularly in the areas of feature detection and feature extraction. [ 1 ]
It has been widely applied in various computer vision systems. Canny has found that the requirements for the application of edge detection on diverse vision systems are relatively similar. Thus, an edge detection solution to address these requirements can be implemented in a wide range of situations. The general criteria for edge detection include:
In computer vision, the Marr–Hildreth algorithm is a method of detecting edges in digital images, that is, continuous curves where there are strong and rapid variations in image brightness. [1] The Marr–Hildreth edge detection method is simple and operates by convolving the image with the Laplacian of the Gaussian function , or, as a fast ...
Deriche edge detector is an edge detection operator developed by Rachid Deriche in 1987. It is a multistep algorithm used to obtain an optimal result of edge detection in a discrete two-dimensional image. This algorithm is based on John F. Canny's work related to the edge detection (Canny's edge detector) and his criteria for optimal edge ...
The Harris corner detector is a corner detection operator that is commonly used in computer vision algorithms to extract corners and infer features of an image. It was first introduced by Chris Harris and Mike Stephens in 1988 upon the improvement of Moravec's corner detector. [1]
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These detectors include edge-based region (EBR) and scale-invariant shape features (SISF) From the detection invariance point of view, feature detectors can be divided into fixed scale detectors such as normal Harris corner detector, scale invariant detectors such as SIFT and affine invariant detectors such as Hessian-affine.
The Roberts cross operator is used in image processing and computer vision for edge detection.It was one of the first edge detectors and was initially proposed by Lawrence Roberts in 1963. [1]