Understanding and developing any vision related model requires feature extraction from the image for analysis. Edges and Corners are the features that are trivially identified by our human eye. Thus extracting edges and images is of prime importance in computer vision.
The Canny Edge Detector is an edge detection technique that uses a multi-stage algorithm flow. It was developed by John F. Canny in 1986. It extracts useful structural information from different vision objects and dramatically reduce the amount of data to be processed. It has been widely applied in various computer vision and imaging applications. Canny Edge detector works on the principle that intensity changes suddenly across the edge, however remains uniform along the edge.
Harris Corner Detector is a corner detection operator that is commonly used tool in computer vision algorithms to extract corners and infer features of an image. A corner is a point whose local neighbourhood stands in two dominant and different edge directions. In other words, a corner is point of intersection of edges. Thus, while an edge has a gradient change in any one direction, a corner has a gradients change along multiple directions. The gradient of the corner (in both directions) have a high variation, which can be used to detect it. This is the principle used in Harris corner detector. Also, it is popular because it is rotation, scale and illumination invariant. It was first introduced by Chris Harris and Mike Stephens in 1988.
