The goal of edge detection is to mark the points in a digital image at which the luminous intensity changes sharply. Sharp changes in image properties usually reflect important events and changes in properties of the world. These include (i) discontinuities in depth, (ii) discontinuities in surface orientation, (iii) changes in material properties and (iv) variations in scene illumination. Edge detection is a research field within image processing and computer vision, in particular within the area of feature extraction.
Edge detection of an image reduces significantly the amount of data and filters out information that may be regarded as less relevant, preserving the important structural properties of an image. There are many methods for edge detection, but most of them can be grouped into two categories, search-based and zero-crossing based. The search-based methods detect edges by looking for maxima and minima in the first derivative of the image, usually local directional maxima of the gradient magnitude. The zero-crossing based methods search for zero crossings in the second derivative of the image in order to find edges, usually the zero-crossings of the Laplacian or the zero-crossings of a non-linear differential expression.

A New Method of Edge Detection - A new method for edge detection based on LMC.A developer try to: 1) create a good but simple way to let the users express their idea about the edges they have in mind regarding a specific image; and to 2) implement a method to detect the type of edges a user ordered.
Edge Detector Comparison - The comparison of edge detection algorithms, five edge detectors were evaluated. A masters thesis detailing the description of this comparison is available. This work was published in Patten Analysis and Machine intelligence.,1997.
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