Edges can be defined as the rigid significant change of image intensity pixels usually appears at the boundary between different regions, edges can modeled according to the image intensity profiles and amplitude changes, such as; Step, Ramp, Ridge /Line, and Roof Edges. Edge detection plays an efficient role in digital image processing and practical aspects of various life fields. Image edge detection frequently minimizes the amount of data and gets rid of worthless information and preserves the essential image characteristics. Edge detection techniques can be grouped into two main categories, Gradient and Laplacian edge detection techniques. Gaussian Mixture Model (GMM) lately applied for edge detection purposes. GMM considered as an unsupervised classifier that required a probability density functions (pdf) of the given data to be calculated at the training step. In the related works, we considered researches that deal with Gaussian model only since it is our concern in this work to focus on its main characteristics and properties effects. In this paper, we discussed and analyzed various concepts related to edges, various edge detection techniques, and ultimately introduced a comparison between these techniques.
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Digital image processing, Edge detection, Gradient methods, Laplacian methods, Gaussian Mixture Model.