A Review: Sobel Canny Hybrid Theoretical Approach & LOG Edge Detection Techniques for Digital Image

Edge detection is an important field in image processing. The purpose of image’s edge Detection is image segmentation, data compression, well matching such as image reconstruction and so on. Images to be compressed are first differentiated and edge information is extracted by investigation of the histogram of small non-overlapping blocks of the differential image. The image is classified into visually active and visually continuous blocks which are then coded individually. There are different methods for achieve the edge detection such as first-order derivatives in an image are computed using the gradient, the second-order derivatives are obtained using the Laplacian and one method for edge detection uses Hilbert Transform. Edge detection refers to the process of locating sharp discontinuities in an image. These discontinuities originate from different scene features such as discontinuities in depth, discontinuities in surface orientation, and changes in material properties and variations in scene illumination. Edge detector is a fundamental tool in image processing, machine vision and computer vision. An effective edge detector reduces a large amount of data but still keeps most of the important feature of the image.