【文章內(nèi)容簡介】 al and applying standard signalprocessing techniques to it. The process involves the enhancement or manipulation of an image which resulted in another image, removal of redundant data and the transformation of a 2D pixel array into a statically uncorrelated data set (Priotr, 2021). Since images contain lots of redundant data, scholars have discovered that the most important information lies in it edges (Canny, 1986). Edges being the local property of a pixel and its immediate neighbourhood, characterizes boundary (ChaugHuang, 2021). They correspond to object boundaries, changes in surface orientation and describe defeat by a small margin. Edges typically correspond to points in the image where the gray value changes significantly from one pixel to the next. Edges represents region in the image with strong intensity contrast。 representing an image by its edges has the fundamental advantage that the amount of data is reduced significantly while retaining most of image’s vital information with high frequencies (Keren, Osadchy, amp。 Gotsman, 2021). Thus, detecting Edges help in extracting useful information characteristics of the image where there are abrupt changes (Folorunso et al., 2021). Edge detection is a process of locating an edge of an image. Detection of edges in an image is a very important step towards understanding image features. Edges consist of meaningful features and contained significant information. It’s reduce significantly the amount of the image size and filters out information that may be regarded as less relevant, preserving the important structural properties of an image (Yuval, 1996). Most images contain some amount of redundancies that can sometimes be removed when edges are detected and replaced, when it is reconstructed (Osuna et al., 1997). Eliminating the redundancy could be done through edge detection. When image edges are detected, every kind of redundancy present in the image is removed (Sparr, 2021). The purpose of detecting sharp changes in image brightness is to capture important events. Applying an edge detector to an image may significantly reduce the amount of data to be processed and may therefore filter out information that may be regarded as less relevant, while preserving the important structural properties of an image. The image quality reflects significant information in the output edge and the size of the image is reduced. This in turn explains further that edge detection is one of the ways of solving the problem of high volume of space images occupy in the puter memory. The problems of storage, transmission over the Inter and bandwidth could be solved when edges are detected (Vincent, 2021). Since edges often occur at image locations representing object boundaries, edge detection is extensively used in image segmentation when images are divided into areas corresponding to different objects. Related Methods Different methods are used to detect edges in image processing among these is Roberts Cross Algorithms. Robert process a photograph into a line drawing, transform the line drawing into a threedimensional representation and finally display the threedimensional structure with all the hidden lines removed, from any point of view (Robert, 1965). The Roberts cross algorithm (Marioamp。 Maltoni, 1997) performs a 2D spatial gradient convolution on the image. The main idea is to bring out the horizontal and vertical edges individually and then to put them together for the resulting edge detection. The two filters highlight areas of high special frequency, which tend to define the edge of an object in an image. The two filters are designed with the intention of bringing out the diagonal edges within the image. The Gx image will enunciate diagonals that run from thee topleft to the bottomright where as the Gy image will bring out edges that run topright to bottomleft. The two individual imagesGx andGy are bined using the approximation equation GyGxG ?? The Canny edge detection operator was developed by John F. Canny in 1986 and uses a multistage algorithm to detect a wide range of edges in images. In addition, canny edge detector is a plex optimal edge detector which takes significantly longer time in result putations. The image is firstly run through a Gaussian blur to get rid of the noise. When the algorithm is applied, the angle and magnitude is obtained which is used to determine portions of the edges to retain. There are two threshold cutoff points where any value in the image below the first threshold is dropped to zero and values above the second threshold is raised to one. Canny (1986) considered the mathematical problem of deriving an optimal smoothing filter given the criteria of detection, localization and minimizing multiple responses to a single edge. He showed that the optimal filter given these assumptions is a sum of four exponential terms. He also showed that this filter can be well approximated by firstorder derivatives of Gaussians. Canny also introduced the notion of nonmaximum suppression, which means that given the presmoothing filters, edge points are defined as points where the gradient magnitude assumes a local maximum in the gradient direction. Another algorithm used is the Susan edge detector. This edge detection algorithm follows the usual method of taking an image and using a predetermined window centered on each pixel in the image applying a locally acting set of rules to give an edge response (Vincent, 2021). The response is then processed to give the output as a set of edges. The Susan edge filter has been implemented using circular masks (kernel) to give isotopic responses with approximations used either with constant weighting within it or with Gaussian weighting. The usual radius is pixels, giving a mask of 37 pixels, and the smallest mask considered is the traditiona