World Journal of Engineering Research and Technology

Gurpreet Kaur* and Er. Manreet Kaur

ABSTRACT

Edge detection is one of the most important tasks in digital image processing. Edges characterize the boundaries i.e. an edge is a boundary between an object and background. Edges in the images are the area with strong intensity contrasts- a jump in intensity from one pixel to the next. Edge detection significantly reduces the amount of data and filters out useful information, while preserves the important structural properties in an image. Therefore edge Detection refers to the process of identifying and locating sharp discontinuities in an image. The discontinuities are abrupt changes in pixel intensity which characterize boundaries of objects in a scene. The shape of the edges in images depends on many parameters: the geometrical and optical properties of the object, the illumination conditions and the noise level in the images. The goal of edge detection in a digital image is to determine the frontiers of all represented objects, based on automatic processing of the gray level information in each present pixel. Classical methods of edge detection involve convolving the image with an operator (a 2-D filter), which is constructed to be sensitive to large gradients in the image while returning values of zero in uniform regions. There is an extremely large number of edge detection operators available, each designed to be sensitive to certain types of edges. The goal of our research work is to study various edge detection techniques, compare those techniques and find the efficient method which has better results in the output image.

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