BibTeX:

@article{IJIRSTV2I10052,
     title={To Implement Convolution in Image Processing},
     author={Prof. N. Mishra, Ms. Mayuri Telrandhe, Ms. Priyanka Padole and Ms. Payal Thakare},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={2},
     number={10},
     pages={328--331},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV2I10052.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Edge detection is one of the most frequently used techniques in digital image processing. An Edge in an image is a significant local change in the image intensity, usually associated with a discontinuity in either the image intensity. Edges detection is a problem of fundamental importance in object extraction as it reduces image data and detects the object which is required [8] [9]. Edges identify object boundaries and are detected through changes in gray level above a particular threshold. Operators that are sensitive to the change in gray levels can be used as edge detectors. The edge detection is mainly applicable in case of data transmission; in that case the detected edge data reduce the amount of data to be transmitted. Sobel operator is one of the most commonly used detection methods and returns edges at points where the gradient of image intensity is maximum. Locations whose gradient value exceeds or greater some threshold are declared edge locations [5]. Prewitt method finds edges using the Prewitt approximation to the derivative and returns edges at those points where gradient of image intensity is maximum or greater. Prewitt operator does not place any emphasis on pixels that are closer to the centre of the masks. The Canny edge detector is considered as the standard methodology of edge detection and it finds edges by looking for local maxima of the gradient of Image [7]. But the advantage of sobel edge detector is that it requires less time and it can be use in most applications where detailing is not required.

Digital image processing, Guassian filter, Sobel edge detector, Kernel matrics