International Journal on Cybernetics & Informatics (IJCI) Vol. 5, No. 4, August 2016 DOI: 10.5121/ijci.2016.5439 363 ENHANCED OPTIMIZATION OF EDGE DETECTION FOR HIGH RESOLUTION IMAGES USING VERILOG HARDWARE DESCRIPTION LANGUAGE WITH LOW POWER CONSUMPTION AND LESS HARDWARE TECHNOLOGY T.Manikyala Rao 1 , Praveen Chakravarthy BH 2 , R. Sushma 3 , G.Parameswara Rao 4 Assistant Professor, Electronics and Communication Engineering, SSCE 1234 ABSTRACT: Edge Detection plays a crucial role in Image Processing and Segmentation where a set of algorithms aims to identify various portions of a digital image at which a sharpened image is observed in the output or more formally has discontinuities. The contour of Edge Detection also helps in Object Detection and Recognition. Image edges can be detected by using two attributes such as Gradient and Laplacian. In our Paper, we proposed a system which utilizes Canny and Sobel Operators for Edge Detection which is a Gradient First order derivative function for edge detection by using Verilog Hardware Description Language and in turn compared with the results of the previous paper in Matlab. The process of edge detection in Verilog significantly reduces the processing time and filters out unneeded information, while preserving the important structural properties of an image. This edge detection can be used to detect vehicles in Traffic Jam, Medical imaging system for analysing MRI, x-rays by using Xilinx ISE Design Suite 14.2. KEYWORDS: Sobel, Canny, Verilog, ISE Design Suite 14.2 1. INTRODUCTION: Images are the Pictorial representation of an Object or a Person in a two dimensional format. If the labels of the co-ordinate functions (X, Y) represent the Intensity level or Gray level then the function “f”, it represents the amplitude of the function of an image. And when those co-ordinate functions represent discrete values then that image can be represented as a Digital Image. That Digital image can be easily processed by many computer algorithms as a sub-category field in digital signal processing. (a.) (b.) Fig1 (a.) Represents the Image of a boy (b.) Represents Gray-Square of a Image