International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 01 | Jan-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 912 Hardware Co-Simulation of Classical Edge Detection Algorithms Using Xilinx System Generator Avinash G. Mahalle 1 , A. M. Shah 2 1 M.Tech, Dept. of Electronics Engineering, GCOEA, Maharashtra, India 2 Assistant Professor, Dept. of Electronics Engineering, GCOEA, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Edge Detection is one of the most important and fundamental processes in the field of Image Processing and Computer Vision. It is a process of localizing pixel intensity changes. Classical edge detection methods such as Robert, Prewitt and Sobel are simple to design than Laplacian based methods. Hence, these are used in Real Time image processing applications quiet more often. The proposed designs for Classical Operators utilize minimum resources. At the same time, it also enhances maximum frequency of operation. Spartan-3E Starter Kit is used for prototyping purpose. JTAG Hardware Co-Simulation utilizes hardware in loop approach. An efficient way to implement image processing tools on reconfigurable hardware is to design algorithms using Xilinx System Generator. Key Words: Image Processing; Edge Detection; Xilinx System Generator; JTAG Hardware Co-simulation; Spartan-3E FPGA 1. INTRODUCTION Digital Image consists of various pixels. Very few of these pixels actually carry information. By detecting edges in an image, one can preserve useful structural information and eliminate redundant data [1]. It is an efficient way for storage and bandwidth utilization. Edges can be characterized by sudden change in the pixel intensity. Thus, edges are components with high spatial frequencies. Any change can be located by mathematical tool named as derivative. In discrete domain, derivative is nothing but difference equations. By convolving image pixels with given masks this change can be located. Finding gradient implies taking derivative for one time whereas, for finding Laplacian derivative is taken two times. Edge profile can be categorized as step, ramp, ridge and roof. Gradient based methods find out maximum/minimum values whereas, Laplacian based methods find out zero crossing [2]. Gradient works well when image contains sharp intensity and low noise. Hardware implementation of these edge detection algorithms is essential in order to use it in real time [3]. Field Programmable Gate Array (FPGA) has advantages over Application Specific Integrated Circuit (ASIC) with no non- recurring expense (NRE), less time to market and high flexibility. A low cost Spartan-3E Starter Kit is used as a hardware platform for implementation. In hardware co- simulation approach normal Simulink blocks are executed in MATLAB environment that generates desired operation while JTAG simulation block loads bit stream file generated (*.bit) in FPGA using Hardware in loop [4]. Writing code in Hardware Description Languages (HDL) along with its test bench programming is very tedious job. Xilinx System Generator provides an efficient way to program FPGA by designing algorithm using blocks [5]. Using various compilation, HDL code and required Test bench are generated automatically along with various parameters such as minimum period, maximum frequency, power dissipation and resource utilization [6]. 2. EDGE DETECTION ALGORITHMS Edge Detection algorithms are broadly classified as follow: -First Order Derivative Methods [Gradient Based]  Robert Operator  Prewitt Operator Classical Operators  Sobel Operator -Second Order Derivative Methods [Laplacian Based]  Marr-Hildreth Edge Detector [LoG] -Optimal Edge Detectors: Canny Algorithm Classical operators include Robert, Prewitt and Sobel operators. Main advantage of these operators is that they are simple to design and have very low latency. These are also less susceptible to noise than Laplacian based methods. Gradient can be find out by convolving operator mask with image pixels. Masks are of different dimensions. Fig -1: Horizontal & Vertical Masks for Classical Operators Robert operator is having [2×2] mask whereas, Prewitt and Sobel have [3×3] mask. The Fig- 1 shows both horizontal and vertical masks for all classical operators.