Abstract-This paper presents an efficient hardware architecture of Prewitt edge detection for high speed image processing applications. The hardware design is implemented by using Verilog hardware description language, whereas the software part is developed by using Matlab. The zero computational error analysis indicates that the proposed architecture produces similar outputs with ideal result obtained by Matlab software simulation. The architecture is capable of operating at a clock frequency of 145 MHz at 550 frames per second (fps), which implies that the system is suitable for both image processing and computer vision applications.
I. INTRODUCTION EDGE detection technique has been used in variety of image processing applications include extracting the edges from image and typically is utilized before object segmentation and feature extraction [1]. Edges are recognized by abrupt intensity variations in an image. The first gradient is typically calculated to locate the edges. The 3 x 3 masks are used to extract the edges from image, and convolved them through it. Prewitt, Canny and Sobel algorithms have been commonly used for edge detection purposes with different masks. Perwitt edge detection technique is selected due to efficiency and simplicity in the single mask. Figure 1 shows typical Perwitt edge detection masks. In this technique, the edges are detected by convolving horizontal and vertical masks Gx and Gy respectively, through the image. The masks are orthogonal to each other and use to measure the difference among the adjacent pixels gray level in horizontal and vertical direction.
Abstract-This paper presents an efficient hardware architecture of Prewitt edge detection for high speed image processing applications. The hardware design is implemented by using Verilog hardware description language, whereas the software part is developed by using Matlab. The zero computational error analysis indicates that the proposed architecture produces similar outputs with ideal result obtained by Matlab software simulation. The architecture is capable of operating at a clock frequency of 145 MHz at 550 frames per second (fps), which implies that the system is suitable for both image processing and computer vision applications.
I. INTRODUCTION EDGE detection technique has been used in variety of image processing applications include extracting the edges from image and typically is utilized before object segmentation and feature extraction [1]. Edges are recognized by abrupt intensity variations in an image. The first gradient is typically calculated to locate the edges. The 3 x 3 masks are used to extract the edges from image, and convolved them through it. Prewitt, Canny and Sobel algorithms have been commonly used for edge detection purposes with different masks. Perwitt edge detection technique is selected due to efficiency and simplicity in the single mask. Figure 1 shows typical Perwitt edge detection masks. In this technique, the edges are detected by convolving horizontal and vertical masks Gx and Gy respectively, through the image. The masks are orthogonal to each other and use to measure the difference among the adjacent pixels gray level in horizontal and vertical direction.
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