外文文献及翻译-fpga实现实时适应图像阈值(编辑修改稿)

外文文献及翻译-fpga实现实时适应图像阈值(编辑修改稿)内容摘要：

processing and related information are vital. Applications employing realtime thresholding include robotics, automobiles, object tracking, and laser range finding. In laser range finding 5 where the range of an object in motion is determined, the captured image is binarized. The thresholding technique is applied to separate the laser spot from the background and to locate the spot centroid. This application is the scenario of interest in the rest of this paper. Another application of real time thresholding is document processing and Optical Character Recognition (OCR). For example a highspeed scanner can scan and process over one hundred pages per minute. The speed requirement in this system imposes a dedicated hardware for image processing and binarization. Typically image captured from scanners by CMOS or CCD camera are converted to binary images. A document consists of text on a relatively uniform background. Therefore converting it to a binary image is suitable for output and storage because it significantly reduces size without loss of important data. All of the mentioned applications have one thing in mon. The high performance and high precision systems dictate an efficient and fast algorithm for thresholding. They also use the image binarization as preprocessing step prior to further processing. Therefore they have to be able to separate the objects from background by calculating an optimum threshold value to avoid losing important information (such as object dimensions and shape). This paper presents new technique for image thresholding in realtime applications. The thresholding technique is implemented in an FPGA. Section 2 provides an overview of image binarization. Wellknown image thresholding techniques and their performance {evaluation} are discussed. Section 3 describes the proposed algorithm for thresholding techniques. The performance of the proposed algorithm in parison with other methods is discussed. Section 4 presents FPGA implementation of the proposed algorithm. Experimental results for implemented hardware concentrating on the functional performance are discussed. The hardware performance in terms of speed and area are pointed out. Section 5 draws some key conclusion remarks from the work presented in this paper. The results of the research are summarized and pros and cons are highlighted. 2 PROBLEM STATEMENT The objective of image binarization is to divide an image into two groups, foreground or object, and background. Inimage processing applications, the gray level values assigned to an object are different from the gray level values of thebackground. Therefore thresholding can be considered as an effective way to separate foreground and background. Theoutput of a thresholding process is a binary image which is obtained by assigning pixels with values less than thethreshold with zeros and the remaining pixels with ones. Let us consider image f of size MxN (M rows and N columns) with L gray levels in the range [0, L1]. The gray level or the brightness of a pixel with coordinates (i,j) is denoted by f(i,j). The threshold, T, is a value in the range of [0, L1].{Now,} the thresholding technique determines an optimum value for T based。