【正文】 ject tracking, and laser range finding. In laser range finding 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. In image processing applications, the gray level values assigned to an object are different from the gray level values of the background. Therefore thresholding can be considered as an effective way to separate foreground and background. The output of a thresholding process is a binary image which is obtained by assigning pixels with values less than the threshold 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 on predefined measurements, so that: ???≤=TjifforTjifforjig),(1),(0),( (1) where g(i,j) is binarized image pixel. In this work we are interested in a light object on a dark background, therefore in the binarized image the pixels below a certain value of gray level are represented by 0, . background, and the pixels above the certain pixel value are represented by 1, . foreground. There are some factors that affect the certain value of gray level (‘threshold’) and plicate the thresholding, such as poor contrast, inconsistency between sizes of object and background, nonuniformity in the background, and correlated noise. Sometimes the binary image loses too much of the region and sometimes gets too many irrelevant background pixels. So the success of the binarization critically depends on the selection of an appropriate threshold. Therefore for finding the optimum threshold an adaptive algorithm is necessary. The adaptive algorithm calculates a threshold value based on the image features such as image statistics and image illumination. Based on a prehensive survey in the literature [1], Thresholding techniques are categorized into six groups as follows: 1. Histogram shapebased methods, where the histogram of the image is viewed as a mixture of two Gaussian distributions associated to the object and background classes, such as convex hull thresholding (Rosenfeld[3]), and peak amp。 valley thresholding (Sezan[4]). 2. Clusteringbased methods, where the graylevel pixels are clustered in two classes as either background and foreground objects, or alternately are modeled as a mixture of two Gaussians, such as iterative thresholding (Riddler[5]), clustering thresholding (Otsu[6]), minimum error thresholding (Kittler[7]), and fuzzy clustering thresholding (Jawaher[8]). 3. Entropybased methods use the difference in entropy between the foreground and background regions, such as entropy thresholding (Kapur[9]), and entropy thresholding (Shanbag [10]). 4. Object attributebased methods, find a measure of similarity (fuzzy shape similarity, edge coincidence, etc.) between the graylevel and the binarized images, such as edge field matching thresholding (Hertz[11]), andtopological stablestate thresholding (Pikaz[12]). 5. Spatial methods, use higherorder probability distribution and/or correlation between pixels, such as higher order entropy thresholding (Abutaleb[13]). 6. Local methods, calculate the threshold value at each pixel based on the local image characteristics, such as local contrast method (White [14]), and surfacefitting threshold (Yaowitz [15]). There are some visual or subjective outlines for performance evaluation, for example: regions should be uniform and homogeneous w