I accurate and reliable navigation of vision-based IV. The main purpose of image segmentation is to separate the

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1 An Improved Otsu Image Segmentation Algorithm for Path Mark Detection under Variable Illumination JIN Li-Sheng TIAN Lei WANG Rong-ben GUO Lie CHU Jiang-wei ( Transportation College of Jilin University, Jilin Changchun , Chna ) Abmuct- Variable illumination is a difficult problem for path mark detection of high-speed vision-based vehicle. It is vitally important to adopt an effective real-time method to solve the problem. In this paper a new grayscale image automatic thresholding algorithm is presented to segment the path mark from the image under different illumination conditions. The new method aims at enhancing its real-time performance which derives from Otsu automatic thresholding algorithm The experimental results show that the new improved algorithm dramaticalty reduces the operating time in image segmentation while ensures the final image segmentation quality. I. INTRODUCTION deal recognition of the navigation path can ensure I accurate and reliable navigation of vision-based IV. The main purpose of image segmentation is to separate the path target from the background in a path image. Tot realize all-day navigation task, the vision-based IV has to overcome the negative impact of variable and uneven lighting condition of the sunshine. Thus the path image acquired from the image prmessing system is quite different under various illuminations. The accuracy and reliability of vision-based IV navigation system is very sensitive to illuminating change. In order to minimize the negative impact of variable illumination and obtain accurate and reliable image segmentation, this paper studies intensively on the automatic thresholding algorithm for image segmentation under different illuminations. Image segmentation plays key role in the following path recognition. Thresholding segmentation algorithm is the most popular algorithm, and is widely used in the image segmentation field. The value of threshold K is de most important for the algorithm. If the value of K is too large, part of the target would be mistakenly classified into the background. Likewise, part of background will be attributed to the target with a smaller K value. The paper studies some popular thresholding algorithms such as Maximum Entropy [1],[5],[6]. Invariable Moment [6], Fuzzy Cluster [4] and Otsu [1],[2],[6]. Otsu algorithm gives a more satisfactory performance in the study. It can well ensure the image segmentation result. However, because of the higher complexity of the algorithm itself, it is too time-consuming to apply for high speed!vehicle navigation. This paper presents an improved algorithm based on the traditional Otsu algorithm for pad image segmentation OTSUALGORITHM In 1979, Japanese scholar Otsu presented a global thresholding algorithm. Suppose that an image compose of target and background, which have different gray-level, and the target js at higher gray level. The gray-level based on the statistical histogram ranges &om 0 to L. Between 0 and L, threshold K is chosen to segment the image into two classes: the background whose gray-level is from 0 to K and the target whose gray-level is kom K+i to L. If a certain threshold K can make the value of interclass variance ob the highest among all the possible 'values. The threshold K is the one with which target and

2 background can be accurately divided. The K is the final threshold we are looking for. The equations involved are as follows: [p(i) = n. N i r=ktl K U, = CP(qi =w(k) q = i-1 ~ L.>' P(i>, =1-o(k) are the variance values of target, background and image respectively; 0, ando, are the interclass and intraclass variance value respectively; 77 is the function for threshold selection. ID. IMPROVED ALGORITHM Otsu defined the following two evaluation function to obtain the threshold. They are as follows: k' = AugMin(c~L) = ArgMin(c~~,,~~ keg +w,d:) (8) (3) Otsu suggests that equation 7 would be more time-saving for image segmentation because interclass variance needn't to calculate all the variances of the two classes. But in fact, equation X(intrac1ass variance) can be transformed to another expression which can make (4) threshold calculation more simple and more timesaving [7]. The improved algorithm is proved as follows: k G (5) Equation 9 is the integral form of intraclass variance, the differential form of intraclass variance showed below: where nj is the amount of pixel at gray-level I, N is the total pixel amount in the image; p(i) is probability, 0, and W, are the probabilities of target and background respectively; pn, p,,,u, are the mean gray-level values of target,background and image respectively; oo, 0,,oT ak = (k- Po (k)y P(k)-2Po (k) t(. - Po (t)fp(br)& +-PI (k))z p(k)- ZP,(k) rjg- Pdk)fP(d& (lo) According to relative mathematical definition: a41.

3 %US po (k) and p, (k) are non-decreasing functiaris: are non-decreasing functions, there must be a root to the (k) =o, suppose: - ak If the equation: f (x) = 2x - lu, ( x) - p1 (x) exists, the equation root can be obtained within 0 to L-1 according to the feature of the equation. Sincef(O)*./(L-1) <O, we thus: can get the threshold through Newton Dichotomy. The flow chart of improved algorithm is as follows: (1)AcquUe the image grayscale range[pleft,pf(jght]; (2)Suppose U, = plej,a, = pright; pl (a,) can be obtained based on equation 3; (4)Get the value of f (a, 1. threshold K =U,, then go to step 6; (5)Iff(a,)>Othen a. =a,; Else iff( a,) < 0 then U, = a,, go to step 3; (6)Binary image with the threshold K;

4 ~~ ~~ IV. CONCL~~SION AND DISCUSSION In order to compare Otsu and the improved algorithm performance in segmenting path image, 300 path images under different illumination are analyzed. In Fig 1, the three original images represent different typical lighting conditions. The number beiow each image is the global mean value of the whole image. Experiments show that better image segmentation quality can be acquired with both Otsu and the improved algorithm in this paper (Fig 2, 3). The two algorithms can automatically select a right threshold to segment the path image and solve the path image segmentation problem under different illumination conditions. Original Threshold Iteration Th~shold Iteration However, the experimental data in Table 1 shows that iteration time of the improved algorithm is greatly reduced. So the time consumed to acquire a right threshold for path image segmentation is fairly saved. The prpcessing time of an image(648 X 430 pixels) is SOms by 'using Otsu algorithm, while the time by the improved algorithm is l0ms based on a computer platform configured with Intel Cerelon 11 OOMHz CPU, RAM 12XM,Windows OS. Thus, the new algorithm improves the real-time performance of image segmentation to a large extent. It is predicted that the improved fast algorithm will have a prosperous practical application in high-speed vision-based vehicle navigation. image left middle right by Otsu times by improved times Algorithm Table 1 information with Otsu and algorithm in the paper ' 183 Fig. 1 Original path image Fig. 2 Binary image with Otsu algorithm -843-

5 Fig. 3 Binary image with improved algorithm in this paper REFERENCES [l] P.K SAHOOA, Survey of Thresholding Technique, computer vision, graphice and image processing, vol.41, pp [Z] Otsu N.A, Threshold Selection Algorithm 6om Gray level Histogram [I], IEEE Trans System Man Cybernet, pp.62-66, sep [3] Brink A.D, Thresholding Digital Image Using Two-dimensional entropies[j], Pattern Recognition, vol. 25, pp~ , Aug [4] WANG Pei-&en, A Fast Image Segmentation Algorithm Based on Mixture of Two-dimensional Thresholdmg and FCM PP ,Sep [SI LIU Jim-zhuang, A Two-dimensional OTSU Auto Image Segmentation Method for Gray-level Image [J], Automation Journal, pp , Feb [6] ZHANG Liu-ji. Image Segmentation [MI, Science Industrial Press, 200 I [7] ZUO Qi, - A Fast image Segmentation Method Based on Histogram Evaluation Function, Computer Engineering an3 Application, vo1.19, pp. 54,2003.