Phương pháp đếm tập giấy xếp chồng nhiều lớp bằng cách ghép hình ảnh

26 NGHIÊN CỨU KHOA HỌC Tạp chí Nghiên cứu khoa học - Đại học Sao Đỏ, ISSN 1859-4190 Số 3(62).2018 THE IMAGE STITCHING METHOD FOR OVERLAPPING PAPER COUNTING PHƯƠNG PHÁP ĐẾM TẬP GIẤY XẾP CHỒNG NHIỀU LỚP BẰNG CÁCH GHÉP HÌNH ẢNH Pham Thi Dieu Thuy1, 2, Ha Minh Tuan1, 2, Nguyen Trong Cac1 , Changyan Xiao 2 Email: 1Sao Do University, Viet Nam 2Ho Nam University, China Date received: 28/5/2018 Date received after correction: 20/9/2018 Release date: 28/9/2018 A

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bstract Stack paper counting has a huge industrial demand in the field of printing and packaging. According to the requirement of real-time high precision for the multi camera system, a new method of image stitching between overlapping papers is presented. The solution helped to deal with the rotation angle deviation,the scale differences and unobvious characteristics in actual images of the counting instrument. Firstly, the ridge two images are acquired. Next the rotating angle is corrected by improved Hough transform method. Afterward, the correcting angle is analyzed in the frequency domain eliminate the effects of scale differences. Finally, using Zero Mean Cross Correlation (ZNCC) in the corrected images to get the relevance of local signals between images to locate the overlapping images. In industrial production lines, the images of the overlapping papers are very similar. General algorithms can not be applied to it, while the counting error will be caused by adding the characteristics with external labels. Traditional image fusion is replaced by locating the overlapping papers in our algorithm, which can not only improve the stitching precision without the help of the external tag, but also guarantee the real-time performance of the algorithm by using the method of stitching and line detection at the same time. It is verified in experiments that the algorithm can fully meet the requirements of the precision, universal and timeliness of the multiple types of paper. Keywords: Image stitching; overlapping paper location; Hough transform; frequency domain analysis; computer vision. Tóm tắt Đếm giấy xếp chồng là một yêu cầu lớn trong công đoạn in ấn và đóng gói trong công nghiệp. Nhằm đáp ứng yêu cầu về thời gian thực và độ chính xác cao của hệ thống nhiều camera, bài báo này đề xuất phương pháp ghép ảnh giấy xếp chồng mới. Các giải pháp được đưa ra giải quyết ba thách thức chính là độ chênh góc nghiêng, sai lệch tỷ lệ và những đường nét hiển thị không rõ nét trong ảnh thu được của thiết bị đếm. Trước tiên, xác định sườn của hai ảnh của tập giấy được thu bởi hai camera. Sau đó, sử dụng phương pháp điều chỉnh góc nghiêng dựa theo biến đổi Hough cải tiến. Tiếp theo, phân tích góc nghiêng đã được hiệu chỉnh trong môi trường tần số và khử ảnh hưởng của sai lệch tỷ lệ. Cuối cùng, áp dụng phép đối chiếu chéo trung bình 0 (ZNCC) trên ảnh đã hiệu chỉnh để tìm ra những điểm tương đồng giữa hai ảnh dùng cho việc xác định vùng ảnh chồng chéo. Ảnh giấy xếp chồng trong công nghiệp rất tương đồng, các phương pháp cũ không giải quyết được sai số đếm vì sự hiện diện của đường nét không mong muốn. Do vậy, phương pháp xác định vùng chồng chéo trong ảnh giấy xếp chồng tốt hơn phương pháp ghép ảnh truyền thống vì không những cải thiện độ chính xác ghép nối mà không cần thêm nhãn phụ mà còn đảm bảo hiệu năng thời gian thực bằng cách đồng thời ghép ảnh và tìm đường thẳng trong ảnh. Thực nghiệm đã cho thấy phương pháp được đề xuất đáp ứng đầy đủ yêu cầu độ chính xác, tính vạn năng và thời gian thực thi trên nhiều loại giấy khác nhau. Từ khóa: Ghép ảnh tập giấy; xác định vùng ảnh chồng chéo; biến đổi Hough cải tiến; phân tích ảnh trong miền tần số; thị giác máy tính. Người phản biện: 1. PGS.TSKH. Trần Hoài Linh 2. TS. Đặng Thúy Hằng 27 LIÊN NGÀNH ĐIỆN - ĐIỆN TỬ - TỰ ĐỘNG HÓA Tạp chí Nghiên cứu khoa học - Đại học Sao Đỏ, ISSN 1859-4190 Số 3(62).2018 1. INTRODUCTION In the packaging and printing industry, the counting of paper products is a very important and indispensable task. If the count is not accurate, it will cause direct economic losses to the company. Similar applications also include the measurement of the number of thin-film products such as solar wafers [1], PCB boards [2], and cardboards. However, traditional physical measurement methods such as thickness and weighing measurement have defects such as large counting error and low efficiency. Mechanical paper based on pneumatic suckers may cause paper damage [3]. With the rapid development of computer technology, machine vision methods have been widely used in production practice. For the task of counting super-laminated and ultra-thin papers, since the field of view and the resolution of the single camera are constrained by each other, only a plurality of images can be counted. Therefore, the visual algorithm for ultra-high stacking paper count is mainly divided into two parts: stitching and line detection [4]. At the same time, the performance of industrial cameras, lenses, and light sources has increased dramatically in recent years, and prices have continued to decrease. In order to ensure the stability, real-time, and accuracy of the instruments, a counting instrument based on camera arrays has been designed. The traditional image stitching technique combines the image sequences of overlapping regions with each other to form a complete image. Common image stitching algorithms are divided into three major categories: (1) region-based stitching algorithms; (2) feature-based stitching algorithms; and (3) integrated stitching algorithms. Region-based image stitching algorithms mainly match by calculating the correlation between local image blocks, such as Zero mean normalized cross-correlation (ZNCC) [5]. This method can eliminate the effects of background drift and has high real-time performance, but it is easily affected by problems such as image rotation and scale changes. The feature-based image stitching method has high robustness and stability, but has large amount of calculation results in very poor real-time performance and requires the images to have sufficient feature points. For example, the SIFT operator [6], the SURF operator [7], and the latest ORB operator [8] all have strong robustness to translation, rotation, and other transformations, but the amount of calculation is large. Even if Lan Hong [9] and other united information projection entropy optimize the SIFT operator, Zhu Lin et al. [10] use Relief-F algorithm to reduce the SURF descriptor dimension, but the algorithm complexity is still too high. In addition, the feature-based stitching algorithm requires that the image has a sufficient number of feature points with large differences. However, the actual paper image feature points are not obvious, the number is small and they are all very similar. Therefore, this type of algorithm is not suitable for the counting instrument in this paper. The third kind of stitching algorithm is an integrated algorithm designed for the characteristics of the actual application image. The general stitching algorithm has high accuracy and good real-time performance, but it is too targeted and is not suitable for other scenarios. For example, Estrada et al. [11] used a stitching algorithm for the eye video designs collected by the head-mounted detection camera, and the assembled panoramas can assist doctors in diagnosis. Wang et al. [12] completed the mosaic task for the domain in the Hough space, which has a good mosaic effect on the workpiece image with straight edges. This paper proposes a high-precision, high-real- time tier stack from the perspective of the Huff domain, frequency domain, and correlation, aiming at the stitching problems such as angle deviation, scale difference, and illumination difference among actual stacked paper images, an overlapping linear positioning algorithm between paper images. 2. INSTRUMENT AND STITCHING PROPLEMS 2.1. Instrument Introduction For thousands of ultra-thin paper (thinest 0.08mm) counting tasks, to ensure counting accuracy, each piece of paper occupies at least 8 pixels, so 5 million pixels (2592*1944) industrial cameras count 324 at most and the field of view is approximately 26mm*19mm. Obviously, a single camera cannot complete its task independently due to the constraints of its own field of view and resolution. Moreover, as shown in Figure 1(a), the instrument uses the smallest MINI industrial camera with a cross-sectional dimension of 29mm*29mm parallel to the field of view. With the field of view and resolution that guarantee the measurement accuracy, even if the two cameras are closely arranged, the captured image still has no overlapping area. In summary, this article first designed a staggered array of camera arrays and applied to paper counting instruments. This form can ensure that there is enough overlap between camera fields of view. The instrument structure is shown in Fig. 1(b): 28 NGHIÊN CỨU KHOA HỌC Tạp chí Nghiên cứu khoa học - Đại học Sao Đỏ, ISSN 1859-4190 Số 3(62).2018 the industrial camera, the lens, and the camera stand together constitute the camera array 1; the laminated paper 5 is stacked on the base and in front of the camera array 1 while the light source 4 provides the light. When the camera array 1 is installed, it is ensured that sufficient overlapped areas are reserved between the cameras, and then the industrial computer 2 can complete the image collection by sending acquisition signals to the camera array 1. Figure 1. Schematic diagram: (a) Schematic view of the overlapping view area: (1) paper; (2) mini industrial camera; (b) Instrument internal structure diagram: (1) camera array; (2) industrial computer; (3) power supply; (4) light source; (5) paper stack. Compared with general image stitching tasks, the image acquisition environment of the instrument is more simple and stable, but the accuracy requirements are more demanding. As far as the acquisition environment is concerned, the general stitching task needs to process images collected by visual devices of different models or even hardware aging, and the stitching interference problem in the images is more serious. However, the laminated paper counting machine in this paper has a good image acquisition environment, and image distortion and other strong interference problems cannot be considered (as shown in the high quality image in Figure 2(a)). In terms of accuracy requirements, the general stitching task can allow error of a dozen pixels, and then image fusion [11] can ensure that the spliced image meets the requirements visually. However, the ultimate purpose of this instrument is paper counting. The paper peaks are separated by about seven pixels. Therefore, the required error is controlled at about 3 pixels, and image fusion is not suitable. The images of the paper collected by the instrument is shown in Fig. 2(a). The similarity between the images in each area is very high, and there is no significant difference between the overlapping area and the non-overlapped area. To better illustrate the problem, feature-based surf operators [7] are used to stitching (a) graphs. The result is shown in figure 2(b): The matching feature points (indicated by circles) in the two figures are connected by a straight line (only the groups of feature points with the highest matching degree are displayed). The feature point matching of the surf operator needs to be matched several times, and the calculation volume is too large to meet the requirements of real-time detection in the industry. For the problem that the characteristic is not obvious, the auxiliary point method (such as the label at A in Figure 2(c)) is often used in the industry to increase the image feature. This method is very effective for image stitching of thicker laminated paper (such as a hard cigarette box with a thickness of 0.23 mm). However, the purpose of this counting instrument is to measure paper with a thickness of 0.08 mm or more. This method of labeling does not meet the development requirements. It is mainly due to the thickness of the label itself. Even if the paper is completely attached to the label, after image formation (as shown in Figure 2(d) at the label edge B and at the label shadow C) will result in a larger label and paper end surface Gap. Coupled with the problem of each camera position and the incident angle of the light is not the same, a paper-based stitching algorithm will result in a sheet-to-sheet error. At the same time, external labels are susceptible to external disturbances, such as problems caused by long-term work such as stains, positional deviations, and even dropping off. Figure 2. Problem illustration: (a) Two images to be stitched; (b) Surf operator matching results; 29 LIÊN NGÀNH ĐIỆN - ĐIỆN TỬ - TỰ ĐỘNG HÓA Tạp chí Nghiên cứu khoa học - Đại học Sao Đỏ, ISSN 1859-4190 Số 3(62).2018 (c) Image with label; (d) Partially enlarged image. Figure 3. The image stitching based on improved Hough transform algorithm 2.2. Stacking paper image stitching algorithm For the particularity of industrial paper images and the requirements for stitching accuracy, we first propose a high-precision real-time stitching method that uses overlay papers instead of image fusion. This method uses the improved Hough transform to solve the angle deviation problem, the frequency domain analysis to solve the scale difference between the images and the correlation between the corrected images to find the hidden image features, and finally determine the precise position of the overlapping paper between images. The complete algorithm flow chart shown in Figure 3, is mainly divided into the following three steps: I. Rotation angle correction: Firstly, the original 2D image and the line detection [4] are used to obtain the ridge image sum. Then use the modified Hough Transform to obtain the sum of the reference angles, respectively, and derive the rotational difference angle accordingly. Finally, an affine transformation of the angle is performed to obtain an angle-corrected image. II. Scaling correction: The 1D profile signal is extracted from the angle-corrected image and the original 2D image respectively, and a fundamental frequency frequency sum is obtained by performing a Fast Fourier Transform (FFT). Then, the scale ratio between the two images is calculated according to the fundamental frequency and the image is corrected by affine scaling of the angle-corrected image to obtain the finally corrected image. III. Positioning overlapping papers: Select the 1D profile signal from the original 2D image, then use the ZNCC operator to calculate the correlation of the corresponding overlapping region of the signal in the corrected image, and finally fuse the correlation measure result to determine the position of the overlapping straight line in the baseline graph. Since this article is mainly for the stitching of the count of the laminated paper, after the overlap line with the image II is detected in the image I, the stitching of the image is completed. When counting, when the overlapping line of image I is detected, it automatically jumps to image II to continue counting. Compared with the traditional stacking paper counting algorithm, this paper does not need to mark the image to be stitched, in order to increase its feature points, and realizes the marker-free stitching, which improves the real- time performance of the algorithm. 2.2.1. Rotation angle correction Rotation angle correction is divided into three steps: one is to extract a straight line for finalizing paper orientation information; the other is to use an improved Hough transform algorithm to obtain the angle difference between the images to be stitched; and thirdly, to perform affine transformation on the stitched image. The two images collected by the paper counting instrument are grayed out. The results are shown in Figure 4. (a) (b) Figure 4. The original image to be stitched: (a) Original image I; (b) Original image II. 30 NGHIÊN CỨU KHOA HỌC Tạp chí Nghiên cứu khoa học - Đại học Sao Đỏ, ISSN 1859-4190 Số 3(62).2018 From the above two figures, there is an angle difference between the original image I and the original image II. So do a rotation angle correction. Figure 5. Extracts the straight line used to finalize the paper orientation information: (a) Original image I; (b) The line-detected ridge image corresponding to the original image; (c) The Hough space diagram of the ridge image, abscissa range; (d) The straight line after the Hough transform extracted from the rough line (e). As shown in Fig 4(a), the paper in the edge image of the laminated paper is placed next to the stack and arranged neatly, and the paper end surface has obvious and abundant directional characteristics, so it can be used as a reference source for image stabilization. In order to accurately extract the reference direction information of the image, it is necessary to perform line detection [4] to obtain the ridge line diagram shown in (b). In the actual measurement, due to the quality of the paper and the limitations of the line detection algorithm, the resulting ridgeline diagram often suffers from disturbances such as breakage, misdetection, so the ridgeline chart needs to be further processed. In this paper, the improved Hough transform is used to obtain accurate direction information and the interference in the ridge diagram can be well eliminated. The traditional Hough transform formula is as follows: p x y *cos( ) *sin( )  (1) The abscissa angle representing the Hough field in the formula represents the ordinate distance. The basic principle is to use the dot- line correspondence between the domain and the domain, so as to find the point where the lines intersect most in the domain to determine the straight line in the domain. According to the actual situation of the laminated paper counting instrument, the Hough transform is first improved, namely searching for the range by restricting the angle and intercepting the necessary Transform area to speed up the algorithm. This is because the position of the camera in the paper- measuring instrument is fixed, so that the angle difference and the overlapping area between the images are constrained within a certain range. Therefore, the angle is constrained between and the partial images are appropriately cut and subjected to the Hough transform. At the same time, according to the accuracy requirement, the step length is set to 0.5 and the step length is set to 0.1. The Huff domain obtained for (b) is shown in (c), where each line in the ridge corresponds to a bright spot in the Hough domain. Because the step accuracy is too high, the bright spot area difference in the Hough domain (figure (c)) is too small. If the line is directly determined by the threshold value, the result is shown in (d), which results in a real straight line location. After the Hough transformation, there are a number of straight lines with little difference in angle. Inspired by Wang et al. [12] optimization of Hough test results, this paper uses special judgment conditions to optimize and refine the Hough transform results and extract the straight lines used to determine the direction information. The judgment conditions are as follows: (1) Delete the nearest neighbor line, leaving only the line with the largest accumulator value in the Hough transform. (2) To prevent interference, remove the three lines that are the largest and the smallest in the straight line. After optimizing and purifying, several sets of straight lines are obtained as shown in (e), and then the average angle of the remaining straight lines is taken as the reference direction of the image. Using the above-described improved Hough transform algorithm, the reference angle sums of the image to be stitched are respectively obtained, and then the rotational differential angle is calculated using the formula. Finally, using the affine transformation pair rotation angle, the formula is as follows: (2) Where 31 LIÊN NGÀNH ĐIỆN - ĐIỆN TỬ - TỰ ĐỘNG HÓA Tạp chí Nghiên cứu khoa học - Đại học Sao Đỏ, ISSN 1859-4190 Số 3(62).2018 And a = a1 – a2, where a1 and a2 are rotation deviation between the ridge and x coordinate in the ridge image I and II respectively. Among them, the affine transformation obtained and eliminated the angle difference. 2.2.2. Positioning overlapping paper As shown in Fig. 5(a), the 2D image feature points are very few, but the 1D profile signal (Fig. 5(b)) still shows more obvious features. Therefore, the correlation of one-dimensional profile signals between images can be found at this implicit image features. Figure 6: Positional alignment of coincident lines: (a) Original ridges to be stitched I; (b) Corrected ridges I; (c) Original ridges to be stitched II; (d) Correlation metrics; (e) Image stitching of alignment lines I; (f) Image stitching of alignment lines II. Figure 6 (a) and (c) are the line-detected ridge images to be stitched, Fig (a) obtained after rotation, scale correction (b). Intercept the 1D signal in the first half of figure (c) (put the initial coordinate at the valley between the papers), and then use the ZNCC operator [5] to calculate the correlation in the second half of figure (b). The middle and latter half are preliminarily approximate overlapping areas. For ease of understanding, the correlation result is processed and displayed in gray scale (as shown in (d)), where 0.5 or less is set to 0, 0.5 ~ 0.8 is 80, 0.8 ~ 0.9 is 150, and 0.9 or more is set to 255. In Fig 6(d) the ellipse position at A is the same as the ellipse at A in Fig (b). It can be seen that only the correlation near the true coordinate is higher than 0.9, and the correlation of 0.8 ~ 0.9 all appears on the straight line whose angle is the reference angle, which is all on the left side of the same paper position. This is due to the high similarity of the 1D signal in the vertical section of the paper end image along the paper end face. Obviously, due to too many high correlation results and small differences, it is not conducive to stably finding precise matching coordinates. (1) Traverse the correlation metrics to find the coordinates with a correlation greater than 0.8. (2) Place all coordinates in the new binary image, and use Hough transform to find the line G with the highest number of votes. (3) Convert the straight G coordinate to the figure (b), and then perform the affine inverse transformation of the result of 2.1 and 2.2 in the straight line G to convert the coordinate into the baseline map (e). At this time, the nearest straight line is searched to the right based on the straight line G coordinate position after the conversion, so that the overlapping straight line B is determined. Through the above method, the overlapping straight line B of the to-be-constructed image I is determined, and at the same time the template signal start coordinate in the to-be-joined image II is searched to the right, and the overlapping straight line B in the ridge map (f) is further positioned. As shown in the overlapped line B in (e) (f), this algorithm can easily guarantee the precision requirement of the post-counting task. 3. EXPERIMENTS AND ANALYSIS In this section, we will conduct extensive experiments and testing to verify the performance of the system based on our proposed algorithms. The image data were captured from a diversity of substrate samples. The image stitching software was developed with a hybrid programming of LabView and C++ language. The configuration of the industrial computer is 2.7 GHz dual-core CPU and 4 GB RAM, and the execution time (including image acquiring time) is about 100ms for the whole image stitching algorithm. To verify that our algorithm can satisfy the requirement of various kinds of substrates measurement, a diversity of samples with different material and surface properties 32 NGHIÊN CỨU KHOA HỌC Tạp chí Nghiên cứu khoa học - Đại học Sao Đỏ, ISSN 1859-4190 Số 3(62).2018 Figure 7. Four types of samples with different characteristic. substrate color, brightness, and contrast. Without further preprocessing, our proposed method was directly applied to these stack images using the same fixed parameters. An exception is with the printed paper stack, where the indistinguishable interval between neighboring substrates makes it difficult to recognize the stripes. This is reflected by the broken or incomplete ridge lines in Fig. 7. However, the missed detection of stripes only rarely happened in local regions. In this paper, batch on-line inspections were carried out for 0.08mm, 0.1mm, 0.11mm, and 0.23mm thick stacks of different samples. The statistical instrument can ensure that the recall rate reaches 99.9% when the accuracy rate is 100%, and there is no need to adjust the parameters when switching between different thickness laminate products. Therefore the instrument greatly reduces the input of manpower and material resources and improves the production efficiency, fully meeting the requirements of industrial applications. To verify the performance of the algorithm, we tested the images of four different types of paper collected by a laminated paper counting apparatus. Experimental results for 50 sets of images show that they all quickly and accurately locate the overlapping lines between the images. 4. CONCLUSION This algorithm is mainly applied to the stacking paper counting instruments in engineering practice. Its primary purpose is to accurately count paper products. Therefore, the requirements for stitching are high real-time and high precision. In terms of real-time performance, since the counting equipment is formed and the parameters between cameras are relatively fixed, and in order to eliminate long-term vibration disturbances, we can perform the correction of the rotation angle and scale only at each start-up and then directly carry out the subsequent calculations. Enter the same rotation difference angle and scale the two parameters. It can greatly accelerate the real-time performance of the algorithm under the premise of ensuring accuracy. The guarantee of accuracy lies in the fact that the algorithm abandons the traditional idea of stitching and merging into a panorama and then counts it. Instead, it first performs line detection and then locates overlapping lines from the ridge line. This method can guarantee the maximum degree of post-paper counting accuracy. are used for testing. As shown in Fig. 7, a series of stacked substrates is arranged sequentially at the top row and the stitched images derived by performing our proposed method are at the bottom row. It can be seen that these stacked substrates obviously vary in geometric and photogrammetric parameters such as stripe width, inter stripe gap, substrate color, brightness, and contrast. Without further preprocessing, our proposed method was directly applied to these stack images using the same fixed parameters. An exception is with the printed paper stack, where the indistinguishable interval between neighboring substrates makes it difficult to recognize the stripes. This is reflected by the broken or incomplete ridge lines in Fig. 7. However, the missed detection of stripes only rarely happened in local regions. 33 LIÊN NGÀNH ĐIỆN - ĐIỆN TỬ - TỰ ĐỘNG HÓA Tạp chí Nghiên cứu khoa học - Đại học Sao Đỏ, ISSN 1859-4190 Số 3(62).2018 REFERENCE [1]. Fang Chao, Tan Wei, Du JianHong. Research on Solar Wafer Counting Based on Texture Features[J]. Information and Electronic Engineering, 2011, 9(2): 185-189. [2]. Wu P H, Kuo C H. A counting algorithm and application of image-based printed circuit boards [J]. 2009. [3]. Uchida I, Hirata A. Suction head in a paper sheet counting machine: U.S. Patent 4, 262, 896[P]. 1981-4-21. [4]. Harba R, Berthe B, Perdoux D, et al. Card- counting device: U.S. Patent Application 12/597, 678[P]. 2008-4-23. [5]. Di Stefano L, Mattoccia S, Tombari F. An algorithm for efficient and exhaustive template matching[M]// Image Analysis and Recognition. Springer Berlin Heidelberg, 2004: 408-415. [6]. Lowe D G. Distinctive image features from scale- invariant keypoints[J]. International journal of computer vision, 2004, 60(2): 91-110. [7]. Bay H, Ess A, Tuytelaars T, et al. Speeded-up robust features (SURF)[J]. Computer vision and image understanding, 2008, 110(3): 346-359. [8]. Rublee E, Rabaud V, Konolige K, et al. ORB: An efficient alternative to SIFT or SURF[C]//2011 International conference on computer vision. IEEE, 2011: 2564-2571. [9]. Lan Hong, Hong YuHuan, Gao XiaoLin. Research and Application of Image Registration Technology for Panoramic Mosaic[J]. Computer Engineering and Science, 2016, 38(02): 317-324. [10]. Zhu Lin, Wang Ying, Liu ShuYun, et al. Fast image stitching algorithm based on improved fast robust features[J]. Journal of Computer Applications, 2014, 34(10): 2944-2947. [11]. Estrada R, Tomasi C, Cabrera M T, et al. Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing[J]. Biomedical optics express, 2011, 2(10): 2871-2887. [12]. Wang K, Shi T, Liao G, et al. 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