Academic literature on the topic 'Harris corner detection'

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Journal articles on the topic "Harris corner detection"

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Zeng, Zhao Yang, Zhi Qiang Jiang, Qiang Chen, and Pan Feng He. "An Improved Corner Detection Algorithm Based on Harris." Advanced Engineering Forum 6-7 (September 2012): 717–21. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.717.

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In order to accurately extract corners from the image with high texture complexity, the paper analyzed the traditional corner detection algorithm based on gray value of image. Although Harris corner detection algorithm has higher accuracy, but there also exists the following problems: extracting false corners, the information of the corners is missing and computation time is a bit long. So an improved corner detection algorithm combined Harris with SUSAN corner detection algorithm is proposed, the new algorithm first use the Harris to detect corners of image, then use the SUSAN to eliminate the false corners. By comparing the test results show that the new algorithm to extract corners very effective, and better than the Harris algorithm in the performance of corner detection.
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Luo, Tao, Zaifeng Shi, and Pumeng Wang. "Robust and Efficient Corner Detector Using Non-Corners Exclusion." Applied Sciences 10, no. 2 (2020): 443. http://dx.doi.org/10.3390/app10020443.

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Corner detection is a traditional type of feature point detection method. Among methods used, with its good accuracy and the properties of invariance for rotation, noise and illumination, the Harris corner detector is widely used in the fields of vision tasks and image processing. Although it possesses a good performance in detection quality, its application is limited due to its low detection efficiency. The efficiency is crucial in many applications because it determines whether the detector is suitable for real-time tasks. In this paper, a robust and efficient corner detector (RECD) improved from Harris corner detector is proposed. First, we borrowed the principle of the feature from accelerated segment test (FAST) algorithm for corner pre-detection, in order to rule out non-corners and retain many strong corners as real corners. Those uncertain corners are looked at as candidate corners. Second, the gradients are calculated in the same way as the original Harris detector for those candidate corners. Third, to reduce additional computation amount, only the corner response function (CRF) of the candidate corners is calculated. Finally, we replace the highly complex non-maximum suppression (NMS) by an improved NMS to obtain the resulting corners. Experiments demonstrate that RECD is more competitive than some popular corner detectors in detection quality and speed. The accuracy and robustness of our method is slightly better than the original Harris detector, and the detection time is only approximately 8.2% of its original value.
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Zhang, Xin, and Xiu Hua Ji. "An Improved Harris Corner Detection Algorithm for Noised Images." Advanced Materials Research 433-440 (January 2012): 6151–56. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6151.

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The Harris corner detection algorithm is widely applied in image mosaic, which is simple and stable. However, the algorithm has a disadvantage that it obtains a lot of false corners when there exist some noise in an image. An improved Harris corner detection algorithm is proposed in this paper. The new algorithm reduces the noise impact greatly. The experimental results show that the improved algorithm not only reduces false corner points greatly, but also retain the majority of true corners. As a result, it improves the detection accuracy and reduces the chance of error matching in image registration.
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Yao, Na, Tie Cheng Bai, and Jie Chen. "Improved FAST Corner Detection Based on Harris Algorithm for Chinese Characters." Advanced Materials Research 850-851 (December 2013): 767–70. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.767.

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According to the characteristics of Chinese characters image, we propose an improved corner detection method based on FAST algorithm and Harris algorithm to improve detection rate and shorten the running time for next feature extraction in this paper. The image of Chinese characters is detected for corners using FAST algorithm Firstly. Second, computing corner response function (CRF) of Harris algorithm, false corners are removed. The corners founded lastly are the endpoints of line segments, providing the length of line segments for shape feature extraction. The proposed method is compared with several corner detection methods over a number of images. Experimental results show that the proposed method shows better performance in terms of detection rate and running time.
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Ryu, Jin-Kyu, and Dong-Kurl Kwak. "Flame Detection Based on Deep Learning Using HSV Color Model and Corner Detection Algorithm." Fire Science and Engineering 35, no. 2 (2021): 108–14. http://dx.doi.org/10.7731/kifse.30befadd.

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Recently, many image classification or object detection models that use deep learning techniques have been studied; however, in an actual performance evaluation, flame detection using these models may achieve low accuracy. Therefore, the flame detection method proposed in this study is image pre-processing with HSV color model conversion and the Harris corner detection algorithm. The application of the Harris corner detection method, which filters the output from the HSV color model, allows the corners to be detected around the flame owing to the rough texture characteristics of the flame image. These characteristics allow for the detection of a region of interest where multiple corners occur, and finally classify the flame status using deep learning-based convolutional neural network models. The flame detection of the proposed model in this study showed an accuracy of 97.5% and a precision of 97%.
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ZHANG Cong-peng, 张从鹏, and 魏学光 WEI Xue-guang. "Rectangle detection based on Harris corner." Optics and Precision Engineering 22, no. 8 (2014): 2259–66. http://dx.doi.org/10.3788/ope.20142208.2259.

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Guo, Yong Fang, Ming Yu, and Yi Cai Sun. "Study on a Real-Time Corner Detection Algorithm." Advanced Materials Research 159 (December 2010): 192–97. http://dx.doi.org/10.4028/www.scientific.net/amr.159.192.

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Conventional Harris corner detector is a desirable detector but it requires significantly more computation time. For MIC detector proposed by Trajkovic, the minimal computational demands of its operator make it well-suited for real-time applications, however the Trajkovic’s operator responses too readily to certain diagonal edges. For this reason, the paper proposed a new corner detection algorithm. The new corner detection algorithm adopted multigrid algorithm and preprocessed the lower resolution revision of the original image to obtain the potential corners, subsequently used autocorrelation matrix to get the corner response function for the corresponding points of the potential corner. The test results indicate the new corner detection algorithm can decrease edge responses and the number of textural corners effectively. Furthermore, it can satisfy the demands of real-time applications.
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Wang, Zhicheng, Rong Li, Zhihao Shao, et al. "Adaptive Harris corner detection algorithm based on iterative threshold." Modern Physics Letters B 31, no. 15 (2017): 1750181. http://dx.doi.org/10.1142/s0217984917501810.

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An adaptive Harris corner detection algorithm based on the iterative threshold is proposed for the problem that the corner detection algorithm must be given a proper threshold when the corner detection algorithm is extracted. In order to avoid the phenomenon of clustering and restrain the pseudo corner, this algorithm realizes the adaptive threshold selection by iteration instead of the threshold value of the Harris corner detection algorithm. Simulation results show that the proposed method achieves good results in terms of threshold setting and feature extraction.
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Zhang, Peng Xin, Wei Min Yang, and Chun Chen. "Research of Corner Detection Algorithm with Stack Volume Measurement." Advanced Materials Research 508 (April 2012): 271–74. http://dx.doi.org/10.4028/www.scientific.net/amr.508.271.

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In this paper, the comparation and analysis of the conner detection algorithm used in the three-dimensional reconstruction were made .Also the improvement and optimization to the parameters of harris algorithm were done. It is concluded that the hariis algorithm improved had a better effect ,simpler operation and better stability in the measurement of material stack volume measurement through the experimental results.
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LU, NA, and ZUREN FENG. "ACCUMULATIVE INTERSECTION SPACE BASED CORNER DETECTION ALGORITHM." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 08 (2008): 1559–86. http://dx.doi.org/10.1142/s0218001408006909.

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There is no parametric formulation of corner, so the conventional Hough transform cannot be employed to detect corners directly. A random corner detection method is developed in this paper based on a new concept "accumulative intersection space" under Monte Carlo scheme. This method transforms the corner detection in the image space into local maxima localization in the accumulative intersection space where the intersections are accumulated by random computations. The proposed algorithm has been demonstrated by both theory and experiments. The proposed algorithm is isotropic, robust to image rotation, insensitive to noise and false corners on diagonal edges. Unlike the other existing contour based corner detection methods, our algorithm can effectively avoid the influence of the edge detectors, such as rounding corners or line interceptions. Extensive comparisons among our approach and the other detectors including Harris operator, Fei Shen and Han Wang detector, Han Wang and Brady detector, Foveated Visual Search method and SIFT feature, have shown the effectiveness of our method.
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Dissertations / Theses on the topic "Harris corner detection"

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Loundagin, Justin. "Optimizing Harris Corner Detection on GPGPUs Using CUDA." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1348.

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ABSTRACT Optimizing Harris Corner Detection on GPGPUs Using CUDA The objective of this thesis is to optimize the Harris corner detection algorithm implementation on NVIDIA GPGPUs using the CUDA software platform and measure the performance benefit. The Harris corner detection algorithm—developed by C. Harris and M. Stephens—discovers well defined corner points within an image. The corner detection implementation has been proven to be computationally intensive, thus realtime performance is difficult with a sequential software implementation. This thesis decomposes the Harris corner detection algorithm into a set of parallel stages, each of which are implemented and optimized on the CUDA platform. The performance results show that by applying strategic CUDA optimizations to the Harris corner detection implementation, realtime performance is feasible. The optimized CUDA implementation of the Harris corner detection algorithm showed significant speedup over several platforms: standard C, MATLAB, and OpenCV. The optimized CUDA implementation of the Harris corner detection algorithm was then applied to a feature matching computer vision system, which showed significant speedup over the other platforms.
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Sääf, André, and Alvin Samuelsson. "Investigating Memory Characteristics of Corner Detection Algorithms using Multi-core Architectures." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-35842.

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In this thesis, we have evaluated the memory characteristics and parallel behaviour of the SUSAN (Smallest Univalue Segment Assimilating Nucleus) and Harris corner detection algorithms. Our purpose is understanding how the memory affects the predictability of these algorithms and furthermore how we can use multi-core machines to improve the execution time of such algorithms. By investigating the execution pattern of the SUSAN and Harris corner detection algorithms, we were able of breaking down the algorithms into parallelizable parts and non-parallelizable parts. We implemented a fork-join model on the parallelizable parts of these two algorithms and we were able to achieve a 7.9--8 times speedup on the two corner detection algorithms using an 8-core P4080 machine. For the sake of a wider study, we also executed these parallel adaptations on 4 different Intel platforms which generated similar results. The parallelized algorithms are also subjects for further improvement. We therefore investigated the memory characteristics of L1 data and instruction cache misses, cycles waiting for L2 cache miss loads, and TLB store misses. In these measurements, we found a strong correlation between L1 data cache replacement and the execution time. To encounter this memory issue, we implemented loop tiling techniques which were adjusted according to the L1 cache size of our test systems. Our tests of the tiling techniques exhibit a less fluctuating memory behaviour, which however comes at the cost of an increase in the execution time.
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Olgemar, Markus. "Camera Based Navigation : Matching between Sensor reference and Video image." Thesis, Linköping University, Department of Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15952.

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<p>an Internal Navigational System and a Global Navigational Satellite System (GNSS). In navigational warfare the GNSS can be jammed, therefore are a third navigational system is needed. The system that has been tried in this thesis is camera based navigation. Through a video camera and a sensor reference the position is determined. This thesis will process the matching between the sensor reference and the video image.</p><p>Two methods have been implemented: normalized cross correlation and position determination through a homography. Normalized cross correlation creates a correlation matrix. The other method uses point correspondences between the images to determine a homography between the images. And through the homography obtain a position. The more point correspondences the better the position determination will be.</p><p>The results have been quite good. The methods have got the right position when the Euler angles of the UAV have been known. Normalized cross correlation has been the best method of the tested methods.</p>
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Cacek, Pavel. "Tvorba panoramatických fotografií." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234953.

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This thesis deals with issues automatic composing panoramic photos from individual photos. Gradually examines the various steps of algorithms and methods used in them, which are used in creating panoramas. It also focuses on the design of the own system based on methods discussed to construct panoramas. This system is implemented using OpenCV library and it is created also a graphical interface using a Qt library. Finally, are in this thesis evaluated outcomes of this designed and implemented system on available datasets.
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Wu, Allen David. "Vision-based navigation and mapping for flight in GPS-denied environments." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37281.

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Traditionally, the task of determining aircraft position and attitude for automatic control has been handled by the combination of an inertial measurement unit (IMU) with a Global Positioning System (GPS) receiver. In this configuration, accelerations and angular rates from the IMU can be integrated forward in time, and position updates from the GPS can be used to bound the errors that result from this integration. However, reliance on the reception of GPS signals places artificial constraints on aircraft such as small unmanned aerial vehicles (UAVs) that are otherwise physically capable of operation in indoor, cluttered, or adversarial environments. Therefore, this work investigates methods for incorporating a monocular vision sensor into a standard avionics suite. Vision sensors possess the potential to extract information about the surrounding environment and determine the locations of features or points of interest. Having mapped out landmarks in an unknown environment, subsequent observations by the vision sensor can in turn be used to resolve aircraft position and orientation while continuing to map out new features. An extended Kalman filter framework for performing the tasks of vision-based mapping and navigation is presented. Feature points are detected in each image using a Harris corner detector, and these feature measurements are corresponded from frame to frame using a statistical Z-test. When GPS is available, sequential observations of a single landmark point allow the point's location in inertial space to be estimated. When GPS is not available, landmarks that have been sufficiently triangulated can be used for estimating vehicle position and attitude. Simulation and real-time flight test results for vision-based mapping and navigation are presented to demonstrate feasibility in real-time applications. These methods are then integrated into a practical framework for flight in GPS-denied environments and verified through the autonomous flight of a UAV during a loss-of-GPS scenario. The methodology is also extended to the application of vehicles equipped with stereo vision systems. This framework enables aircraft capable of hovering in place to maintain a bounded pose estimate indefinitely without drift during a GPS outage.
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Book chapters on the topic "Harris corner detection"

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Shunqing, Xiong, Zhou Weihong, and Xia Wei. "Optimization of Harris Corner Detection Algorithm." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-26007-0_8.

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Sun, Qingfeng. "An Improved Harris Corner Detection Algorithm." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6504-1_14.

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Wan, Li, Zhenming Yu, and Qiuhui Yang. "Corner Detection Algorithm with Improved Harris." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47791-5_30.

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He, Yiwei, Yue Ma, Dalian Liu, and Xiaohua Chen. "Parallel Harris Corner Detection on Heterogeneous Architecture." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93701-4_34.

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Chen, Peijiang, and Liying Wu. "Adaptive Harris Corner Detection Algorithm Based on Modified Detector." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3648-5_117.

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Shyam Prakash, Choudhary, Sushila Maheshkar, and Vikas Maheshkar. "Image Manipulation Detection Using Harris Corner and ANMS." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8569-7_9.

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Pan, Haixia, Yanxiang Zhang, Chunlong Li, and Huafeng Wang. "An Adaptive Harris Corner Detection Algorithm for Image Mosaic." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45643-9_6.

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Paul, Johny, Walter Stechele, Manfred Kröhnert, et al. "Resource-Aware Harris Corner Detection Based on Adaptive Pruning." In Architecture of Computing Systems – ARCS 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04891-8_1.

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Wang, Le, Minrui Fei, and Taicheng Yang. "Circular Mask and Harris Corner Detection on Rotated Images." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6370-1_52.

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Chen, Lu, Jing Han, Yi Zhang, and Lian-fa Bai. "Real-Time Panoramic Image Mosaic via Harris Corner Detection on FPGA." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21969-1_10.

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Conference papers on the topic "Harris corner detection"

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Jin, Dongliang, Songhao Zhu, and Yanyun Cheng. "Salient object detection via harris corner." In 2017 29th Chinese Control And Decision Conference (CCDC). IEEE, 2017. http://dx.doi.org/10.1109/ccdc.2017.7978684.

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Yao, Na, Tiecheng Bai, Xia Jiang, and Haifang Lv. "Improved Harris Corner Detection for Chinese Characters." In 2013 Fourth World Congress on Software Engineering (WCSE). IEEE, 2013. http://dx.doi.org/10.1109/wcse.2013.60.

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Ye, Zhiyong, Yijian Pei, and Jihong Shi. "An Improved Algorithm for Harris Corner Detection." In 2009 2nd International Congress on Image and Signal Processing (CISP). IEEE, 2009. http://dx.doi.org/10.1109/cisp.2009.5304635.

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Ye, Zhiyong, Yijian Pei, and Jihong Shi. "An Adaptive Algorithm for Harris Corner Detection." In 2009 International Conference on Computational Intelligence and Software Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cise.2009.5366231.

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Bekkanti, Ashok, Syed Karimunnisa, Subbarao Gogulamudi, KTPS Kumar, and CMAK Zeelan Basha. "Enhanced Computerized Bone Fracture Detection Using Harris Corner Detection." In 2020 International Conference on Smart Electronics and Communication (ICOSEC). IEEE, 2020. http://dx.doi.org/10.1109/icosec49089.2020.9215240.

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Han, Songqi, Weibo Yu, Hongtao Yang, and Shicheng Wan. "An Improved Corner Detection Algorithm Based on Harris." In 2018 Chinese Automation Congress (CAC). IEEE, 2018. http://dx.doi.org/10.1109/cac.2018.8623814.

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Luo, Shuhua, and Jun Zhang. "Accelerating Harris Algorithm with GPU for Corner Detection." In 2013 Seventh International Conference on Image and Graphics (ICIG). IEEE, 2013. http://dx.doi.org/10.1109/icig.2013.36.

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Ram, Parvathy, and S. Padmavathi. "Analysis of Harris corner detection for color images." In 2016 International conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 2016. http://dx.doi.org/10.1109/scopes.2016.7955862.

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Dey, Nilanjan, Anamitra Bardhan Roy, and Achintya Das. "Detection and measurement of bimalleolar fractures using Harris corner." In the International Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2345396.2345405.

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Changan, Komal Shrirang, and Purushottam G. Chilveri. "Stereo image feature matching using Harris corner detection algorithm." In 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT). IEEE, 2016. http://dx.doi.org/10.1109/icacdot.2016.7877675.

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