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Journal articles on the topic 'COMPLEX BACKGROUND'

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Author: Grafiati

Published: 11 September 2023

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1

Salman, Ahmad, Salman Maqbool, Abdul Hannan Khan, Ahsan Jalal, and Faisal Shafait. "Real-time fish detection in complex backgrounds using probabilistic background modelling." Ecological Informatics 51 (May 2019): 44–51. http://dx.doi.org/10.1016/j.ecoinf.2019.02.011.

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2

Xiang, Ying Zhuo, Dong Mei Yang, and Ji Kun Yan. "Vehicles Categorization in Complex Background." Advanced Materials Research 998-999 (July 2014): 708–11. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.708.

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This paper presents a novel approach to categorize multi-view vehicles in complex background using only two dimension characteristic vectors instead of high dimension vectors. Vehicles have large variability of models and the view-point makes the appearance change dramatically. Significant characteristics should be chosen as the evidence to categorize. In this paper, we categorize the vehicles into two categories – cars and lorries. Line detection method is used and calculating the average line length and the number of parallel lines as the two characteristics. A linear classifier is trained using 30 different view cars and lorries as the training set and an 10 additional different cars and lorries as the testing set.

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3

Liu, Chao, Fei Peng Da, and Chen Xing Wang. "Text Extraction from Complex Background Images." Advanced Materials Research 765-767 (September 2013): 975–79. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.975.

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The rapid development of internet technology leads to an effective way to share ideas with digital information. Extracting text information from digital information especially complex background images has become increasingly important due to the demand of text understanding. In this paper, we propose a k-means clustering based method for text extraction. After locating text regions with the corner detection, an improved k-means algorithm is adopted to extract text from complex background. Experiment results show the high performance of the presented method.

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4

Pang, Xin. "Saliency Detection Algorithm under Complex Background." Journal of Information and Computational Science 12, no. 2 (January 20, 2015): 423–29. http://dx.doi.org/10.12733/jics20105232.

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5

Song, Xian-Rui, and Yu-Long Qiao. "Moving Target Detection in Complex Background." Research Journal of Information Technology 4, no. 4 (April 1, 2012): 195–203. http://dx.doi.org/10.3923/rjit.2012.195.203.

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6

Peng, Gao, and Li Liang. "Complex Background Palm Segmentation using SVM." International Journal of Software Engineering and Its Applications 9, no. 5 (May 31, 2015): 335–46. http://dx.doi.org/10.14257/ijseia.2015.9.5.33.

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7

Gao, Peng, and Liang Li. "Complex Background Palm Segmentation using SVM." International Journal of Security and Its Applications 9, no. 5 (May 31, 2015): 335–46. http://dx.doi.org/10.14257/ijsia.2015.9.5.33.

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8

Santoyo-Morales, Juana E., and Rogelio Hasimoto-Beltran. "Video Background Subtraction in Complex Environments." Journal of Applied Research and Technology 12, no. 3 (June 2014): 527–37. http://dx.doi.org/10.1016/s1665-6423(14)71632-3.

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9

Peng, Gui Hua, He Chen, and Qiang Wu. "Infrared Small Target Detection under Complex Background." Advanced Materials Research 346 (September 2011): 615–19. http://dx.doi.org/10.4028/www.scientific.net/amr.346.615.

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This paper presents an algorithm for detecting the small infrared target under complex background. An method, Local Mutation Weighted Information Entropy (LMWIE), is proposed to suppress background. Then, enhance targets’ gray value by calculating the local energy. For the problem that the gray value of noises is enhanced with the gray value improvement of targets, image segmentation bases on the adaptive threshold. Experiment results indicate that it is a robust and effective small target detection algorithm.

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10

CHEN, Xi, Bin HE, Yong-ji LONG, Xiang-yu SONG, and Guo-ling BI. "Adaptive ship target detection in complex background." Chinese Journal of Liquid Crystals and Displays 37, no. 3 (2022): 405–14. http://dx.doi.org/10.37188/cjlcd.2021-0219.

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11

YUAN, Wei-qi, and Chun-xia HAN. "Multi-face detection algorithm in complex background." Journal of Computer Applications 30, no. 3 (April 2, 2010): 635–38. http://dx.doi.org/10.3724/sp.j.1087.2010.00635.

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12

Pai, Zhang. "Adaptive Image Enhancement Algorithm with Complex Background." Open Cybernetics & Systemics Journal 9, no. 1 (June 26, 2015): 594–600. http://dx.doi.org/10.2174/1874110x01509010594.

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13

Hou, Jing, Guihong Tan, Gerald R. Fink, Brenda J. Andrews, and Charles Boone. "Complex modifier landscape underlying genetic background effects." Proceedings of the National Academy of Sciences 116, no. 11 (February 25, 2019): 5045–54. http://dx.doi.org/10.1073/pnas.1820915116.

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The phenotypic consequence of a given mutation can be influenced by the genetic background. For example, conditional gene essentiality occurs when the loss of function of a gene causes lethality in one genetic background but not another. Between two individual Saccharomyces cerevisiae strains, S288c and Σ1278b, ∼1% of yeast genes were previously identified as “conditional essential.” Here, in addition to confirming that some conditional essential genes are modified by a nonchromosomal element, we show that most cases involve a complex set of genomic modifiers. From tetrad analysis of S288C/Σ1278b hybrid strains and whole-genome sequencing of viable hybrid spore progeny, we identified complex sets of multiple genomic regions underlying conditional essentiality. For a smaller subset of genes, including CYS3 and CYS4, each of which encodes components of the cysteine biosynthesis pathway, we observed a segregation pattern consistent with a single modifier associated with conditional essentiality. In natural yeast isolates, we found that the CYS3/CYS4 conditional essentiality can be caused by variation in two independent modifiers, MET1 and OPT1, each with roles associated with cellular cysteine physiology. Interestingly, the OPT1 allelic variation appears to have arisen independently from separate lineages, with rare allele frequencies below 0.5%. Thus, while conditional gene essentiality is usually driven by genetic interactions associated with complex modifier architectures, our analysis also highlights the role of functionally related, genetically independent, and rare variants.

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14

Urban, Matthew W., Chenyi Wang, Azra Alizad, and Mostafa Fatemi. "Complex background suppression for vibro-acoustography images." Ultrasonics 56 (February 2015): 456–72. http://dx.doi.org/10.1016/j.ultras.2014.09.014.

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15

Yang, Guangzheng, and Thomas S. Huang. "Human face detection in a complex background." Pattern Recognition 27, no. 1 (January 1994): 53–63. http://dx.doi.org/10.1016/0031-3203(94)90017-5.

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16

Ma, Yan, Yongru Zhang, and Shengming Yang. "Soil Image Segmentation Algorithm under Complex Background." Journal of Physics: Conference Series 1549 (June 2020): 022036. http://dx.doi.org/10.1088/1742-6596/1549/2/022036.

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17

Ding, Ying, Li Wen-Hui, Fan Jing-Tao, and Yang Hua-Min. "Robust moving object detection under complex background." Computer Science and Information Systems 7, no. 1 (2010): 201–10. http://dx.doi.org/10.2298/csis1001201d.

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We present a novel method to robustly and efficiently detect moving object, even under the complexity background, such as illumination changes, long shadows etc. This work is distinguished by three key contributions. The first is the integration of the Local Binary Pattern texture measure which extends the moving object detection work for light illumination changing. The second is the introduction of HSI color space measure which removes shadows for the background subtraction. The third contribution is a novel fuzzy way using the Choquet integral which improves detection accuracy. The experiment results using several dataset videos show the robustness and effectiveness of the proposed method.

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18

Yin, Yang, Yang Liu, Shuai Chen, and Quanshun Yang. "UAV Target Detection under Complex Sky Background." Journal of Autonomous Intelligence 5, no. 2 (October 22, 2022): 1. http://dx.doi.org/10.32629/jai.v5i2.514.

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<p class="15">At present, unmanned aerial vehicles (UAVs) are widely used in various fields, and the management of UAVs is very important to solve the problems in the field of low-altitude safety. Due to the low flying height, small radar cross section, and inconspicuous characteristic signals of UAVs, the detection of UAVs based on video frames taken by fixed cameras cannot meet the existing requirements in terms of tracking speed and recognition accuracy. This paper proposes a multi-sensor fusion model. Firstly, the UAV target signal is improved by spatial filtering and improved Sobel operator edge detection algorithm, and then Gaussian filter is used to denoise, and finally the UAV small target is extracted based on the maximum inter-class variance method threshold segmentation algorithm. Experimental results show that this method can effectively enhance the UAV target signal in a complex environment, and the threshold segmentation method also has good adaptability, and can effectively screen UAVs under a complex sky background.</p>

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19

Wang, Hong Xia, and Ning Liu. "Research on Fingerprint Image Segmentation under Complex Background." Applied Mechanics and Materials 687-691 (November 2014): 3612–15. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3612.

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Fingerprints are widely used in biometric techniques for automatic personal identification. In this paper, a fingerprint segmentation method is proposed to overcome the bad impact imposed by the complex background of the fingerprint image. The proposed approach is based on combining global and local processing to achieve segmentation of fingerprint images. The approach is implemented in three stages: preprocessing, segmentation, and post-processing. The effectiveness of the proposed method can be confirmed through the experimental results.

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20

YE, Long-huan, Jun-feng WANG, Lin GAO, and Jun YUAN. "Character segmentation method of check under complex background." Journal of Computer Applications 32, no. 11 (May 28, 2013): 3198–200. http://dx.doi.org/10.3724/sp.j.1087.2012.03198.

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21

Sen, Mihir, Juan Jáuregui-Correa, and Carlos López-Cajún. "Foreground and Background Components in Separable Complex Systems." Systems 4, no. 3 (July 19, 2016): 27. http://dx.doi.org/10.3390/systems4030027.

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22

Nie, Ting, Bin He, Guoling Bi, Yu Zhang, and Wensheng Wang. "A Method of Ship Detection under Complex Background." ISPRS International Journal of Geo-Information 6, no. 6 (May 31, 2017): 159. http://dx.doi.org/10.3390/ijgi6060159.

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23

NING Qiang, 宁强, 秦鹏杰 QIN Peng-jie, 石欣 SHI Xin, 李文昌 LI Wen-chang, 廖亮 LIAO Liang, and 朱家庆 ZHU Jia-qing. "Infrared Target Detection Algorithm under Complex Ground Background." ACTA PHOTONICA SINICA 48, no. 4 (2019): 410001. http://dx.doi.org/10.3788/gzxb20194804.0410001.

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24

LI, Hong-bo, Wei WANG, and Yu WU. "Multi-objects colorful segmentation algorithm for complex background." Journal of Computer Applications 29, no. 12 (March 2, 2010): 3322–25. http://dx.doi.org/10.3724/sp.j.1087.2009.03322.

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25

QIU, Wei, and Bin CHEN. "Number location and character segmentation under complex background." Journal of Computer Applications 30, no. 12 (January 6, 2011): 3325–26. http://dx.doi.org/10.3724/sp.j.1087.2010.03325.

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26

Bo, Gu, Hu Hao, Ren Yan, and Liu Xinyu. "Moving Target Detection and Tracking in Complex Background." International Journal of Smart Home 9, no. 9 (September 30, 2015): 95–102. http://dx.doi.org/10.14257/ijsh.2015.9.9.10.

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27

Ma, Yingdong, Yuchen Liu, Shuai Liu, and Zhibin Zhang. "Multiple Object Detection and Tracking in Complex Background." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 02 (January 12, 2017): 1755003. http://dx.doi.org/10.1142/s0218001417550035.

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Multiple object tracking is a fundamental step for many computer vision applications. However, detecting and tracking objects in complex background is still a challenging task. This paper proposes an approach, which combines an improved Gaussian mixture modeling (GMM) with multiple particle filters (MPFs) for automatic multiple targets detecting and tracking. For GMM, we make improvement on GMM in the phase of model updating by using the expectation maximization algorithm and [Formula: see text] recent frames with weight parameters of Gaussian distributions. In the tracking stage, we integrate multiple features of targets, including color, edge and depth, into MPFs to improve the performance of object tracking. By comparing with various particle filter approaches, the experimental results show that our approach can track multiple targets in complex backgrounds automatically and accurately.

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28

He Lifeng, 何立风, 刘艳玲 Liu Yanling, 钟岩 Zhong Yan, and 姚斌 Yao Bin. "Video Foreground Target Extraction Algorithm in Complex Background." Laser & Optoelectronics Progress 57, no. 16 (2020): 161013. http://dx.doi.org/10.3788/lop57.161013.

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29

�� and Maria Kharitonova. "Complex Project Management: Historical Background, Problems and Approaches." Russian Journal of Project Management 3, no. 4 (December 17, 2014): 3–14. http://dx.doi.org/10.12737/6665.

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The project management becomes more and more popular nowadays and is widely used around the world. As the science is only formed, it is obvious that its some areas are well developed, and some other ones lack for attention, contain contradictions, problems, weak places. At the moment a great deal of interesting researches is published in scientific magazines. However, despite it, today there is no structured information on the science�s status and development directions. This work purpose is to allocate the main tendencies of project management�s scientific development. An analysis related to results of researches concerning a question of project management�s scientific development vectors definition covering a time period from 1960 to 2003 has been presented in this paper, as well as a comparative analysis of results with tendencies received from conclusions based on research of scientific publications in the project management area�s leading magazine � ´International Journal of Project Management´ for 2013. Forecasts related to future tendencies of project management�s scientific development had been formulated based on the results of research.

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30

Zhang, Xianlin, Xueming Li, Shuxin Ouyang, and Yang Liu. "Photo-to-Sketch Transformation in a Complex Background." IEEE Access 5 (2017): 8727–35. http://dx.doi.org/10.1109/access.2017.2707394.

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31

Moncrieff, Simon, Svetha Venkatesh, and Geoff West. "Online audio background determination for complex audio environments." ACM Transactions on Multimedia Computing, Communications, and Applications 3, no. 2 (May 2007): 8. http://dx.doi.org/10.1145/1230812.1230814.

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32

Zha, Yufei, Duyan Bi, and Yuan Yang. "Learning complex background by multi-scale discriminative model." Pattern Recognition Letters 30, no. 11 (August 2009): 1003–14. http://dx.doi.org/10.1016/j.patrec.2009.05.005.

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33

Zhao, Yaxin, Li Zhao, and Ya Li. "Multiple Vehicle License Plate Location in Complex Background." International Journal of Advanced Network, Monitoring and Controls 3, no. 1 (2018): 62–68. http://dx.doi.org/10.21307/ijanmc-2018-004.

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34

�� and O. Klimenko. "Complex Project Management: Historical Background, Problems and Approaches." Russian Journal of Project Management 3, no. 3 (September 17, 2014): 4–13. http://dx.doi.org/10.12737/5780.

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This paper opens a new heading of our magazine "Complex Project Management" which is devoted to forming direction of project management. In the paper the complex project management�s actual problems connected with methodological basis development and formalization of knowledge and experience accumulated in this area are considered. The author discovers and specifies a concept of "complexity", defines historical prerequisites of new direction emergence, reveals factors influencing projects� complexity degree, presents complexity elements classification in projects and sets the further purposes for research continuation in this area.

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35

Wang, Yanjiang, and Baozong Yuan. "Segmentation method for face detection in complex background." Electronics Letters 36, no. 3 (2000): 213. http://dx.doi.org/10.1049/el:20000233.

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36

Stergiopoulou, Ekaterini, Kyriakos Sgouropoulos, Nikos Nikolaou, Nikos Papamarkos, and Nikos Mitianoudis. "Real time hand detection in a complex background." Engineering Applications of Artificial Intelligence 35 (October 2014): 54–70. http://dx.doi.org/10.1016/j.engappai.2014.06.006.

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37

Sun, Li, Wenjuan Sheng, and Yiqing Liu. "Background modeling and its evaluation for complex scenes." Multimedia Tools and Applications 74, no. 11 (January 18, 2014): 3947–66. http://dx.doi.org/10.1007/s11042-013-1806-0.

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38

Vogt PR, del Valle LD, and Fimia DR. "COVID-19: The complex background of a pandemic." International Journal of Scholarly Research in Biology and Pharmacy 3, no. 1 (September 30, 2023): 018–22. http://dx.doi.org/10.56781/ijsrbp.2023.3.1.0035.

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The struggle between man and infectious diseases dates back to the very beginning of civilization. It must be taken into account that the increase of infectious entities has not been, nor is it something casual, but rather, consequences derived from the bad actions and management of the human being on the ecosystems. The objective of the present investigation consisted in arguing, in the light of the new pandemic of COVID-19, the complex framework that encloses this pandemic with its various twists and turns and points of view that turn it into a pandemic of paradoxes. It is concluded that the current situation that the planet is living, because of the new coronavirus is one more effect, derived from the bad proceeding of the anthropogenic activity, accumulated during thousands of years, so that undoubtedly through this study, both the scientific community and the society will have a much clearer idea about COVID-19 itself, where we do not know yet if the virus will become endemic, recurrent year after year or finally will be controlled, so it is needed the joint contest of all human beings who inhabit this planet to achieve victory.

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39

Zhang, Guoyang, Xiaofeng Nie, and Zhongqiang Du. "Target polarization characteristics analysis and detection in complex background." Journal of Physics: Conference Series 1914, no. 1 (May 1, 2021): 012001. http://dx.doi.org/10.1088/1742-6596/1914/1/012001.

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40

Zhang, Xin, Xiao Tao Wang, Bing Wang, and Yue Hua Gao. "Research on Moving Human Positioning in Complex Background Video." Advanced Materials Research 225-226 (April 2011): 403–6. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.403.

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Human Skin Color(HSC) features have been widely used in video moving human positioning. However, in complex background video sequences, due to illumination changes or other moving objects which have similar HSC regions, the effect of moving human positioning is not satisfactory. A new method of moving human positioning applied on complex background video sequences is presented in this paper. Firstly, brightness information of the video sequence images is detected and analyzed based on HSV color model. Secondly, adopt the multi frame subtraction method to extract the moving object regions from motionless background. Then, the regions with distinctive HSC features are separated from other moving objects using the data fusion model of HSC and brightness information. Finally, identify human object among regions with HSC features according to the prior knowledge of human. The experimental results show that the method provided in this paper is effective in moving human positioning of complex background video, and has the strong illumination change adaptability and anti-jamming ability.

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41

Zhang, Xin, Thomas Gonnot, and Jafar Saniie. "Real-Time Face Detection and Recognition in Complex Background." Journal of Signal and Information Processing 08, no. 02 (2017): 99–112. http://dx.doi.org/10.4236/jsip.2017.82007.

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42

Zhang, Yan Xi, Yong Jian Liang, Dao Wen Wu, Qiang Wen, and Heng Yuan Yang. "Gesture Acquisition and Tracking with Kinect under Complex Background." Applied Mechanics and Materials 511-512 (February 2014): 541–44. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.541.

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With the rapid development of economy and society, the demand for the man-machine interactive experience is in high enhancement. Hand gesture tracking and recognition is a key technology. This paper studies the obtaining and following about dynamic hand gesture based on Kinect, which is under complex background. Not only has important theoretical significance and tracking, but also it has a broad application prospect. This paper first introduces the human skeleton tracking technology which was based on Kinect. The gesture movement orbit was acquired by tracking the hand gesture and separating the gesture from the complex background so as to lay the foundation for gesture recognition. Software was developed which can recognize human action language, control the game to respond, achieve human-computer interaction more friendly and make the game more fun by the beforehand action.

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43

Юркуц, Ю. Ю., О. М. Поп, М. В. Стець, and В. Т. Маслюк. "Background stabilization and reduction in the gamma-spectrometric complex." Scientific Herald of Uzhhorod University.Series Physics 30 (December 31, 2011): 188–96. http://dx.doi.org/10.24144/2415-8038.2011.30.188-196.

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44

Wang, Xin, and He Pan. "Research on Fast Face Detection Algorithm in Complex Background." Advanced Materials Research 1030-1032 (September 2014): 1779–82. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.1779.

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This paper presents a fast algorithm for face detection in complex background, in which image color information is used first, project upper part of the partition of face in the gray image to horizontal and vertical direction. Determine the eyes positions by the minimum value, and scope the human eye in the face of prior knowledge to judge and adjust the face region.

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45

ZHANG, Long, and Xuezhi YANG. "Video Magnification under the Presence of Complex Background Motions." IEICE Transactions on Information and Systems E104.D, no. 6 (June 1, 2021): 909–14. http://dx.doi.org/10.1587/transinf.2020edl8134.

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46

Gasser, U., and B. Zhou. "Accurate detection of spherical objects in a complex background." Optics Express 29, no. 23 (October 25, 2021): 37048. http://dx.doi.org/10.1364/oe.434652.

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47

ZOU, Cheng-ming, and Wei LI. "Background updating algorithm based on classification in complex scene." Journal of Computer Applications 28, no. 9 (June 5, 2009): 2274–77. http://dx.doi.org/10.3724/sp.j.1087.2008.02274.

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48

Yan, Junhua, Qianqian Xu, He Duan, Yong Yang, and Yongqi Xiao. "Slow Ground Moving Object Detection in Rotating Complex Background." Journal of Imaging Science and Technology 61, no. 2 (March 1, 2017): 205071–2050716. http://dx.doi.org/10.2352/j.imagingsci.technol.2017.61.2.020507.

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49

Borisova, I. V. "Segmentation and tracking of objects on a complex background." Journal of Optical Technology 78, no. 5 (May 31, 2011): 305. http://dx.doi.org/10.1364/jot.78.000305.

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50

Huang, He-xia, Hui-jun Tan, Shu Sun, and Fa-jia Sheng. "Unthrottled Flows with Complex Background Waves in Curved Isolators." AIAA Journal 55, no. 9 (September 2017): 2942–55. http://dx.doi.org/10.2514/1.j055735.

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