Relevant bibliographies by topics / Video synopsis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Video synopsis'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "Video synopsis"

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Li, Teng, Tao Mei, In-So Kweon, and Xian-Sheng Hua. "Multi-video synopsis for video representation." Signal Processing 89, no. 12 (2009): 2354–66. http://dx.doi.org/10.1016/j.sigpro.2009.03.023.

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Zhu, Jianqing, Shengcai Liao, and Stan Z. Li. "Multicamera Joint Video Synopsis." IEEE Transactions on Circuits and Systems for Video Technology 26, no. 6 (2016): 1058–69. http://dx.doi.org/10.1109/tcsvt.2015.2430692.

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Baskurt, Kemal Batuhan, and Refik Samet. "Video synopsis: A survey." Computer Vision and Image Understanding 181 (April 2019): 26–38. http://dx.doi.org/10.1016/j.cviu.2019.02.004.

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Xie, Yujia, Meizhen Wang, Xuejun Liu, and Yiguang Wu. "Surveillance Video Synopsis in GIS." ISPRS International Journal of Geo-Information 6, no. 11 (2017): 333. http://dx.doi.org/10.3390/ijgi6110333.

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Wang, Zong Yue. "Video Synopsis Generation Using GPGPU." Applied Mechanics and Materials 433-435 (October 2013): 297–300. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.297.

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Video summaries provide a compact video representation preserving the essential activities of the original video, but the summaries may be confusing when mixing different activities together. Summaries Clustered methodology, showing similar activities simultaneously, enables to view much easier and more efficiently. However, it is very time consuming in generating summaries, especially in calculating motion distance and collision cost. To improve the efficiency of generating summaries, a parallel video synopsis generation algorithm is proposed based on GPGPU. The experiment result shows genera
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Li, Xuelong, Zhigang Wang, and Xiaoqiang Lu. "Video Synopsis in Complex Situations." IEEE Transactions on Image Processing 27, no. 8 (2018): 3798–812. http://dx.doi.org/10.1109/tip.2018.2823420.

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Pritch, Y., A. Rav-Acha, and S. Peleg. "Nonchronological Video Synopsis and Indexing." IEEE Transactions on Pattern Analysis and Machine Intelligence 30, no. 11 (2008): 1971–84. http://dx.doi.org/10.1109/tpami.2008.29.

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Guo, Yi, Su-Rong Qi-Mu, Wei Jin, and Xing Wei. "Object-based video synopsis and multi-video body retrieval." Signal Processing Research 5 (2016): 7. http://dx.doi.org/10.14355/spr.2016.05.002.

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He, Yi, Changxin Gao, Nong Sang, Zhiguo Qu, and Jun Han. "Graph coloring based surveillance video synopsis." Neurocomputing 225 (February 2017): 64–79. http://dx.doi.org/10.1016/j.neucom.2016.11.011.

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Fu, Wei, Jinqiao Wang, Liangke Gui, Hanqing Lu, and Songde Ma. "Online video synopsis of structured motion." Neurocomputing 135 (July 2014): 155–62. http://dx.doi.org/10.1016/j.neucom.2013.12.041.

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Dissertations / Theses on the topic "Video synopsis"

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Guilluy, Wilko. "Video stabilization : A synopsis of current challenges, methods and performance evaluation." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCD094.

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Le développement continu de capteurs vidéo et leurs miniaturisations ont étendus leurs usages dans diverses applications allant de la vidéo-surveillance aux systèmes de chirurgie assisté par ordinateur et l’analyse de mouvements physiques et de phénomènes astrophysiques. De nos jours, il est devenu possible de capturer des séquences vidéos dans n’importe quel environnement, sans de lourds et complexes ajustements comme c’était le cas avec les anciens capteurs vidéos. Cependant,l’aisance à accéder à l’information visuelle à travers des capteurs de plus en plus faciles à manipuler a conduit à un
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Guo, Jing-Ming, Cheng-Hsin Chang, Hua Lee, and Vincent Radzicki. "EFFICIENT ARTIFACT-FREE VIDEO SYNOPSIS FOR TARGET DETECTION AND TRACKING." International Foundation for Telemetering, 2017. http://hdl.handle.net/10150/626981.

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Target detection and tracking is one of the most common applications of video systems. The requirement of processing time due to the computation complexity has been the most challenging element for these operations. To achieve computation efficiency, video synopsis is one of the most promising approaches. Video synopsis is a technique that focuses on the removal or reduction of spatial and temporal redundancies, while maintaining important activities in the original video. In this paper, a new video synopsis scheme is introduced. Major characteristics of this technique include efficient backgr
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Rodriguez, Mikel. "SPATIO-TEMPORAL MAXIMUM AVERAGE CORRELATION HEIGHT TEMPLATES IN ACTION RECOGNITION AND VIDEO SUMMARIZATION." Doctoral diss., University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2215.

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Action recognition represents one of the most difficult problems in computer vision given that it embodies the combination of several uncertain attributes, such as the subtle variability associated with individual human behavior and the challenges that come with viewpoint variations, scale changes and different temporal extents. Nevertheless, action recognition solutions are critical in a great number of domains, such video surveillance, assisted living environments, video search, interfaces, and virtual reality. In this dissertation, we investigate template-based action recognition algorithms
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Chang, Cheng-hsin, and 張正欣. "Efficient Artifact-Free Video Synopsis." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/25282458946151527636.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>102<br>Video synopsis is utilized to provide a condensed video without spatial and temporal redundancies, while maintaining all the activities in the original video. Existing video synopsis methods presume that the object tubes (three dimension space time volume) are perfectly retrieved from the original video, yet it is not the case in practical applications due to the object detection/tracking failure or object collision. Another problem is the energy minimization based method often suffers from heavily computational cost, which thus prolongs the processing time. I
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Chiang, Tzu-Hsuan, and 蔣子萱. "The Coherent Event Based Synopsis of Surveillance Video." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/09145145843953925651.

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碩士<br>國立交通大學<br>資訊科學與工程研究所<br>102<br>In order to monitor all kinds of events in daily life, surveillance cameras are widely used nowadays. Millions of surveillance records present challenges to their browsing and retrieving. Previous approaches are proposed to address this problem by eliminating the inherent spatiotemporal redundancy as much as possible. However, maximizing the visual information in the synopsis frames may distract the visual attention of viewers. While simultaneously showing several events on one screen, users would make more mistakes or perform their tasks more slowly. There
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Lin, Chin-Hsien, and 林晉賢. "Coherent Event-based Surveillance Video Synopsis Using Trajectory Clustering." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/53260788368309973029.

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碩士<br>國立交通大學<br>資訊科學與工程研究所<br>103<br>To find out specific persons or objects from long surveillance videos is very labor-intensive and time-consuming. For efficiently browsing surveillance videos, video synopsis is developed to alleviate the inherent spatiotemporal redundancy by stitching objects in frames without considering time order. However, too much information in a synopsis frame may distract users’ attention. Therefore, we propose a novel surveillance video synopsis system using coherent event trajectory clustering containing the following advantages: (1) synopsis videos of similar eve
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Hsing-ChengChen and 陳行正. "Surveillance Video Synopsis Using Illumination Invariant Foreground Extraction and Volumetric Cut." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/86201366703190729945.

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Hsieh, Chen-Wei, and 謝禎蔚. "Based on 3D Trajectory Projection Object Grouping and Classification for Video Synopsis." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/3g58j6.

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碩士<br>國立臺灣科技大學<br>電子工程系<br>103<br>Nowadays, the video surveillance systems have become popular, and are deployed in every corner of our environment, such as in the school, airport, street, etc. It generates huge data consistantly. Thus, browsing and searching for a specific event from this database becomes challenging. To reduce the efforts by manually searching targets, the video synopsis technique was proposed to efficiently capture the dynamic behavior of a specific object with limited time period. The video synopsis method provides a condensed video by removing the spatial or temporal redu
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Lin, Yu-Da, and 林郁達. "Online Video Synopsis: Object Detection and Management Based on Deep Learning and Minimum Collision Trajectory." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/07958314078990257750.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>105<br>Video synopsis is a feasible solution to expedite browsing in raw surveillance data and to perform various video analytics tasks. The technique provides a condensed video with reduced spatial or temporal redundancies, without losing the actual content of the source video. However, conventional methods are computationally intensive and time-consuming and also with blinking effect in the resultant video. To overcome these problems, we propose a trajectory-based video synopsis system which can achieve high-performance without object tracking and energy optimizati
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"Synopsis of video streams and its application to computer aided diagnosis for GI tract abnormalities based on wireless capsule endoscopy (CE) video." 2012. http://library.cuhk.edu.hk/record=b5549629.

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無線膠囊內窺鏡（CE）是一種用於檢查整個胃腸道，尤其是小腸的無創技術。它極大地改善了許多小腸疾病的診斷和管理方式，如不明原因的消化道出血，克羅恩病，小腸腫瘤，息肉綜合征等。儘管膠囊內窺鏡有很好的臨床表現，但它仍然有一定的局限性。主要問題是每次檢查產生約50,000 幅低質量的圖像，對於醫生來說，評估如此大量的圖像是一項非常耗時、耗力的工作。<br>到目前為止，對於膠囊內窺鏡的分析和評估，學者們都把膠囊內窺鏡圖像視為單獨的，獨立的觀測對象。事實並非如此，因為圖像之間往往有顯著的重疊。特別是當膠囊內窺鏡在被小腸蠕動緩緩推動時，它可以捕捉同一病灶的多個視圖。我們的研究目的是使用所有可用的資訊，包括多幅圖像，研究對於膠囊內窺鏡的電腦輔助診斷（CAD）系統。<br>在這篇論文中，我們提出了一個嵌入分類器的多類隱馬爾可夫模型（HMM）的方案，它可以融合多幅相鄰圖像的時間資訊。由於膠囊內窺鏡圖像的品質比較低，我們首先進行預處理，以加強膠囊內窺鏡圖像，增加其對比度，消除噪聲。我們調查研究了多種圖像增強的方法，並調整了它們的參數使其適用於膠囊內窺鏡圖像。<br>對於基於單幅圖像的有監督的分類，AdaBoost 作為一個集成分類器來融合多個分類器，即本論文中的支持向量機（SVM），k-近鄰（k-NN），貝葉斯分類。在分類之前，我們提取和融合了顏色，邊緣和紋理特徵。<br>對於無線膠囊內窺鏡的視頻摘要，
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Books on the topic "Video synopsis"

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Turim, Maureen, and Michael Walsh. Sound Events. Edited by John Richardson, Claudia Gorbman, and Carol Vernallis. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199733866.013.0026.

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This article appears in theOxford Handbook of New Audiovisual Aestheticsedited by John Richardson, Claudia Gorbman, and Carol Vernallis. This chapter is a comprehensive survey of sound practices in avant-garde film, video art, and installation art since the 1960s. It addresses a series of artistic approaches to sound: silence, tone and drone, antic and aleatory, multilayering and cacophony, work with voices, legacies of cinematic exhibition, and resonant spaces in galleries and museums. It is broadly chronological, beginning with major figures of the 1960s and ending with artists currently wor
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Book chapters on the topic "Video synopsis"

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Mahapatra, Ansuman, and Pankaj K. Sa. "Video Synopsis: A Systematic Review." In Studies in Computational Intelligence. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6844-2_8.

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Thirumalaiah, G., and S. Immanuel Alex Pandian. "Dynamic Object Indexing Technique for Distortionless Video Synopsis." In Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00665-5_85.

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Kumar, Nikhil, Ashish Kumar, and Neeta Kandpal. "Video Synopsis for IR Imagery Considering Video as a 3D Data Cuboid." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2104-6_21.

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Panagiotakis, Costas, Nelly Ovsepian, and Elena Michael. "Video Synopsis Based on a Sequential Distortion Minimization Method." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40261-6_11.

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Ye, Gensheng, Wenjuan Liao, Jichao Dong, Dingheng Zeng, and Huicai Zhong. "A Surveillance Video Index and Browsing System Based on Object Flags and Video Synopsis." In MultiMedia Modeling. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14442-9_36.

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Badal, Tapas, Neeta Nain, and Mushtaq Ahmed. "Surveillance Video Synopsis While Preserving Object Motion Structure and Interaction." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2107-7_18.

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Thirumalaiah, G., S. Immanuel Alex Pandian, D. Teja Sri, M. Karthik Chowdary, and A. Kumarteja. "An Optimized Clustered Based Video Synopsis by Using Artificial Intelligence." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7961-5_55.

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Jagtap, Swati, and Nilkanth B. Chopade. "A Comprehensive Investigation About Video Synopsis Methodology and Research Challenges." In Inventive Computation and Information Technologies. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4305-4_66.

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Cai, Dongqi, Fei Su, and Zhicheng Zhao. "Adaptive Synopsis of Non-Human Primates’ Surveillance Video Based on Behavior Classification." In MultiMedia Modeling. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27671-7_60.

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Yogameena, B., and R. Janani. "Abnormal Activity-Based Video Synopsis by Seam Carving for ATM Surveillance Applications." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5148-2_40.

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Conference papers on the topic "Video synopsis"

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Choudhary, Vikas, and Anil K. Tiwari. "Surveillance Video Synopsis." In Image Processing (ICVGIP). IEEE, 2008. http://dx.doi.org/10.1109/icvgip.2008.84.

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Li, Teng, Tao Mei, In-So Kweon, and Xian-Sheng Hua. "Video^M: Multi-video Synopsis." In 2008 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2008. http://dx.doi.org/10.1109/icdmw.2008.84.

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Huang, Chun-Rong, Hsing-Cheng Chen, and Pau-Choo Chung. "Online surveillance video synopsis." In 2012 IEEE International Symposium on Circuits and Systems - ISCAS 2012. IEEE, 2012. http://dx.doi.org/10.1109/iscas.2012.6271627.

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Tong Yao, Maosen Xiao, Caiwen Ma, Chao Shen, and Peng Li. "Object based video synopsis." In 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA). IEEE, 2014. http://dx.doi.org/10.1109/wartia.2014.6976479.

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Yildiz, Alparslan, Adem Ozgur, and Yusuf Sinan Akgul. "Fast non-linear video synopsis." In 2008 23rd International Symposium on Computer and Information Sciences (ISCIS). IEEE, 2008. http://dx.doi.org/10.1109/iscis.2008.4717951.

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Pritch, Yael, Sarit Ratovitch, Avishai Hendel, and Shmuel Peleg. "Clustered Synopsis of Surveillance Video." In 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2009. http://dx.doi.org/10.1109/avss.2009.53.

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Lu, Minlong, Yueming Wang, and Gang Pan. "Generating fluent tubes in video synopsis." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638063.

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Hoshen, Yedid, and Shmuel Peleg. "Live video synopsis for multiple cameras." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7350790.

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Mahapatra, Ansuman, Pankaj K. Sa, and Banshidhar Majhi. "A multi-view video synopsis framework." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351002.

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Kakodra, Shweta S., C. Sujatha, and Padmashree Desai. "Query-By-Object Based Video Synopsis." In 2021 International Conference on Intelligent Technologies (CONIT). IEEE, 2021. http://dx.doi.org/10.1109/conit51480.2021.9498311.

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