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Relevant bibliographies by topics / Video processing

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Academic literature on the topic 'Video processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Video processing"

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Jiang, Zhiying, Chong Guan, and Ivo L. de Haaij. "Congruity and processing fluency." Asia Pacific Journal of Marketing and Logistics 32, no. 5 (2019): 1070–88. http://dx.doi.org/10.1108/apjml-03-2019-0128.

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Purpose The purpose of this paper is to investigate the benefits of Ad-Video and Product-Video congruity for embedded online video advertising. A conceptual model is constructed to test how congruity between online advertisements, advertised products and online videos impact consumer post-viewing attitudes via processing fluency. Design/methodology/approach An online experiment with eight versions of mock video sections (with embedded online video advertisements) was conducted. The study is a 2 (type of appeal: informational vs emotional) × 2 (Ad-Video congruity: congruent vs incongruent) × 2

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Coutts, Maurice D., and Dennis L. Matthies. "Video disc processing." Journal of the Acoustical Society of America 81, no. 5 (1987): 1659. http://dx.doi.org/10.1121/1.395033.

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Uytterhoeven, G. "Digital video processing." Journal of Computational and Applied Mathematics 66, no. 1-2 (1996): N5—N6. http://dx.doi.org/10.1016/0377-0427(96)80474-2.

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Ritwik Baranwal. "Automatic Summarization of Cricket Highlights using Audio Processing." January 2021 7, no. 01 (2021): 48–53. http://dx.doi.org/10.46501/ijmtst070111.

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The problem of automatic excitement detection in cricket videos is considered and applied for highlight generation. This paper focuses on detecting exciting events in video using complementary information from the audio and video domains. First, a method of audio and video elements separation is proposed. Thereafter, the “level-of-excitement” is measured using features such as amplitude, and spectral center of gravity extracted from the commentators speech’s amplitude to decide the threshold. Our experiments using actual cricket videos show that these features are well correlated with human as

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Wira Widjanarko, Kresna, Krisna Aditya Herlambang, and Muhamad Abdul Karim. "Faster Video Processing Menggunakan Teknik Parallel Processing Dengan Library OpenCV." Jurnal Komunikasi, Sains dan Teknologi 1, no. 1 (2022): 10–18. http://dx.doi.org/10.61098/jkst.v1i1.2.

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Video yang diputar seringkali terlalu lama diproses, terutama aplikasi yang membutuhkan pemrosesan real-time, seperti pemrosesan aplikasi video webcam, oleh karena itu dilakukan pemrosesan paralel untuk mempercepat proses komputasi video. Penelitian ini membahas tentang pemrosesan paralel pada video agar proses komputasi video berjalan lebih cepat dibandingkan tanpa pemrosesan paralel. Pengujian dilakukan dengan dua jenis data: aliran video dari webcam laptop dan file video .mp4. Bahasa pemrograman yang digunakan dalam pengujian ini adalah Python dengan bantuan library OpenCV. Penelitian ini m

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Et. al., G. Megala,. "State-Of-The-Art In Video Processing: Compression, Optimization And Retrieval." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (2021): 1256–72. http://dx.doi.org/10.17762/turcomat.v12i5.1793.

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Video compression plays a vital role in the modern social media networking with plethora of multimedia applications. It empowers transmission medium to competently transfer videos and enable resources to store the video efficiently. Nowadays high-resolution video data are transferred through the communication channel having high bit rate in order to send multiple compressed videos. There are many advances in transmission ability, efficient storage ways of these compressed video where compression is the primary task involved in multimedia services. This paper summarizes the compression standard

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Лун, Сюй, Xu Long, Йан Йихуа, Yan Yihua, Чэн Цзюнь, and Cheng Jun. "Guided filtering for solar image/video processing." Solar-Terrestrial Physics 3, no. 2 (2017): 9–15. http://dx.doi.org/10.12737/stp-3220172.

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A new image enhancement algorithm employing guided filtering is proposed in this work for enhancement of solar images and videos, so that users can easily figure out important fine structures imbedded in the recorded images/movies for solar observation. The proposed algorithm can efficiently remove image noises, including Gaussian and impulse noises. Meanwhile, it can further highlight fibrous structures on/beyond the solar disk. These fibrous structures can clearly demonstrate the progress of solar flare, prominence coronal mass emission, magnetic field, and so on. The experimental results pr

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P. Hima Chandana, R. Ragupathy, and D. Vivekananda Reddy. "Video Summarization Using Machine Learning Techniques: An Overview." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 05 (2025): 2080–86. https://doi.org/10.47392/irjaem.2025.0326.

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Technological advancement is a persistent aspect in our lives, and it emerges and grows at an exponential rate. Every day, a large quantity of data is generated in the form of text, image, audio and video. To process large amounts of video data, like movies, social media data and Surveillance data, requires a large amount of storage. Therefore, minimizing these videos by processing only vital content, takes a long processing time. To extract the key content from the video, which is a time-consuming procedure for the viewer, the entire video must be watched to overcome such challenges, video su

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Moon, Nazmun Nessa, Imrus Salehin, Masuma Parvin, et al. "Natural language processing based advanced method of unnecessary video detection." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5411. http://dx.doi.org/10.11591/ijece.v11i6.pp5411-5419.

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<span>In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze

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Nazmun, Nessa Moon, Salehin Imrus, Parvin Masuma, et al. "Natural language processing based advanced method of unnecessary video detection." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5411–19. https://doi.org/10.11591/ijece.v11i6.pp5411-5419.

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In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze and prepare

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Dissertations / Theses on the topic "Video processing"

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Aggoun, Amar. "DPCM video signal/image processing." Thesis, University of Nottingham, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.335792.

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Zhao, Jin. "Video/Image Processing on FPGA." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/503.

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Video/Image processing is a fundamental issue in computer science. It is widely used for a broad range of applications, such as weather prediction, computerized tomography (CT), artificial intelligence (AI), and etc. Video-based advanced driver assistance system (ADAS) attracts great attention in recent years, which aims at helping drivers to become more concentrated when driving and giving proper warnings if any danger is insight. Typical ADAS includes lane departure warning, traffic sign detection, pedestrian detection, and etc. Both basic and advanced video/image processing technologies are

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Isaieva, O. A., and О. Г. Аврунін. "Image processing for video dermatoscopy." Thesis, Osaka, Japan, 2019. http://openarchive.nure.ua/handle/document/10347.

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Chen, Juan. "Content-based Digital Video Processing. Digital Videos Segmentation, Retrieval and Interpretation." Thesis, University of Bradford, 2009. http://hdl.handle.net/10454/4256.

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Recent research approaches in semantics based video content analysis require shot boundary detection as the first step to divide video sequences into sections. Furthermore, with the advances in networking and computing capability, efficient retrieval of multimedia data has become an important issue. Content-based retrieval technologies have been widely implemented to protect intellectual property rights (IPR). In addition, automatic recognition of highlights from videos is a fundamental and challenging problem for content-based indexing and retrieval applications. In this thesis, a paradigm is

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Haynes, Simon Dominic. "Reconfigurable architectures for video image processing." Thesis, Imperial College London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322797.

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Fernando, Warnakulasuriya Anil Chandana. "Video processing in the compressed domain." Thesis, University of Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326724.

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Leonce, Andrew. "HDR video enhancement, processing and coding." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/19639.

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Advances in digital camera technology have led to the development of image sensors that are capable of capturing High Dynamic Range (HDR) images. Although this has enabled the capture of greater depths of colour and illumination, there remain problems with regards to transmitting and displaying the HDR image data. Current consumer level displays are designed to only show images with a depth of 8-bits per pixel per channel. Typical HDR images can be 10-bits per pixel per channel and upwards, leading to the first problem, how to display HDR images on Standard Dynamic Range (SDR) displays. This i

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Wu, Hao-Yu M. Eng Massachusetts Institute of Technology. "Eulerian Video Processing and medical applications." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/77452.

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Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 68-69).<br>Our goal is to reveal subtle yet informative signals in videos that are difficult or impossible to see with the naked eye. We can either display them in an indicative manner, or analyse them to extract important measurements, such as vital signs. Our method, which we call Eulerian Video Processing, takes a standard video sequence as input, and applies spatial decomposition, fo

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Tsoligkas, Nick A. "Video/Image Processing Algorithms for Video Compression and Image Stabilization Applications." Thesis, Teesside University, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.517469.

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As the use of video becomes increasingly popular and wide spread in the areas of broadcast services, internet, entertainment and security-related applications, providing means for fast. automated, and effective techniques to represent video based on its content, such as objects and meanings, is important topic of research. In many applications.. removing the hand shaking effect and making video images stable and clear or decomposing (and then transmitting) the video content into a collection of meaningful objects is a necessity. Therefore automatic techniques for video stabilization, extractio

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Tsoi, Yau Chat. "Video cosmetics : digital removal of blemishes from video /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20TSOI.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 83-86). Also available in electronic version. Access restricted to campus users.

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Books on the topic "Video processing"

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Cremers, Daniel, Marcus Magnor, Martin R. Oswald, and Lihi Zelnik-Manor, eds. Video Processing and Computational Video. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24870-2.

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Shivakumara, Palaiahnakote, and Umapada Pal. Cognitively Inspired Video Text Processing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7069-5.

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Sicuranza, Giovanni L., and Sanjit K. Mitra, eds. Multidimensional Processing of Video Signals. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3616-1.

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Pereira, Rafael Silva, and Karin K. Breitman. Video Processing in the Cloud. Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2137-4.

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Comaniciu, Dorin, Rudolf Mester, Kenichi Kanatani, and David Suter, eds. Statistical Methods in Video Processing. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b104157.

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Sicuranza, Giovanni L., and Sanjit Kumar Mitra. Multidimensional processing of video signals. Springer, 1992.

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Ling, Guan, Kung S. Y, and Larsen Jan, eds. Multimedia image and video processing. CRC Press, 2000.

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L, Sicuranza Giovanni, and Mitra Sanjit Kumar, eds. Multidimensional processing of video signals. Kluwer Academic, 1992.

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Takao, Nishitani, Ang Peng H, and Catthoor Francky, eds. VLSI video/image signal processing. Kluwer Academic Publishers, 1993.

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Breitman, K. K. (Karin K.) and SpringerLink (Online service), eds. Video Processing in the Cloud. Rafael Silva Pereira, 2011.

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Book chapters on the topic "Video processing"

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Pajankar, Ashwin. "Video Processing." In Raspberry Pi Image Processing Programming. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8270-0_10.

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Parekh, Ranjan. "Video Processing." In Fundamentals of IMAGE, AUDIO, and VIDEO PROCESSING Using MATLAB®. CRC Press, 2021. http://dx.doi.org/10.1201/9781003019718-3.

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Inoué, Shinya, and Kenneth R. Spring. "Digital Image Processing." In Video Microscopy. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-5859-0_12.

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Schu, Markus. "Video Processing Tasks." In Handbook of Visual Display Technology. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-14346-0_42.

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Schu, Markus. "Video Processing Tasks." In Handbook of Visual Display Technology. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-35947-7_42-2.

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Schu, Markus. "Video Processing Tasks." In Handbook of Visual Display Technology. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-540-79567-4_42.

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Müller, Karsten, Heiko Schwarz, Peter Eisert, and Thomas Wiegand. "Video Data Processing." In Digital Transformation. Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-58134-6_4.

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Huang, Yung-Lin. "Video Signal Processing." In Advances in Multirate Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59274-9_6.

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Zhang, Yu-Jin. "Video Image Processing." In Handbook of Image Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-5873-3_21.

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Walter, Robert J., and Michael W. Berns. "Digital Image Processing and Analysis." In Video Microscopy. Springer US, 1986. http://dx.doi.org/10.1007/978-1-4757-6925-8_10.

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Conference papers on the topic "Video processing"

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Homoud, Amani, Saptarshi Das, and Stuart Townley. "Challenges in Underwater Video Data Processing." In 2025 Eighth International Women in Data Science Conference at Prince Sultan University (WiDS PSU). IEEE, 2025. https://doi.org/10.1109/wids-psu64963.2025.00034.

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Jensen, Bob. "Processing techniques for technical video production." In OSA Annual Meeting. Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.tha1.

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This presentation will focus on how a technical video production is assembled. Participants will discover the advantages and secrets of producing good technical videos. Various productions from the Maui Space Surveillance Site will be used to demonstrate how many separate elements are combined to achieve videos that meet complex technical objectives. Discussion of key production elements will include establishing objectives, choosing production values for a particular audience, script and storyboard writing, pre- production work, and techniques for production/post-production. Participants will

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Pinto, Carlos, Aleksandar Beric, Satendra Singh, and Sachin Farfade. "HiveFlex-Video VSP1: Video Signal Processing Architecture for Video Coding and Post-Processing." In Eighth IEEE International Symposium on Multimedia (ISM'06). IEEE, 2006. http://dx.doi.org/10.1109/ism.2006.83.

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Hannan, Tanveer, Rajat Koner, Jonathan Kobold, and Matthias Schubert. "Box Supervised Video Segmentation Proposal Network." In 24th Irish Machine Vision and Image Processing Conference. Irish Pattern Recognition and Classification Society, 2022. http://dx.doi.org/10.56541/azwk8552.

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Bounding box supervision provides a balanced compromise between labeling effort and result quality for image segmentation. However, there exists no such work explicitly tailored for videos. Applying the image segmentation methods directly to videos produces sub-optimal solutions because they do not exploit the temporal information. In this work, we propose a box-supervised video segmentation proposal network. We take advantage of intrinsic video properties by introducing a novel box-guided motion calculation pipeline and a motion-aware affinity loss. As the motion is utilized only during train

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"Image and Video Processing." In 2020 International Conference on Smart Systems and Technologies (SST). IEEE, 2020. http://dx.doi.org/10.1109/sst49455.2020.9264108.

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"Image and video processing." In 2010 2nd International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2010. http://dx.doi.org/10.1109/ipta.2010.5586833.

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"Session WA7b: Video processing." In 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017. http://dx.doi.org/10.1109/acssc.2017.8335724.

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"Session TP8b2: Video processing." In 2015 49th Asilomar Conference on Signals, Systems and Computers. IEEE, 2015. http://dx.doi.org/10.1109/acssc.2015.7421369.

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"Image and Video Processing." In 2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2022. http://dx.doi.org/10.1109/iwssip55020.2022.9854455.

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"Image and Video Processing." In 2019 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2019. http://dx.doi.org/10.1109/iwssip.2019.8787278.

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Reports on the topic "Video processing"

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Roach, A. B. WebRTC Video Processing and Codec Requirements. RFC Editor, 2016. http://dx.doi.org/10.17487/rfc7742.

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Mitchell, Owen R., and Dominick Andrisani. Video Processing for Target Extraction, Recognition, and Tracking. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada232403.

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Fenimore, Charles, and Bruce F. Field. Video processing with the Princeton engine at NIST. National Bureau of Standards, 1991. http://dx.doi.org/10.6028/nist.tn.1288.

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Senecal, J., and A. Wegner. Extension of 4-8 Texture Hierarchies to Large Video Processing and Visualization. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/924013.

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Anastasiadis, S. H., J. K. Chen, J. T. Koberstein, A. F. Siegel, and J. E. Sohn. The Determination of Interfacial Tension by Video Image Processing of Pendant Fluid Drops. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada165745.

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Wolf, Stephen. A Full Reference (FR) Method Using Causality Processing for Estimating Variable Video Delays. Institute for Telecommunication Sciences, 2009. https://doi.org/10.70220/722c4grl.

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Lischer-Katz, Zack, Rashida Braggs, and Bryan Carter. Investigating Volumetric Video Creation and Curation for the Digital Humanities: a White Paper Describing Findings from the Project: Preserving BIPOC Expatriates’ Memories During Wartime and Beyond. The University of Arizona Libraries, 2024. http://dx.doi.org/10.2458/10150.674673.

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Volumetric video capture technologies offer humanities scholars and other researchers new, immersive ways of engaging with historical and cultural knowledge for research and pedagogical purposes; however, the high cost of this technology and a paucity of expert knowledge in the field have limited its adoption. In particular, volumetric video offers rich new possibilities for recording, preserving, and re-experiencing BIPOC (Black, indigenous, and other people of color) stories in immersive detail, which have been underrepresented in the historical record. This technology is still experimental

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Carter, R. J. Modification and Validation of an Automotive Data Processing Unit, Compessed Video System, and Communications Equipment. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/2734.

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Qi, Yuan. Learning Algorithms for Audio and Video Processing: Independent Component Analysis and Support Vector Machine Based Approaches. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada458739.

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Selvaraju, Ragul, SHABARIRAJ SIDDESWARAN, and Hariharan Sankarasubramanian. The Validation of Auto Rickshaw Model for Frontal Crash Studies Using Video Capture Data. SAE International, 2020. http://dx.doi.org/10.4271/2020-28-0490.

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Despite being Auto rickshaws are the most important public transportation around Asian countries and especially in India, the safety standards and regulations have not been established as much as for the car segment. The Crash simulations have evolved to analyze the vehicle crashworthiness since crash experimentations are costly. The work intends to provide the validation for an Auto rickshaw model by comparing frontal crash simulation with a random head-on crash video. MATLAB video processing tool has been used to process the crash video, and the impact velocity of the frontal crash is obtain

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