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Relevant bibliographies by topics / Digital video – Data processing

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Academic literature on the topic 'Digital video – Data processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 28 January 2023

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Journal articles on the topic "Digital video – Data processing"

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Soedarso, Nick. "Mengolah Data Video Analog Menjadi Video Digital Sederhana." Humaniora 1, no. 2 (October 31, 2010): 569. http://dx.doi.org/10.21512/humaniora.v1i2.2897.

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Nowadays, editing technology has entered the digital age. Technology will demonstrate the evidence of processing analog to digital data has become simpler since editing technology has been integrated in the society in all aspects. Understanding the technique of processing analog to digital data is important in producing a video. To utilize this technology, the introduction of equipments is fundamental to understand the features. The next phase is the capturing process that supports the preparation in editing process from scene to scene; therefore, it will become a watchable video.

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Wang, Lei. "Sports Feedback System Based on Video Digital Processing." Computational and Mathematical Methods in Medicine 2022 (June 16, 2022): 1–10. http://dx.doi.org/10.1155/2022/2859567.

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The current sports feedback system has high application value and is widely used in physical education courses, but it has problems such as large feedback error, long response time, and incomplete data, leading to poor effect of sports feedback system. In order to improve the effectiveness of sports feedback system, a sports feedback system based on video digital processing is designed. By constructing the overall architecture of video digital sports feedback system, the positioning module, interface module, power supply circuit module, sampling module, input module, signal extraction module, and video digital processing module are designed. Using information communication mechanism, TCP/IP network protocol, serial communication, and shared memory to improve video digital processing, experimental results show that the designed system has good error convergence of 0.1, short response time of 32 s, and high data integrity of 94%, which has strong practicability.

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Lobanov, Dmitriy, and Elena Strungar. "Mathematical data processing according to digital image correlation method for polymer composites." Frattura ed Integrità Strutturale 14, no. 54 (September 23, 2020): 56–65. http://dx.doi.org/10.3221/igf-esis.54.04.

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The paper analyses the numerical algorithms for experimental data processing using a contactless video system Vic-3D, designed for three-dimension analysis of displacement and strain fields, and digital image correlation method. The authors considered methodological issues of conducting an experiment using a video system. They suggested recommendations on the choice of parameters of calculation of correlation, the size of subset and step during the analysis of non-homogeneous displacement and strain fields in polymer composite materials through laminated fiberglass composite. The efficient parameters of mathematical data processing are identified according to digital image correlation method on the basis of building fields for one frame on the surface of laminated fiberglass reinforced plastic at various subset values and at fixed step value. The paper shows the impact of step value on the strain fields detail degree. The authors have identified the relation of the chosen parameters of experimental data processing using digital image correlation method with the scaled levels of consideration of composite materials strain processes. To evaluate the strains at various scale levels, the paper uses supplementary video system instruments: “virtual extensometer”, “rectangular area” and “line”. The authors obtained a longitudinal strain profile that allows evaluating the location of strain peak areas on the composite object surface.

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Chervyakov, N. I., P. A. Lyakhov, and A. R. Orazaev. "3D-generalization of impulse noise removal method for video data processing." Computer Optics 44, no. 1 (February 2020): 92–100. http://dx.doi.org/10.18287/2412-6179-co-577.

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The paper proposes a generalized method of adaptive median impulse noise filtering for video data processing. The method is based on the combined use of iterative processing and transformation of the result of median filtering based on the Lorentz distribution. Four different combinations of algorithmic blocks of the method are proposed. The experimental part of the paper presents the results of comparing the quality of the proposed method with known analogues. Video distorted by impulse noise with pixel distortion probabilities from 1% to 99% inclusive was used for the simulation. Numerical assessment of the quality of cleaning video data from noise based on the mean square error (MSE) and structural similarity (SSIM) showed that the proposed method shows the best result of processing in all the considered cases, compared with the known approaches. The results obtained in the paper can be used in practical applications of digital video processing, for example, in systems of video surveillance, identification systems and control of industrial processes.

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Zarmehi, Nematollah, and Mohammad Ali Akhaee. "Digital video steganalysis toward spread spectrum data hiding." IET Image Processing 10, no. 1 (January 1, 2016): 1–8. http://dx.doi.org/10.1049/iet-ipr.2014.1019.

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Pratitasari, Roesnia Dwi, and Abdul Gafur. "Pengembangan video digital pengalengan buah dan sayur untuk mata kuliah teknologi pengolahan hasil pertanian." Jurnal Inovasi Teknologi Pendidikan 5, no. 1 (October 5, 2018): 48–60. http://dx.doi.org/10.21831/jitp.v5i1.14526.

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Penelitian ini bertujuan: (1) menghasilkan video digital pengalengan buah dan sayur untuk mata kuliah teknologi pengolahan hasil pertanian, (2) mengetahui kelayakan video digital pengalengan buah dan sayur pada mata kuliah teknologi pengolahan hasil pertanian di Akademi Komunitas Negeri Temanggung, dan (3) mengetahui keefektifan video digital pengalengan buah dan sayur pada mata kuliah teknologi pengolahan hasil pertanian di Akademi Komunitas Negeri Temanggung. Penelitian pengembangan ini mengacu pada langkah yang dikembangkan oleh Borg and Gall. Desain pengembangan dikelompokkan menjadi tiga prosedur pengembangan, yang meliputi: (a) analisis kebutuhan, (b) pengembangan produk, dan (c) validasi dan revisi. Teknik pengumpulan data menggunakan kuesioner dan tes. Data yang diperoleh dianalisis secara deskriptif kuantitatif. Efektifitas produk dianalisis menggunakan rumus N-gain (hasil belajar yang dinormalisasi). Hasil penelitian menunjukkan (1) produk yang dihasilkan adalah video digital pengalengan buah dan sayur, (2) produk yang dikembangkan layak digunakan sebagai salah satu sumber belajar dilihat dari aspek media dan aspek materi, dan (3) efektifitas pengembangan produk termasuk kategori tinggi.Kata kunci: video digital, pengalengan buah dan sayur DEVELOPING DIGITAL VIDEO OF CANNING FRUITS AND VEGETABLES FOR AGRICULTURAL PRODUCTS PROCESSING TECHNOLOGY COURSEAbstractThis research aims to: (1) produce digital video canning of fruits and vegetables for the course of technology processing of agricultural products, (2) determine the feasibility of digital video of canning fruits and vegetables in the subject of technological processing of agricultural products at the Academy of Community Affairs Temanggung, and ( 3) determine the effectiveness of digital video of canning fruits and vegetables in the subject of agricultural processing technology in Temanggung State Community college. The study is a research and development (R & D) adapted from Borg and Gall model that consist of analysis of needs, product development, and the validation and revision. The data were analyzed by descriptive qualitative. The effectiveness of the product was analyzed using the formula N-gain (normalized learning outcomes). The results of the research show that: (1) the resulted product is a digital video of canning fruits and vegetables, (2) the developed product is suitable to use as a learning resource of learning from the aspect of media and material aspects, and (3) the effectiveness of developed product is high.Keywords: digital video, fruit and vegetable canning

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Xu, Yuanjin, and Xiaojun Liu. "Interactive Algorithms in Complex Image Processing Systems Based on Big Data." Complexity 2020 (May 5, 2020): 1–9. http://dx.doi.org/10.1155/2020/5929584.

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In the era of big data, images and videos are one of the main means of information dissemination. In recent years, research on the problem of image and video reorganization and integration has become a hot topic in digital image processing technology. Using a computer for image processing, complicated programming is unavoidable. Therefore, it is necessary to optimize the interactive algorithms for image processing. In this paper, the content of image processing experiment is screened and integrated, and an image processing experiment system based on Matlab GUI platform is established for different levels of image processing knowledge modules. In order to verify the effectiveness and practicability of the optimization algorithm proposed in this paper, experimental simulations were performed on complex natural images and complex human eye images. The speed of the USB camera is generally between 15 frames/second and 25 frames/second, and in a 240 × 320 picture, the interactive algorithm in this article only needs about 59 ms, which is enough to complete the automatic interaction in the video in real time, which is convenient for subsequent image extraction. The experimental results show that the interactive algorithm in the complex image processing system in this paper optimizes the image extraction rate and improves the antinoise performance of the segmentation and the segmentation effect of the deep depression region.

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Henry, Jason, Alvaro Rodriguez, and Donald Wlodkowic. "Impact of digital video analytics on accuracy of chemobehavioural phenotyping in aquatic toxicology." PeerJ 7 (August 5, 2019): e7367. http://dx.doi.org/10.7717/peerj.7367.

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Chemobehavioural phenotypic analysis using small aquatic model organisms is becoming an important toolbox in aquatic ecotoxicology and neuroactive drug discovery. The analysis of the organisms’ behavior is usually performed by combining digital video recording with animal tracking software. This software detects the organisms in the video frames, and reconstructs their movement trajectory using image processing algorithms. In this work we investigated the impact of video file characteristics, video optimization techniques and differences in animal tracking algorithms on the accuracy of quantitative neurobehavioural endpoints. We employed larval stages of a free-swimming euryhaline crustacean Artemia franciscana,commonly used for marine ecotoxicity testing, as a proxy modelto assess the effects of video analytics on quantitative behavioural parameters. We evaluated parameters such as data processing speed, tracking precision, capability to perform high-throughput batch processing of video files. Using a model toxicant the software algorithms were also finally benchmarked against one another. Our data indicates that variability in video file parameters; such as resolution, frame rate, file containers types, codecs and compression levels, can be a source of experimental biases in behavioural analysis. Similarly, the variability in data outputs between different tracking algorithms should be taken into account when designing standardized behavioral experiments and conducting chemobehavioural phenotyping.

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Li, Yi, and Jialin Gang. "Development of Art and Culture Creative Industry Using FPGA and Dynamic Image Sampling." Wireless Communications and Mobile Computing 2021 (April 2, 2021): 1–7. http://dx.doi.org/10.1155/2021/6639045.

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The TVPS146’s digital video processor is used for digital video processing and image output on digital signal processing (DSP) ports. The following is a summary of the field programmable gate array (FPGA) features and programming principles. FPGA is designed by the ping-pong operation, including serial to parallel conversion design, pipeline operation, and data interface synchronization. A dance video screenshot analysis microsystem is composed of it and video dynamic image processing. Finally, according to the number of dance culture performance institutions based on the internet from 2014 to 2018, the development strategy of dance culture and creativity based on the internet is put forward. The results show that the image sharpness of the video screenshots processed by the system is improved, and the effect of enhanced processing is achieved compared to the unprocessed video screenshots. In addition, after the video screenshot processed by the system is compressed, some details are lost and the outline of the image is blurred. The combination of internet immediacy, mass, grassroots, and interactivity can accelerate the development of China’s dance culture and creative industry and provide an effective practical foundation for innovation in the development of art and culture.

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Xu, Saisai. "Sports Auxiliary Training Based on Computer Digital 3D Video Image Processing." Computational Intelligence and Neuroscience 2022 (January 10, 2022): 1–8. http://dx.doi.org/10.1155/2022/2105790.

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With the continuous development of social economy, sports have received more and more attention. How to improve the quality of sports has become the focus of research. The computer digital 3D video image processing is introduced in this paper, taking shooting as the starting point, in which computer digitization technology is used to collect images of sequence targets through combining the operation flow of shooting, monitor the results and data of shooting and process 3D video images, conduct the analyze and mine according to the corresponding statistical processing results, and evaluate the corresponding training. The simulation experiment proves that the computerized digital 3D video image processing is effective and can scientifically support sports-assisted training.

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Dissertations / Theses on the topic "Digital video – Data processing"

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Altilar, Deniz Turgay. "Data partitioning and scheduling for parallel digital video processing." Thesis, Queen Mary, University of London, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399730.

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Gu, Lifang. "Video analysis in MPEG compressed domain." University of Western Australia. School of Computer Science and Software Engineering, 2003. http://theses.library.uwa.edu.au/adt-WU2003.0016.

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The amount of digital video has been increasing dramatically due to the technology advances in video capturing, storage, and compression. The usefulness of vast repositories of digital information is limited by the effectiveness of the access methods, as shown by the Web explosion. The key issues in addressing the access methods are those of content description and of information space navigation. While textual documents in digital form are somewhat self-describing (i.e., they provide explicit indices, such as words and sentences that can be directly used to categorise and access them), digital video does not provide such an explicit content description. In order to access video material in an effective way, without looking at the material in its entirety, it is therefore necessary to analyse and annotate video sequences, and provide an explicit content description targeted to the user needs. Digital video is a very rich medium, and the characteristics in which users may be interested are quite diverse, ranging from the structure of the video to the identity of the people who appear in it, their movements and dialogues and the accompanying music and audio effects. Indexing digital video, based on its content, can be carried out at several levels of abstraction, beginning with indices like the video program name and name of subject, to much lower level aspects of video like the location of edits and motion properties of video. Manual video indexing requires the sequential examination of the entire video clip. This is a time-consuming, subjective, and expensive process. As a result, there is an urgent need for tools to automate the indexing process. In response to such needs, various video analysis techniques from the research fields of image processing and computer vision have been proposed to parse, index and annotate the massive amount of digital video data. However, most of these video analysis techniques have been developed for uncompressed video. Since most video data are stored in compressed formats for efficiency of storage and transmission, it is necessary to perform decompression on compressed video before such analysis techniques can be applied. Two consequences of having to first decompress before processing are incurring computation time for decompression and requiring extra auxiliary storage.To save on the computational cost of decompression and lower the overall size of the data which must be processed, this study attempts to make use of features available in compressed video data and proposes several video processing techniques operating directly on compressed video data. Specifically, techniques of processing MPEG-1 and MPEG-2 compressed data have been developed to help automate the video indexing process. This includes the tasks of video segmentation (shot boundary detection), camera motion characterisation, and highlights extraction (detection of skin-colour regions, text regions, moving objects and replays) in MPEG compressed video sequences. The approach of performing analysis on the compressed data has the advantages of dealing with a much reduced data size and is therefore suitable for computationally-intensive low-level operations. Experimental results show that most analysis tasks for video indexing can be carried out efficiently in the compressed domain. Once intermediate results, which are dramatically reduced in size, are obtained from the compressed domain analysis, partial decompression can be applied to enable high resolution processing to extract high level semantic information.

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Wang, Yang, and 王揚. "Digital video segmentation and annotation in news programs." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31225305.

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Chen, Wu-hsiang Jonas. "Error-resilient techniques for packet video communications /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/5850.

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Bayrakeri, Sadik. "Scalable video coding using spatio-temporal interpolation." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/15385.

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Wong, Chi Wah. "Studying real-time rate control in perceptual, modeling and efficient aspects /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202004%20WONGC.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.
Includes bibliographical references (leaves 205-212). Also available in electronic version. Access restricted to campus users.

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Lanciani, Christopher A. "Compressed-domain processing of MPEG audio signals." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/13760.

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Guo, Liwei. "Restoration and modeling for multimedia compression /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?ECED%202008%20GUOL.

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Ye, Mang. "Open-world person re-identification." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/688.

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With the increasing demand of intelligent video surveillance systems, person re-identification (re-ID) plays an important role in intelligent video analysis, which aims at matching person images across non-overlapping camera views. It has gained increasing attention in computer vision community. With the advanced deep neural networks, existing methods have achieved promising performance on the widely-used re-ID benchmarks, even outperform the human-level rank-1 matching accuracy. However, most of the research efforts are conducted on the closed-world settings, with large-scale well annotated training data and all the person images are from the same visible modality. As a prerequisite in practical video surveillance application, there is still a large gap between the closed-world research-oriented setting and the practical open-world settings. In this thesis, we try to narrow the gap by studying three important issues in open-world person re-identification, including 1) unsupervised learning with large-scale unlabelled training data; 2) learning robust re-ID model with label corrupted training data and 3) cross-modality visible-thermal person re-identification with multi-modality data. For unsupervised learning with unlabelled training data, we mainly focus on video-based person re-identification, since the video data is usually easily obtained by tracking algorithms and the video sequence provides rich weakly labelled samples by assuming the image frames within the tracked sequence belonging to the same person identity. Following the cross-camera label estimation approach, we formulate the cross-camera label estimation as a one-to-one graph matching problem, and then propose a novel dynamic graph matching framework to estimate cross-camera labels. However, in a practical wild scenario, the unlabelled training data usually cannot satisfy the one-to-one matching constraint, which would result in a large proportion of false positives. To address this issue, we further propose a novel robust anchor embedding method for unsupervised video re-ID. In the proposed method, some anchor sequences are firstly selected to initialize the CNN feature representation. Then a robust anchor embedding method is proposed to measure the relationship between the unlabelled sequences and anchor sequences, which considers both the scalability and efficiency. After that, a top-{dollar}k{dollar} counts label prediction strategy is proposed to predict the labels of unlabelled sequences. With the newly estimated sequences, the CNN representation could be further updated. For robust re-ID model learning with label corrupted training data, we propose a two-stage learning method to handle the label noise. Rather than simply filtering the falsely annotated samples, we propose a joint learning method by simultaneously refining the falsely annotated labels and optimizing the neural networks. To address the limited training samples for each identity, we further propose a novel hard-aware instance re-weighting strategy to fine-tune the learned model, which assigns larger weights to hard samples with correct labels. For cross-modality visible-thermal person re-identification, it addresses an important issue in night-time surveillance applications by matching person images across different modalities. We propose a dual-path network to learn the cross-modality feature representations, which learns the multi-modality sharable feature representations by simultaneously considering the modality discrepancy and commonness. To guide the feature representation learning process, we propose a dual-constrained top-ranking loss, which contains both cross-modality and intra-modality top-ranking constraints to reduce the large cross-modality and intra-modality variations. Besides the open-world person re-identification, we have also studied the unsupervised embedding learning problem for general image classification and retrieval. Motivated by supervised embedding learning, we propose a data augmentation invariant and instance spread-out feature. To learn the feature embedding, we propose a instance feature-based softmax embedding, which optimizes the embedding directly on top of the real-time instance features. It achieves much faster learning speed and better accuracy than existing methods. In short, the major contributions of this thesis are summarized as follows. l A dynamic graph matching framework is proposed to estimate cross-camera labels for unsupervised video-based person re-identification. l A robust anchor embedding method with top-{dollar}k{dollar} counts label prediction is proposed to efficiently estimate the cross-camera labels for unsupervised video-based person re-identification under wild settings. l A two-stage PurifyNet is introduced to handle the label noise problem in person re-identification, which jointly refines the falsely annotated labels and mines hard samples with correct labels. l A dual-constrained top-ranking loss with a dual-path network is proposed for cross-modality visible-thermal person re-identification, which simultaneously addresses the cross-modality and intra-modality variations. l A data augmentation invariant and instance spread-out feature is proposed for unsupervised embedding learning, which directly optimizes the learned embedding on top of real-time instance features with softmax function

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Lee, Yen-Chi. "Error resilient video streaming over lossy networks." Diss., Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04082004-180302/unrestricted/lee%5fyen-chi%5f200312%5fphd.pdf.

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Books on the topic "Digital video – Data processing"

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Desktop digital video. Indianapolis, IN: Prompt Publications, 1997.

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Digital video & PC editing. Chicago: Contemporary Books, 2003.

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Digital video and pc editing. London: Teach Yourself, 2003.

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Nina, Parikh, ed. The digital video idea book. New York: McGraw-Hill/Osborne, 2003.

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Cope, Peter. Teach yourself digital video and PC editing. Chicago, Ill: McGraw-Hill, 2004.

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Packet video: Modeling and signal processing. Boston: Artech House, 1994.

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Rubin, Michael. Beginner's Final cut pro: Learn to edit digital video. Berkeley, CA: Peachpit Press, 2003.

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Jones, Frederic H. Desktop digital video production. Upper Saddle River, NJ: Prentice Hall, 1999.

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Schenk, Sonja. The digital filmmaking handbook. 4th ed. Boston, Mass: Cengage Learning, 2012.

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Nina, Parikh, Wang Yiduo, and Cui Wei, eds. Shu ma she xiang ji qiao da quan. Nanjing Shi: Jiangsu ke xue ji shu chu ban she, 2005.

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Book chapters on the topic "Digital video – Data processing"

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Müller, Karsten, Heiko Schwarz, Peter Eisert, and Thomas Wiegand. "Video Data Processing." In Digital Transformation, 43–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-58134-6_4.

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Leal-Taixé, Laura, Matthias Heydt, Axel Rosenhahn, and Bodo Rosenhahn. "Understanding What we Cannot See: Automatic Analysis of 4D Digital In-Line Holographic Microscopy Data." In Video Processing and Computational Video, 52–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24870-2_3.

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Bianchi, Tiziano, and Alessandro Piva. "Image and Video Processing History Recovery." In Handbook of Digital Forensics of Multimedia Data and Devices, 572–611. Chichester, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118705773.ch15.

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Kim, Seung-Jin, Tae-Su Kim, Ki-Ryong Kwon, Sang-Ho Ahn, and Kuhn-Il Lee. "Digital Watermarking Based on Three-Dimensional Wavelet Transform for Video Data." In Advances in Multimedia Information Processing - PCM 2005, 337–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11582267_30.

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Inoué, Shinya, and Kenneth R. Spring. "Digital Image Processing." In Video Microscopy, 509–58. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-5859-0_12.

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Cozzolino, Davide, and Luisa Verdoliva. "Multimedia Forensics Before the Deep Learning Era." In Handbook of Digital Face Manipulation and Detection, 45–67. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_3.

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AbstractImage manipulation is as old as photography itself, and powerful media editing tools have been around for a long time. Using such conventional signal processing methods, it is possible to modify images and videos obtaining very realistic results. This chapter is devoted to describe the most effective strategies to detect the widespread manipulations that rely on traditional approaches and do not require a deep learning strategy. In particular, we will focus on manipulations like adding, replicating, or removing objects and present the major lines of research in multimedia forensics before the deep learning era and the rise of deepfakes. The most popular approaches look for artifacts related to the in-camera processing chain (camera-based clues) or the out-camera processing history (editing-based clues). We will focus on methods that rely on the extraction of a camera fingerprint and need some prior information on pristine data, for example, through a collection of images taken from the camera of interest. Then we will shift to blind methods that do not require any prior knowledge and reveal inconsistencies with respect to some well-defined hypotheses. We will also briefly review the most interesting features of machine learning- based methods and finally present the major challenges in this area.

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Walter, Robert J., and Michael W. Berns. "Digital Image Processing and Analysis." In Video Microscopy, 327–92. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4757-6925-8_10.

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Mandal, Mrinal Kr. "Digital Image and Video Processing." In Multimedia Signals and Systems, 257–82. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0265-4_11.

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Barbarien, Joeri, Adrian Munteanu, and Peter Schelkens. "Digital Image and Video Compression." In Optical and Digital Image Processing, 441–61. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527635245.ch20.

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Meyer-Baese, Uwe. "Image and Video Processing." In Digital Signal Processing with Field Programmable Gate Arrays, 739–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-45309-0_10.

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Conference papers on the topic "Digital video – Data processing"

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Patil, Jagruti, and Punit Tulpule. "Infrastructure Camera Video Data Processing of Traffic at Roundabouts." In SAE WCX Digital Summit. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2021. http://dx.doi.org/10.4271/2021-01-0165.

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Wang, Hongtao, Xinhua Li, and Shusheng Wang. "A Video Data Processing Method for Space Application." In ICDSP 2020: 2020 4th International Conference on Digital Signal Processing. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3408127.3408147.

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Chochia, Pavel A. "Automatic gray scale correction of video data." In Digital Image Processing and Computer Graphics: Fifth International Workshop, edited by Nikolai A. Kuznetsov and Victor A. Soifer. SPIE, 1994. http://dx.doi.org/10.1117/12.199656.

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Topiwala, Pankaj, and Wei Dai. "Bootstrapping HDR video quality analysis from SDR via data-adaptive grading." In Applications of Digital Image Processing XLV, edited by Andrew G. Tescher and Touradj Ebrahimi. SPIE, 2022. http://dx.doi.org/10.1117/12.2633820.

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Rivera López, José Saúl, and César Joel Camacho Bello. "Efficient encryption of image data in video sequences using discrete orthogonal moments." In Applications of Digital Image Processing XL, edited by Andrew G. Tescher. SPIE, 2017. http://dx.doi.org/10.1117/12.2274256.

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Reznik, A. L., A. A. Soloviev, and A. V. Torgov. "Improving the spatial resolution of digital images and video sequences using subpixel scanning." In Spatial Data Processing for Monitoring of Natural and Anthropogenic Processes 2021. Crossref, 2021. http://dx.doi.org/10.25743/sdm.2021.44.67.029.

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High-performance method for improving the resolution of digital images and video sequences based on minimum-variance signal reconstruction are considered. A distinctive feature of the developed algorithms is that they allow (with the availability of modern computing power) to obtain improved images and video in “real time”.

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Nikolaidis, Nikos, and Ioannis Pitas. "Image and video fingerprinting for digital rights management of multimedia data." In 2006 International Symposium on Intelligent Signal Processing and Communications. IEEE, 2006. http://dx.doi.org/10.1109/ispacs.2006.364920.

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Arsic, Dejan, Bjorn Schuller, Benedikt Hornler, and Gerhard Rigoll. "Resolving partial occlusions in crowded environments utilizing range data and video cameras." In 2009 16th International Conference on Digital Signal Processing (DSP). IEEE, 2009. http://dx.doi.org/10.1109/icdsp.2009.5201162.

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Wang, Zhiwei, and Linjing Lai. "No-reference video quality assessment using data dimensionality reduction and attention-based pooling." In Fourteenth International Conference on Digital Image Processing (ICDIP 2022), edited by Yi Xie, Xudong Jiang, Wenbing Tao, and Deze Zeng. SPIE, 2022. http://dx.doi.org/10.1117/12.2643807.

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Raheel, Muhammad Salman, Saeid Iranmanesh, Raad Raad Member, and Mohammad Raad. "An adaptive data collection technique for streaming video over decentralized MANETs." In 2016 Sixth International Conference on Digital Information Processing and Communications (ICDIPC). IEEE, 2016. http://dx.doi.org/10.1109/icdipc.2016.7470815.

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Reports on the topic "Digital video – Data processing"

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Ioup, Juliette W., George E. Ioup, and Joseph S. Wheatley. Wavelet Digital Signal Processing of Undersea Acoustic Data. Fort Belvoir, VA: Defense Technical Information Center, April 2002. http://dx.doi.org/10.21236/ada405774.

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Rhodes, William T. Optical Digital Algebraic Processing for Multi-Sensor-Array Data. Fort Belvoir, VA: Defense Technical Information Center, February 1986. http://dx.doi.org/10.21236/ada167196.

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Toutin, Th, and M. Beaudoin. Real-time Extraction on Planimetric and Altimetric Features From Digital Stereo SPOT Data Using a Digital Video Plotter. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1995. http://dx.doi.org/10.4095/218531.

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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), April 1997. http://dx.doi.org/10.2172/2734.

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Rheault, M. M., R. Simard, P. Keating, and M. M. Pelletier. Mineral exploration: digital image processing of LANDSAT, SPOT, magnetic and geochemical data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128045.

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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, September 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 obtained. The vehicle modelled in CATIA is imported in the LS-DYNA software simulation environment to perform frontal crash simulation at the captured speed. The simulation is compared with the crash video at 5, 25, and 40 milliseconds respectively. The comparison shows that the crash pattern of simulation and real crash video are similar in detail. Thus the modelled Auto-rickshaw can be used in the future to validate the real-time crash for providing the scope of improvement in Three-wheeler safety.

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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, September 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 obtained. The vehicle modelled in CATIA is imported in the LS-DYNA software simulation environment to perform frontal crash simulation at the captured speed. The simulation is compared with the crash video at 5, 25, and 40 milliseconds respectively. The comparison shows that the crash pattern of simulation and real crash video are similar in detail. Thus the modelled Auto-rickshaw can be used in the future to validate the real-time crash for providing the scope of improvement in Three-wheeler safety.

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Chien, Stanley, Lauren Christopher, Yaobin Chen, Mei Qiu, and Wei Lin. Integration of Lane-Specific Traffic Data Generated from Real-Time CCTV Videos into INDOT's Traffic Management System. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317400.

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The Indiana Department of Transportation (INDOT) uses about 600 digital cameras along populated Indiana highways in order to monitor highway traffic conditions. The videos from these cameras are currently observed by human operators looking for traffic conditions and incidents. However, it is time-consuming for the operators to scan through all video data from all the cameras in real-time. The main objective of this research was to develop an automatic and real-time system and implement the system at INDOT to monitor traffic conditions and detect incidents automatically. The Transportation and Autonomous Systems Institute (TASI) of the Purdue School of Engineering and Technology at Indiana University-Purdue University Indianapolis (IUPUI) and the INDOT Traffic Management Center have worked together to research and develop a system that monitors the traffic conditions based on the INDOT CCTV video feeds. The proposed system performs traffic flow estimation, incident detection, and the classification of vehicles involved in an incident. The goal was to develop a system and prepare for future implementation. The research team designed the new system, including the hardware and software components, the currently existing INDOT CCTV system, the database structure for traffic data extracted from the videos, and a user-friendly web-based server for identifying individual lanes on the highway and showing vehicle flowrates of each lane automatically. The preliminary prototype of some system components was implemented in the 2018–2019 JTRP projects, which provided the feasibility and structure of the automatic traffic status extraction from the video feeds. The 2019–2021 JTRP project focused on developing and improving many features’ functionality and computation speed to make the program run in real-time. The specific work in this 2021–2022 JTRP project is to improve the system further and implement it on INDOT’s premises. The system has the following features: vehicle-detection, road boundary detection, lane detection, vehicle count and flowrate detection, traffic condition detection, database development, web-based graphical user interface (GUI), and a hardware specification study. The research team has installed the system on one computer in INDOT for daily road traffic monitoring operations.

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Bates, C. Richards, Melanie Chocholek, Clive Fox, John Howe, and Neil Jones. Scottish Inshore Fisheries Integrated Data System (SIFIDS): Work package (3) final report development of a novel, automated mechanism for the collection of scallop stock data. Edited by Mark James and Hannah Ladd-Jones. Marine Alliance for Science and Technology for Scotland (MASTS), 2019. http://dx.doi.org/10.15664/10023.23449.

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[Extract from Executive Summary] This project, aimed at the development of a novel, automated mechanism for the collection of scallop stock data was a sub-part of the Scottish Inshore Fisheries Integrated Data Systems (SIFIDS) project. The project reviewed the state-of-the-art remote sensing (geophysical and camera-based) technologies available from industry and compared these to inexpensive, off-the -shelf equipment. Sea trials were conducted on scallop dredge sites and also hand-dived scallop sites. Data was analysed manually, and tests conducted with automated processing methods. It was concluded that geophysical acoustic technologies cannot presently detect individual scallop but the remote sensing technologies can be used for broad scale habitat mapping of scallop harvest areas. Further, the techniques allow for monitoring these areas in terms of scallop dredging impact. Camera (video and still) imagery is effective for scallop count and provide data that compares favourably with diver-based ground truth information for recording scallop density. Deployment of cameras is possible through inexpensive drop-down camera frames which it is recommended be deployed on a wide area basis for further trials. In addition, implementation of a ‘citizen science’ approach to wide area recording is suggested to increase the stock assessment across the widest possible variety of seafloor types around Scotland. Armed with such data a full, statistical analysis could be completed and data used with automated processing routines for future long-term monitoring of stock.

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Volkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3241.

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The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business for business analytics and production, in sales for offerings and, for sales forecasting, in marketing for customizing customers, and recommendations on purchasing, digital marketing, in banking and insurance for risk assessment, fraud detection, scoring, and in medicine for disease forecasting, process automation and patient health monitoring, in tourism in the field of price analysis, flight safety, opinion mining etc. However, data science applications in education have been relatively limited, and many opportunities for advancing the fields still unexplored.

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