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Books on the topic 'Video image analysi'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

Tokan-Lawal, Folashade. Quantitative image analysis of video images of the larynx. UMIST, 1998.

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2

Tian, Jing, and Li Chen. Intelligent image and video interpretation: Algorithms and applications. Information Science Reference, 2013.

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3

C, Kotropoulos, and Pitas I, eds. Nonlinear model-based image/video processing and analysis. Wiley, 2001.

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4

Nony, Anaïs. Performative Images. Amsterdam University Press, 2023. http://dx.doi.org/10.5117/9789463722827.

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Performative Images draws upon the work of video artists and activists in France between the 1970s and the early 2020s and focuses on significant practices with technology. Video art and video activism are analysed together in the book to revaluate key concepts in media studies and foreground a performative approach to the theory of image technology. The book engages works in visual culture, performance studies, digital studies, critical race theory, and feminist methodologies to account for the changes brought about by video technology in social and psychic life. Performative Images is about

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5

Camastra, Francesco, and Alessandro Vinciarelli. Machine Learning for Audio, Image and Video Analysis. Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6735-8.

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6

Camastra, Francesco, and Alessandro Vinciarelli. Machine Learning for Audio, Image and Video Analysis. Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-007-0.

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7

Kwaśnicka, Halina, and Lakhmi C. Jain, eds. Bridging the Semantic Gap in Image and Video Analysis. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73891-8.

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8

Randall, Reed Todd, ed. Digital image sequence processing, compression, and analysis. CRC Press, 2005.

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9

Zheng, Nanning. Statistical learning and pattern analysis for image and video processing. Springer, 2009.

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10

Zheng, Nanning, and Jianru Xue. Statistical Learning and Pattern Analysis for Image and Video Processing. Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-312-9.

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11

Alessandro, Vinciarelli, ed. Machine learning for audio, image and video analysis: Theory and applications. Springer, 2008.

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12

Lauren, Fletcher, Klute Glenn K, and United States. National Aeronautics and Space Administration., eds. Evaluation of lens distortion errors using an underwater camera system for video-based motion analysis. National Aeronautics and Space Administration, 1994.

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13

Rajala, Sarah A. Evaluation of the wake effects on plume dispersion using video image analysis. U.S. Environmental Protection Agency, Atmospheric Research and Exposure Assessment Laboratory, 1990.

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14

Turner, James M. Images en mouvement: Stockage, repérage, indexation. Presses de l'Université du Québec, 1998.

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15

Merhav, Neri. Fast inverse motion compensation algorithms for MPEG-2 and for partial DCT information. Hewlett-Packard Laboratories, Technical Publications Department, 1996.

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16

Manfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Firenze University Press, 2015. http://dx.doi.org/10.36253/978-88-6655-793-7.

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The Models and Analysis of Vocal Emissions with Biomedical Applications (MAVEBA) workshop came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video an

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17

Amato, Alberto. Semantic Analysis and Understanding of Human Behavior in Video Streaming. Springer New York, 2013.

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18

Manfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Firenze University Press, 2017. http://dx.doi.org/10.36253/978-88-6453-607-1.

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The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, im

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19

L, Snow W., and Langley Research Center, eds. Video photographic considerations for measuring the proximity of a probe aircraft with a smoke seeded trailing vortex. National Aeronautics and Space Administration, Langley Research Center, 1990.

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20

Schneider, Florian. Studying Political Communication and Media in East Asia. Amsterdam University Press, 2025. https://doi.org/10.5117/9789048564453.

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Communication is at the heart of politics, and certainly in East Asia, where vibrant media systems and cultural industries connect with political processes that range from the democratic to autocratic. If we are to understand East Asian societies, and 21st-century politics more broadly, we need to take media and communication seriously. This textbook provides a flexible toolbox of methods and theories for studying political communication. Taking its examples and exercises from Japan, Korea, and the Chinese-speaking world, it walks readers through core issues, media contents, and communicative

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21

Jackson, Susan. The power of the visual image: An analysis of the imagery of tourist videos. [University of Surrey], 1991.

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22

Manfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-449-6.

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The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the newborn to the adult and elderly. Over the years the initial issues have grown and spread also in other fields of research such as occupational voice disorders, neurology, rehabilitation, ima

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23

Manfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Firenze University Press, 2019. http://dx.doi.org/10.36253/978-88-6453-961-4.

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The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, im

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24

Manfredi, Claudia, ed. Pan European Voice Conference - PEVOC 11. Firenze University Press, 2015. http://dx.doi.org/10.36253/978-88-6655-795-1.

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The Pan European VOice Conference (PEVOC) was born in 1995 and therefore in 2015 it celebrates the 20th anniversary of its establishment: an important milestone that clearly expresses the strength and interest of the scientific community for the topics of this conference. The most significant themes of PEVOC are singing pedagogy and art, but also occupational voice disorders, neurology, rehabilitation, image and video analysis. PEVOC takes place in different European cities every two years (www.pevoc.org). The PEVOC 11 conference includes a symposium of the Collegium Medicorum Theatri (www.com

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25

Allain, Rhett. Physics and Video Analysis. Morgan & Claypool Publishers, 2016.

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26

(Editor), C. Kotropoulos, and Ioannis Pitas (Editor), eds. Nonlinear Model-Based Image/Video Processing and Analysis. Wiley-Interscience, 2001.

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27

Image and video processing in the compressed domain. CRC Press, 2011.

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28

Chaudhuri, Subhasis, and A. Ranjith Ram. Video Analysis and Repackaging for Distance Education. Springer New York, 2014.

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29

Hanjalic, Alan. Content-Based Analysis of Digital Video. Springer, 2013.

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30

Hanjalic, Alan. Content-Based Analysis of Digital Video. Springer, 2004.

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31

Jain, Lakhmi C., and Halina Kwaśnicka. Bridging the Semantic Gap in Image and Video Analysis. Springer, 2018.

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32

Jain, Lakhmi C., and Halina Kwaśnicka. Bridging the Semantic Gap in Image and Video Analysis. Springer, 2019.

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33

Acton, Scott T., and Nilanjan Ray. Biomedical Image Analysis: Segmentation (Synthesis Lectures on Image, Video, & Multimedia Processing). Morgan & Claypool Publishers, 2007.

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34

Scott T. Acton and Nilanjan Ray. Biomedical Image Analysis: Tracking (Synthesis Lectures on Image, Video, & Multimedia Processing). Morgan & Claypool Publishers, 2006.

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35

Hanjalic, A. Content-Based Analysis of Digital Video. Kluwer Academic Publishers, 2004.

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36

Pitas, Ioannis, and C. Kotropoulos. Nonlinear Model-Based Image/Video Processing and Analysis. Wiley & Sons, Incorporated, John, 2008.

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37

Hawkins, Stan. Aesthetics and Hyperembodiment in Pop Videos. Edited by John Richardson, Claudia Gorbman, and Carol Vernallis. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199733866.013.002.

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This article appears in theOxford Handbook of New Audiovisual Aestheticsedited by John Richardson, Claudia Gorbman, and Carol Vernallis. This chapter uses textual analysis of the music video “Umbrella,” featuring Rihanna, to demonstrate the intricacies of sound and image synchronization. It argues that music highlights subject positions according to the viewer’s expectations, assessment, and understanding of the displayed subject. Rihanna’s erotic imagery forms a critical point for contemplating the pop artist’s physical responses to music. One central ingredient of most video performances is

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38

Bio-Inspired Computing for Image and Video Processing. Taylor & Francis Group, 2018.

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39

Acharjya, D. P., and V. Santhi. Bio-Inspired Computing for Image and Video Processing. Taylor & Francis Group, 2018.

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40

Acharjya, D. P., and V. Santhi. Bio-Inspired Computing for Image and Video Processing. Taylor & Francis Group, 2018.

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41

Acharjya, D. P., and V. Santhi. Bio-Inspired Computing for Image and Video Processing. Taylor & Francis Group, 2018.

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42

Bio-Inspired Computing for Image and Video Processing. Taylor & Francis Group, 2018.

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43

Monekosso, Dorothy, Myo Thida, How-lung Eng, and Paolo Remagnino. Contextual Analysis of Videos. Morgan & Claypool Publishers, 2013.

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44

Monekosso, Dorothy, Myo Thida, How-lung Eng, and Paolo Remagnino. Contextual Analysis of Videos. Springer International Publishing AG, 2013.

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45

Monekosso, Dorothy, Myo Thida, How-lung Eng, and Paolo Remagnino. Contextual Analysis of Videos. Morgan & Claypool Publishers, 2013.

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46

Korsgaard, Mathias Bonde. Music Video Transformed. Edited by John Richardson, Claudia Gorbman, and Carol Vernallis. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199733866.013.015.

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This article appears in theOxford Handbook of New Audiovisual Aestheticsedited by John Richardson, Claudia Gorbman, and Carol Vernallis. This chapter asks what music video has become today and how its audiovisual aesthetics have changed online. It suggests that music videos generally through process of remediation content more actively than any other media form, performing the dual function of “visualizing music” (by recasting a song visually) and “musicalizing vision” (by structuring images according to musical logic). The discussion identifies and provides an overview of several new music vi

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47

Parmelee, John. Meet the Candidate Videos. Greenwood Publishing Group, Inc., 2003. http://dx.doi.org/10.5040/9798400684548.

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Parmelee shows how presidential primary campaign videocassettes serve many functions for candidates on their road to the White House. These videocassettes, which include images and issues often based on polling data and focus groups, are sent out before the primaries to battleground states to establish an initial image of the candidate. A variety of methods are used to explore the videocassettes of the Democratic and Republican presidential candidates—Gary Bauer, Bill Bradley, George W. Bush, Steve Forbes, Al Gore, and John McCain—who released presidential primary meet the candidate videos dur

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48

Chellappa, Rama, Amit K. Roy-chowdhury, Al Bovik, and S. Kevin Zhou. Recognition of Humans and Their Activities Using Video (Synthesis Lectures in Image, Video, & Multimedia Processing). Morgan & Claypool Publishers, 2005.

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49

Xue, Jianru, and Nanning Zheng. Statistical Learning and Pattern Analysis for Image and Video Processing. Springer London, Limited, 2012.

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50

The development of a video-based motion analysis system. 1992.

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