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Books on the topic 'Video image analysi'

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Author: Grafiati
Published: 14 March 2023

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1

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

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2

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

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3

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

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4

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

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5

Subhasis, Chaudhuri, and SpringerLink (Online service), eds. Video Analysis and Repackaging for Distance Education. New York, NY: Springer New York, 2012.

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6

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

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7

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

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8

Content-based analysis of digital video. Boston, MA: Kluwer Academic Publishers, 2004.

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9

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

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10

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

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11

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

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12

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

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13

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

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14

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

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15

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. [Washington, D.C.]: National Aeronautics and Space Administration, 1994.

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16

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

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17

Ziyou, Xiong, and Huang Thomas S. 1936-, eds. Facial analysis from continuous video with applications to human-computer interface. Boston: Kluwer Academic Publishers, 2004.

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18

Manfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Florence: 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 analysis. MAVEBA takes place every two years always in Firenze, Italy.
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19

Jay, Kuo C. C., ed. Intelligent systems for video analysis and access over the Internet. Upper Saddle River, N.J: Prentice Hall PTR, 2003.

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20

Manfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Florence: 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, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy.
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21

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

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22

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

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23

Manfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Florence: 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, image and video analysis. MAVEBA takes place every two years in Firenze, Italy. This edition celebrates twenty-two years of uninterrupted and successful research in the field of voice analysis.
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24

Manfredi, Claudia, ed. Models and Analysis of Vocal Emissions for Biomedical Applications. Florence: 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, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis.
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25

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

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26

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. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1990.

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27

Manfredi, Claudia, ed. Pan European Voice Conference - PEVOC 11. Florence: 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.comet-collegium.com)
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28

Bernd, Girod, ed. Multi-frame motion-compensated prediction for video transmission. Boston: Kluwer Academic Publishers, 2001.

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29

Domingo, Mery, and Rueda Luis, eds. Advances in image and video technology: Second pacific rim symposium, PSIVT 2007, Santiago, Chile, December 17-19, 2007 : proceedings. Berlin: Springer, 2007.

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30

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

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31

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

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32

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 disclosed by the suggestive positioning of the gendered body, which extends far beyond everyday experience. Such notions are theorized through aspects of hyperembodiment and hypersexuality, wherein the technological constructedness of the body constitutes a prime part of video production. The aesthetics of performance are predicated on the reassemblance of the body audiovisually. Editing, production, and technology shape the images, which are stimulated by musical sound, and ultimately the audiovisual flow in pop videos mediates a range of conventions that say much about our ever-evolving cultural domains.
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33

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 video types that have come into existence online, placing them in five categories. In particular, the chapter focuses on interactive music videos and music video apps through close analyses of both Arcade Fire’s interactive video “We Used to Wait” and Björk’s interactive “app album”Biophilia. Both of these actively challenge what we have come to expect of music videos while still performing some familiar functions, prompting us to consider whether they are even music videos.
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34

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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35

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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36

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

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37

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

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38

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

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39

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

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40

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

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41

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

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42

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

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43

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

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44

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

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45

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

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46

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

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47

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

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48

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

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49

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

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50

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

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