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Image and Video Acquisition, Representation and Storage

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Machine Learning for Audio, Image and Video Analysis

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

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The eye is the organ that allows our brain to acquire the visual information around us. One of the most challanging tasks in the science consists in developing a machine that can see, that is it can acquire, integrate and interpret the visual information embedded in still images and videos. This is the topic of scientific domain called image processing. The topic of image processing is so large it cannot be described in a single chapter. Therefore for comprehensive surveys of this topic, the reader can refer to [10] [23] [27].

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References

  1. 1. T. Acharaya and A. K. Ray. Image Processing: Principles and Applications. John Wiley and Sons, 2005.

    Google Scholar 

  2. 2. D. Ballard and C Brown. Computer Vision. Academic Press, 1982.

    Google Scholar 

  3. 3. B. E. Bayer. Color imaging array. US Patent 3,971,065. Technical report, Eastman Kodak Company, 1976.

    Google Scholar 

  4. 4. J. Bormans, J. Gelissen, and A. Perkis. MPEG-21: The 21st century multimedia framework. IEEE Signal Processing Magazine, 2003.

    Google Scholar 

  5. G. Buchsbaum. An analytical derivation of visual nonlinearity. IEEE Transac- tions on Biomedical Engineering, BME-27(5):237-242, 1980.

    Article  Google Scholar 

  6. 6. T. H. Cormen, C. E. Leiserson, and R. L. Rivest. Introduction to Algorithms. MIT Press, 1990.

    Google Scholar 

  7. 7. A. Del Bimbo, editor. Visual Information Retrieval. Morgan Kaufman Publish- ers, 1999.

    Google Scholar 

  8. T. Ebrahimi. MPEG-4 video verification model: A video encoding/decoding algorithm based on content representation. Image Communication Journal, 9(4):367-384, 1996.

    MathSciNet  Google Scholar 

  9. K. S. Gibson and D. Nickerson. Analysis of the munsell colour system based on maesurements made in 1919 and 1926. Journal of Optical Society of America, 3(12):591-608, 1940.

    Article  Google Scholar 

  10. 10. R. C. Gonzalez and R. E. Woods. Digital Image Processing. Addison Wesley, 1992.

    Google Scholar 

  11. 11. G. Healey and Q. Luong. Color in computer vision: Recent progress. In Handbook of Pattern Recognition and Computer Vision, pages 283-312. World Scientific Publishing, 1998.

    Google Scholar 

  12. D. A. Huffman. A method for the construction of minimum-redundancy codes. Proceedings of the IRE, 40(9):1098-1101, 1952.

    Article  Google Scholar 

  13. L. M. Hurvich and D. Jameson. An opponent process theory of colour vision. Psychological Review, 64(6):384-404, 1957.

    Article  Google Scholar 

  14. L. M. Hurvich and D. Jameson. Some quantitative aspects of an opponent-colors theory: Iv a psychological color specification system. Journal of the Optical Society of America, 45(6):416-421, 1957.

    Google Scholar 

  15. 15. A. K. Jain. Fundamentals of Digital Image Processing. Prentice-Hall, 1989.

    Google Scholar 

  16. 16. D. B. Judd and G. Wyszecki. Color in Business, Science and Industry. John Wiley and Sons, 1975.

    Google Scholar 

  17. R. Koenen, F. Pereira, and L. Chiariglione. MPEG-4: Context and objectives. Image Communication Journal, 9(4):295-304, 1997.

    Google Scholar 

  18. E. H. Land. Color vision and the natural images. Proceedings of the National Academy of Sciences, 45(1):116-129, 1959.

    Google Scholar 

  19. D. Le Gall. MPEG: a video compression standard for multimedia applications. Communications of the ACM, 34(4):46-58, 1991.

    Article  MathSciNet  Google Scholar 

  20. G. W. Meyer. Tutorial on colour science. The Visual Computer, 2(5):278-290, 1986.

    Article  Google Scholar 

  21. 21. A. H. Munsell. An Atlas of the Munsell System. Wassworth-Howland, 1915.

    Google Scholar 

  22. 22. C. L. Novak and S. A. Shafer. Color Vision. Encyclopedia of Artificial Intelli- gence. John Wiley and Sons, 1992.

    Google Scholar 

  23. 23. W. K. Pratt. Digital Image Processing. John Wiley and Sons, 1991.

    Google Scholar 

  24. T. Sakamoto, C. Nakanishi, and T. Hase. Software pixel interpolation for dig-ital still cameras suitable for a 32-bit mcu. IEEE Transactions on Consumer Electronics, 44(4):1342-1352, 1998.

    Article  Google Scholar 

  25. P. Salembier and J. R. Smith. MPEG-7 multimedia description schemes. IEEE Transactions on Circuits and Systems for Video Technology, 11(6):748-759, 2001.

    Article  Google Scholar 

  26. 26. A. S. Tanenbaum. Modern Operating Systems. Prentice-Hall, 2001.

    Google Scholar 

  27. 27. E. Trucco and A. Verri. Introductory Techniques for 3-D Computer Vision. Prentice-Hall, 1998.

    Google Scholar 

  28. P. Tsai, T. Acharaya, and A. K. Ray. Adaptive fuzzy color interpolation. Journal of Electronic Imaging, 11(3):293-305, 2002.

    Article  Google Scholar 

  29. 29. B. A. Wandell. Foundations of Vision. Sinauer Associates, 1995.

    Google Scholar 

  30. 30. G. Wyszecki and W. S. Stiles. Color Science. Mc Graw-Hill, 1982.

    Google Scholar 

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(2008). Image and Video Acquisition, Representation and Storage. In: Machine Learning for Audio, Image and Video Analysis. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84800-007-0_3

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  • DOI: https://doi.org/10.1007/978-1-84800-007-0_3

  • Publisher Name: Springer, London

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