A Methodology for Picture Indexing and Encoding

  • Chapter
Picture Engineering

Part of the book series: Springer Series in Information Sciences ((SSINF,volume 6))

Abstract

Researchers in image processing and pattern recognition have traditionally regarded pictures as two-dimensional array of pixels. Recently, researchers working on pictorial information systems have developed the concept of logical pictures, which consist of picture objects and picture relations. The concept of relational database has also been used in develo** pictorial database models, although there seems to be a need to extend the relational database concept for pictorial database management. On the other hand, for many image processing problems, a hierarchical data structure seems to be the most natural.

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References

  1. N. S. Chang and K. S. Fu, “A Relational Database System for Images,” TR-EE 79–28, Dept. of Electrical Engineering, Purdue University, May 1979.

    Google Scholar 

  2. S. K. Chang and Y. Wong, “Optimal Hist ram Matching by Monotone Gray Level Transformation,” Communicatioi of the ACM, Vol. 22, No. 10, ACM, 835–840, October 1978.

    Article  Google Scholar 

  3. S. K. Chang, J. Reuss, and B. H. McCormick, “Design Considerations of a Pictorial Database System,” Internation Journal on Policy Analysis and Information Systems, Vol. 1, No. 2, Knowledge System Laboratory, UICC, pp. 49–70, January 1978.

    Google Scholar 

  4. S. K. Chang, B. S. Lin, and R. Walser, “A Generalized Zooming Technique for Pictorial Database Systems,” Proceedings of National Computer Conference, AFIPS, Vol. 48, pp. 147–156, 1979.

    Google Scholar 

  5. S. K. Chang, “Ln Norm Optimal Histogram Matching,” Proceedings Processing, IEEE Computer Society, pp. 169–174, August 1979.

    Google Scholar 

  6. S. K. Chang and W. H. Cheng, “A Methodology for Structured Data Base Decomposition”, IEEE Transactions on Software Engineering, Vol. SE-6, No. 2, March 1980, 205–218.

    Article  MathSciNet  Google Scholar 

  7. Y. T. Chien, “Hierarchical Data Structures for Picture Storage, Retrieval and Classification,” in Pictorial Information System, (Chang and Fu, eds.), Springer-Verlag, West Germany, 1980.

    Google Scholar 

  8. H. Freeman and R. Shapiro, “Determining the Encasing Rectangle for an Arbitrary Curve,” Communications of the ACM, Vol.18, No. 7, ACM, pp. 409–413, July 1975.

    Article  MATH  Google Scholar 

  9. A. Klinger, M. L. Rhode, and V. T. To, “Accessing Image Data,” International Journal on Policy Analysis and Information Systems, Vol. 1, No. 2, Knowledge System Laboratory, UICC, pp. 171–189, January 1978.

    Google Scholar 

  10. A. Klinger, “Analysis, Storage, and Retrieval of Elevation Data with Application to Improve Penetration,” U. S. ARMY Corps of Engineers, Engineer Topological Laboratories, Fort Belvoir, Virginia, 22060, March 1979.

    Google Scholar 

  11. S. H. Liu and S. K. Chang, “Picture Covering by 2-D AH Encoding”, Proceedings of IEEE Workshop on Computer Architecture for Pattern Analysis and Image Database Management, Hot Springs, Virginia, November 11–13, 1981.

    Google Scholar 

  12. D. M. Mckeown Jr. and D. J. Reddy, “A Hierarchical Symbolic Representation for Image Database,” Proceedings of IEEE Workshop on Picture Data Description and Management, IEEE Computer Society, pp. 40–44, April 1977.

    Google Scholar 

  13. R. D. Merrill, “Representation of Contours and Regions for Efficient Computer Search,” Communications of the ACM, Vol. 16, No. 2, ACM, pp. 69–82, February 1973.

    Article  MathSciNet  Google Scholar 

  14. D. L. Milgram, “Constructing Trees for Region Description,” Computer Graphics and Image Processsing 11, Academic Press, pp. 88–99, 1979.

    Google Scholar 

  15. J. Omolayole and A. Klinger, “A Hierarchical Data Structure Scheme for Storing Pictures,” Technical Report, Computer Science Department, UCLA, 1979.

    Google Scholar 

  16. J. L. Reuss and S. K. Chang, “Picture Paging for Efficient Image Processing,” Proceedings of IEEE Computer Society Conference on Pattern Recognition and Image Processing, IEEE Computer Society, pp. 69–74, May 1978.

    Google Scholar 

  17. A. Rosenfeld and A. C. Kak, Digital Picture Processing, Academic Press, N. Y., 1976.

    Google Scholar 

  18. L. G. Shapiro and R. M. Haralick, “A Spatial Data Structure,” Technical Report #CS 79005-R, Dept. of Computer Science, Virginia Polytechnic Institute and State University, p. 35, August 1979.

    Google Scholar 

  19. H. Silver, “An Investigation into Picture Paging Techniques”, ISRL Technical Report, Department of Information Engineering, University of Illinois at Chicago, March 1982.

    Google Scholar 

  20. J. M. Smith, and D. C. P. Smith, “Database Abstraction: Aggragation and Geralization”, ACM Trans, on Database Systems, Vol. 2. No. 2, pp. 105–133, 1977.

    Article  Google Scholar 

  21. S. L. Tanimoto, “An Iconic/Symbolic Data Structuring Scheme,” in Pattern Recognition and Artificial Intelligence, Academic Press, pp. 452–471, 1976.

    Google Scholar 

  22. M. Ward and Y. T. Chien, “A Pictorial Database Management System which uses Histogram Classification as a Similarity Measure,” Proceedings of COMPSAC 79, IEEE Computer Society, pp. 153–156, 1979.

    Google Scholar 

  23. C. C. Yang and S. K. Chang, “Encoding Techniques for Efficient Retrieval from Pictorial Databases,” Proceedings of IEEE Computer Society Conference on Pattern Recognition and Image Processing, IEEE Computer Society, pp. 120–125, June 1978.

    Google Scholar 

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© 1982 Springer-Verlag Berlin Heidelberg

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Chang, SK. (1982). A Methodology for Picture Indexing and Encoding. In: Fu, Ks., Kunii, T.L. (eds) Picture Engineering. Springer Series in Information Sciences, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-87867-1_3

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  • DOI: https://doi.org/10.1007/978-3-642-87867-1_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-87869-5

  • Online ISBN: 978-3-642-87867-1

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