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Abstract

The large memory requirements associated with storing pictorial data are well known to anyone who has worked with them. For example, storing an ordinary frame of television requires at least 512×512 bytes, if we use three bits for two of the primary colors and two for the third. A black and white passport photograph requires at least a 64×64 matrix with six bits per element, well above the size of a record containing whatever other information is in a passport. (A page of single-spaced typewritten text requires about 3000 bytes.) Problems of storage, search, retrieval, transmission, etc. are particularly difficult whenever pictorial data are encountered. These difficulties are somewhat counterintuitive because humans often find it easier to deal with pictures than with text. It is far easier for us to remember the face of a new acquaintance than a page of typewritten text. The difficulty of matching such human performance on a computer can be appreciated by pointing out that, at least for some people, the recollection of the face is better when it belongs to a member of the opposite sex and that the text is remembered better if it is a piece of prose than if it is a list of names. Therefore, any data compaction techniques that depend only on signal processing are not likely to reduce the data volume to a size compatible with our intuitive expectations.

Keywords

Binary Tree Single Pixel Adjacency Graph Bibliographical Note Traversal Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Relevant Literature

  1. [6.AHU]
    Aho, A. V.; Hopcroft, J. E.; and Ullman, J. D. The Design and Analysis of Computer Algorithms, Reading, Mass.: Addison-Wesley, 1974.MATHGoogle Scholar
  2. [6.DEL]
    DeMillo, R. A.; Eisenstat, S. C.; and Lipton, R. J. “Preserving Average Proximity in Arrays” CACM, 21 (1978), pp. 228–231.MATHMathSciNetGoogle Scholar
  3. [6.GR]
    Grimsdale, R. L.; Summer, F. H.; Tunis, C. J.; and Kilburn, T. “A System for the Automatic Recognition of Patterns,” Proc. IEE, 106B(1959), pp. 210–221.Google Scholar
  4. [6.HP]
    Horowitz, S. L. and Pavlidis, T. “Picture Segmentation by a Tree Traversal Algorithm,” Journal of the ACM, 23 (1976), pp. 368–388.CrossRefMATHGoogle Scholar
  5. [6.HS]
    Hunter, G. M. and Steiglitz, K. “Operations on Images Using Quad Trees,” IEEE Trans. Pattern Analysis and Machine Intelligence, PAMI-1 (1979), pp. 145–153.CrossRefGoogle Scholar
  6. [6.KD]
    Klinger, A. and Dyer, C. R. “Experiments on Picture Representation Using Regular Decomposition,” CGIP, 5 (1976), pp. 68–105.Google Scholar
  7. [6.KK]
    Knowlton, K. “Progressive Transmission of Grey Scale and B/W Pictures by Simple, Efficient and Lossless Encoding Schemes,” IEEE Proceedings, 68 (1980), pp. 885–896.CrossRefGoogle Scholar
  8. [6.KN]
    Knuth, D.E., Fundamental Algorithms, vol. 1, Reading, Mass.: Addison-Wesley, 1973.Google Scholar
  9. [6.RO]
    Rosenberg, A. L. “Preserving Proximity in Arrays,” SIAM J. Computing, 4 (1975), pp. 443–460.CrossRefMATHGoogle Scholar
  10. [6.SH]
    Shani, U. “Filling Regions in Binary Raster Images — a Graph-theoretic Approach,” SIGGRAPH’80, pp. 321–327.Google Scholar
  11. [6.SR]
    Sammet, H. and Rosenfeld, A. “Quadtree Representations of Binary Images,” Proc. Fifth Intern. Conf. on Pattern Recognition, Miami Beach, December 1980, pp. 815–818. (Published by IEEE Computer Society, IEEE Catalog No. 80CH1499–3.)Google Scholar
  12. [6.ST]
    Sloan, K. R., Jr. and Tanimoto, S. L. “Progressive Refinement of Raster Images,” IEEE Trans. on Computers, C-28 (1979), pp. 871–874.CrossRefGoogle Scholar
  13. [6.TA]
    Tanimoto, S. L., “Hierarchical Approaches to Picture Processing,” Ph. D. Dissertation, Dept. of Electrical Engineering, Princeton University, August 1975, 241pp. (Available from University Microfilms).Google Scholar
  14. [6.TP]
    Tanimoto, S. L. and Pavlidis, T. “A Hierarchical Data Structure for Picture Processing,” CGIP, 2 (1975), pp. 104–119.Google Scholar
  15. [6.WA]
    Warnock, J. E. “A Hidden-Surface Algorithm for Computer Generated Half-tone Pictures,” Technical Report 4–15, Computer Science Department, University of Utah, 1969.Google Scholar

Copyright information

© Computer Science Press, Inc. 1982

Authors and Affiliations

  • Theo Pavlidis
    • 1
  1. 1.Bell LaboratoriesUSA

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