Vision pyramids that do not grow too high

  • Authors:

  • Walter G. Kropatsch;Yll Haxhimusa;Zygmunt Pizlo;Georg Langs

  • Affiliations:

  • Institute of Computer Aided Automation, Pattern Recognition and Image Processing Group 183/2, Vienna University of Technology, Favoritenstrasse 9, A-1040 Vienna, Austria;Institute of Computer Aided Automation, Pattern Recognition and Image Processing Group 183/2, Vienna University of Technology, Favoritenstrasse 9, A-1040 Vienna, Austria;Department of Psychological Sciences, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907-1364, USA;Institute of Computer Aided Automation, Pattern Recognition and Image Processing Group 183/2, Vienna University of Technology, Favoritenstrasse 9, A-1040 Vienna, Austria and Institute for Computer ...

  • Venue:
  • Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
  • Year:
  • 2005

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Abstract

In irregular pyramids, their vertical structure is not determined beforehand as in regular pyramids. We present three methods, all based on maximal independent sets from graph theory, with the aim to simulate the major sampling properties of the regular counterparts: good coverage of the higher resolution level, not too large sampling gaps and, most importantly, the resulting height, e.g. the number of levels to reach the apex. We show both theoretically and experimentally that the number of vertices can be reduced by a factor of 2.0 at each level. The plausibility of log (diameter) pyramids is supported by psychological and psychophysical considerations. Their technical relevance is demonstrated by enhancing appearance-based object recognition. An irregular pyramid hypothesis generation for robust PCA through top-down attention mechanisms achieves higher speed and quality than regular pyramids and non-pyramidal approaches.