Hierarchical image analysis using irregular tessellations

  • Annick Montanvert
  • Peter Meer
  • Azriel Rosenfeld
Image Features
Part of the Lecture Notes in Computer Science book series (LNCS, volume 427)


In this paper we have presented an image analysis technique in which a separate hierarchy is built over every compact object of the input. The approach is made possible by a stochastic decimation algorithm which adapts the structure of the hierarchy to the analyzed image. For labeled images the final description is unique. For gray level images the classes are defined by converging local processes and slight differences may appear. At the apex every root can recover information about the represented object in logirhtmic number of processing steps, and thus the adjacency graph can become the foundation for a reulational model of the scene.


Gray Level Gray Scale Image Similarity Graph Gray Level Image Adjacency Graph 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • Annick Montanvert
    • 1
  • Peter Meer
    • 2
  • Azriel Rosenfeld
    • 2
  1. 1.Equipe RFMQ - TIM3 (UA CNRS 397)Grenoble cedexFrance
  2. 2.Center for Automation ResearchUniversity of MarylandCollege ParkUSA

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