Signal, Image and Video Processing

, Volume 7, Issue 3, pp 467–478 | Cite as

Information measures for object understanding

  • Xavier Bonaventura
  • Miquel Feixas
  • Mateu Sbert
Original Paper


In this paper, we present a new information-theoretic framework for object understanding. From a visibility channel between a set of viewpoints and the polygons of an object, and three specific information measures introduced in the field of neural systems, we analyze and visualize the information associated with an object. Our approach is twofold since we present several forms of representing the shape information in the object space and different ways of capturing this information from the viewpoint space. First, we introduce several information measures associated with the polygons of the object. The way we visualize, this polygonal information provides us with different forms of perceiving the shape of the object. Second, we present several ways of evaluating the shape information from the observer’s point of view. To do this, the polygonal information is “projected” onto the viewpoints to quantify the information associated with a viewpoint and is used to select the \(N\) best views and to explore the object. A number of experiments show the behavior of all proposed measures.


Information theory Ambient occlusion Obscurances  Viewpoint selection Object exploration 



This work has been funded in part by grant number TIN2010-21089-C03-01 of Spanish Government and grant number 2009-SGR-643 of Generalitat de Catalunya (Catalan Government).


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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Xavier Bonaventura
    • 1
  • Miquel Feixas
    • 1
  • Mateu Sbert
    • 1
  1. 1.University of GironaGironaSpain

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