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Shape complexity based on mutual information

Shape complexity has recently received attention from different fields, such as computer vision and psychology. In this paper, integral geometry and information theory tools are applied to quantify the shape complexity from two different perspectives: from the inside of the object, we evaluate its degree of structure or correlation between its surfaces (inner complexity), and from the outside, we compute its degree of interaction with the circumscribing sphere (outer complexity). Our shape complexity measures are based on the following two facts: uniformly distributed global lines crossing an object define a continuous information channel and the continuous mutual information of this channel is independent of the object discretisation and invariant to translations, rotations, and changes of scale. The measures introduced in this paper can be potentially used as shape descriptors for object recognition, image retrieval, object localisation, tumour analysis, and protein docking, among others

© International Conference Shape Modeling and Applications, 2005, p. 355-360

IEEE

Author: Rigau Vilalta, Jaume
Feixas Feixas, Miquel
Sbert, Mateu
Date: 2005
Abstract: Shape complexity has recently received attention from different fields, such as computer vision and psychology. In this paper, integral geometry and information theory tools are applied to quantify the shape complexity from two different perspectives: from the inside of the object, we evaluate its degree of structure or correlation between its surfaces (inner complexity), and from the outside, we compute its degree of interaction with the circumscribing sphere (outer complexity). Our shape complexity measures are based on the following two facts: uniformly distributed global lines crossing an object define a continuous information channel and the continuous mutual information of this channel is independent of the object discretisation and invariant to translations, rotations, and changes of scale. The measures introduced in this paper can be potentially used as shape descriptors for object recognition, image retrieval, object localisation, tumour analysis, and protein docking, among others
Format: application/pdf
Citation: Rigau, J., Feixas, M., i Sbert, M. (2005). Shape complexity based on mutual information. International Conference Shape Modeling and Applications : 2005, 355 - 360. Recuperat 1 octubre 2010, a http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1563243
ISBN: 0-7695-2379-X
Document access: http://hdl.handle.net/10256/3066
Language: eng
Publisher: IEEE
Collection: Reproducció digital del document publicat a: http://dx.doi.org/10.1109/SMI.2005.42
Articles publicats (D-IMA)
Is part of: © International Conference Shape Modeling and Applications, 2005, p. 355-360
Rights: Tots els drets reservats
Subject: Complexitat computacional
Geometria integral
Geometria computacional
Percepció de les formes
Computational complexity
Computational geometry
Form perception
Integral geometry
Title: Shape complexity based on mutual information
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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