Shape complexity based on mutual information
| Autor: | Jaume Rigau, Mateu Sbert, Miquel Feixas |
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| Rok vydání: | 2006 |
| Předmět: |
Theoretical computer science
Computational complexity theory Percepció de les formes Cognitive neuroscience of visual object recognition Mutual information Information theory Geometria computacional Computational geometry Form perception Object detection Integral geometry Computational complexity Geometria integral Invariant (mathematics) Complexitat computacional Algorithm Mathematics |
| Zdroj: | SMI Recercat. Dipósit de la Recerca de Catalunya instname © International Conference Shape Modeling and Applications, 2005, p. 355-360 Articles publicats (D-IMA) DUGiDocs – Universitat de Girona |
| DOI: | 10.1109/smi.2005.42 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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