Technical Section: Viewpoint-driven simplification using mutual information
Computers and Graphics
Scene complexity analysis using random curves
CGIM '08 Proceedings of the Tenth IASTED International Conference on Computer Graphics and Imaging
SMI 2011: Full Paper: On visual complexity of 3D shapes
Computers and Graphics
Information theory in computer graphics and visualization
SIGGRAPH Asia 2011 Courses
Sketch-based 3D model retrieval by viewpoint entropy-based adaptive view clustering
3DOR '13 Proceedings of the Sixth Eurographics Workshop on 3D Object Retrieval
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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.