Shape analysis algorithm based on information theory

Page, D. H.; Koschan, Andreas; Sukumar, Sreenivas R.; Roui-Abidi, Besma; Abidi, Mongi A.

doi:10.1109/icip.2003.1246940

Cited by 82 publications

(80 citation statements)

References 8 publications

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“…Gaussian curvature is also used in previous work [Page et al 2003;Polonsky et al 2005] we compute Gaussian curvature on the surface of the object using Meyer et al's angle defect formula [2002]. We treat the computed Gaussian curvatures analogously to the mean curvatures.…”

Section: :4mentioning

confidence: 99%

Perceptual models of viewpoint preference

et al. 2011

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The question of what are good views of a 3D object has been addressed by numerous researchers in perception, computer vision, and computer graphics. This has led to a large variety of measures for the goodness of views as well as some special-case viewpoint selection algorithms. In this article, we leverage the results of a large user study to optimize the parameters of a general model for viewpoint goodness, such that the fitted model can predict people's preferred views for a broad range of objects. Our model is represented as a combination of attributes known to be important for view selection, such as projected model area and silhouette length. Moreover, this framework can easily incorporate new attributes in the future, based on the data from our existing study. We demonstrate our combined goodness measure in a number of applications, such as automatically selecting a good set of representative views, optimizing camera orbits to pass through good views and avoid bad views, and trackball controls that gently guide the viewer towards better views.

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Section: :4mentioning

confidence: 99%

Perceptual models of viewpoint preference

et al. 2011

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“…In fact, quantifying the complexity of 3D objects in this way has been shown to strongly correlate with human observers notions of complexity [35]. In the space below the building blocks of computing this measure are presented, and the reader is referred to [24] and [35] for more in depth discussions of their theoretical underpinnings.…”

Section: Entropy Of Curvaturementioning

confidence: 99%

“…This angle excess Φj has a direct relationship to the Gaussian curvature at that point [24]. This will be the variable on which entropy is calculated.…”

Section: Entropy Of Curvaturementioning

confidence: 99%

“…One commonly used measure of complexity is Shannon's Entropy [28], which measures the information content of a random variable. Recent work [24,35] has demonstrated how notions of Shannon Entropy can be applied to measuring the complexity of a 3D object by considering the curvature of the object as a random variable. In fact, quantifying the complexity of 3D objects in this way has been shown to strongly correlate with human observers notions of complexity [35].…”

Section: Entropy Of Curvaturementioning

confidence: 99%

“…But, what is the random variable x on which H will be calculated? Following [24,35] x will be a measure of Gaussian curvature of the points on a body shape. Since the body shapes here are built out of triangular meshes the points at which this curvature is non-zero are precisely the vertices of the triangular mesh.…”

Section: Entropy Of Curvaturementioning

confidence: 99%

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The principles of embodied cognition dictate that intelligent behavior must arise out of the coupled dynamics of an agent's brain, body, and environment. While the relationship between controllers and morphologies (brains and bodies) has been investigated, little is known about the interplay between morphological complexity and the complexity of a given task environment. It is hypothesized that the morphological complexity of a robot should increase commensurately with the complexity of its task environment. Here this hypothesis is tested by evolving robot morphologies in a simple environment and in more complex environments. More complex robots tend to evolve in the more complex environments lending support to this hypothesis. This suggests that gradually increasing the complexity of task environments may provide a principled approach to evolving more complex robots.

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2D Shape Measures for Computer Vision

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2007

Handbook of Applied Algorithms

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Shape analysis algorithm based on information theory

Cited by 82 publications

References 8 publications

Perceptual models of viewpoint preference

Perceptual models of viewpoint preference

On the relationship between environmental and morphological complexity in evolved robots

2D Shape Measures for Computer Vision

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