Towards understanding what makes 3D objects appear simple or complex

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

doi:10.1109/cvprw.2008.4562975

Cited by 20 publications

(23 citation statements)

References 16 publications

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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%

“…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%

“…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]. 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%

“…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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On the relationship between environmental and morphological complexity in evolved robots

Auerbach

Bongard

2012

Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation

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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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Section: Entropy Of Curvaturementioning

confidence: 99%

Section: Entropy Of Curvaturementioning

confidence: 99%

Section: Entropy Of Curvaturementioning

confidence: 99%

Section: Entropy Of Curvaturementioning

confidence: 99%

See 2 more Smart Citations

On the relationship between environmental and morphological complexity in evolved robots

Auerbach

Bongard

2012

Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation

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“…For this research, complexity refers to the quality or state of an object in terms of its intricacy and connections (Sukumar et al, 2008). There are several ways of defining the complexity of a model.…”

Section: Levels Of Complexitymentioning

confidence: 99%

Entry‐level RP machines: how well can they cope with geometric complexity?

Pei

Campbell²,

Beer³

2011

Assembly Automation

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PurposeThe purpose of this paper is to examine the limitations of entry‐level rapid prototyping (ELRP) machines when fabricating objects with high complexity.Design/methodology/approachThe literature review provides an overview of RP technologies, followed by a discussion on the different levels of complexity in objects. The paper continues with a discussion on the definition of ELRP, followed by a number of experiments to explore the limitations of an ELRP system when fabricating complex models, and to compare the results obtained with those from a professional RP machine using standardised build parameters and the same acrylonitrile butadiene styrene material.FindingsOf the five complex models that were produced from the Rapman machine, four of them were affected by warping; also, support structures were difficult to remove due to the interwoven build pattern. The study also found that the Rapman parts were coarsely built as opposed to the Dimension parts that were less coarse. The Rapman parts were also much lighter due to the hollow internal structure, as compared to the dimension parts that were virtually solid. From a quantitative viewpoint, parts produced from the Rapman machine showed significantly greater average errors in both absolute and percentage terms.Practical implicationsUsers should bear in mind the restrictions of ELRP machines when fabricating complex shapes. The models may be prone to warping and the support structures could be difficult to remove.Originality/valueThis paper allows developers to understand the restrictions when fabricating complex models on an ELRP machine. The findings will also enable manufacturers to develop better entry‐level systems.

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Analysis of morphological traits as a tool to identify the realized niche of phytoplankton populations: what do the shape of planktic microalgae, Anna Karenina and Vincent van Gogh have in common?

Naselli-Flores

Padisák

2023

Hydrobiologia

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Understanding the dynamics of phytoplankton assemblages in various and variable aquatic ecosystems is of paramount importance, given the strategic supporting services offered by these organisms. Such knowledge is implicitly based on the analysis of the realized niche of the different populations, i.e. of the sets of conditions within which populations show a positive growth. The range of phytoplankton morphological traits variability is evolutionarily selected to maximize the ecological performance of species while they are entrained in the spectrum of turbulent flows. In addition, most phytoplankton species exhibit high morphological plasticity that can further optimize their performance under reduced environmental variability. Although this plasticity is well known, it is seldom considered in phytoplankton studies. Morphological analysis could therefore be used as a tool to estimate the environmental variability within which a species can persist and, ultimately, the niche width of phytoplankton populations. This opinion paper tries to answer the questions: to what extent can the morphological variability of phytoplankton offer a synthesis of the environmental variability of aquatic ecosystems?. Do the morphological traits contain sufficient information to describe the width of the realized niche of phytoplankton species? What can we do to fill eventual gaps in our knowledge?

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Towards understanding what makes 3D objects appear simple or complex

Cited by 20 publications

References 16 publications

On the relationship between environmental and morphological complexity in evolved robots

On the relationship between environmental and morphological complexity in evolved robots

Entry‐level RP machines: how well can they cope with geometric complexity?

Analysis of morphological traits as a tool to identify the realized niche of phytoplankton populations: what do the shape of planktic microalgae, Anna Karenina and Vincent van Gogh have in common?

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