SHREC'21: Quantifying shape complexity

@article{Arslan2022SHREC21QS,
  title={SHREC'21: Quantifying shape complexity},
  author={Mazlum Ferhat Arslan and Alexandros Haridis and Paul L. Rosin and Sibel Tari and Charlotte A. Brassey and James D. Gardiner and Asli Gençtav and Murat Genctav},
  journal={Comput. Graph.},
  year={2022},
  volume={102},
  pages={144-153}
}
1 Citations

References

SHOWING 1-10 OF 33 REFERENCES
Shape complexity based on mutual information
TLDR
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.
Measuring the Complexity of Polygonal Objects
TLDR
A basic set of parameters describing a polygon and a set of intuitive lingual properties are developed and condensed into one measure of complexity, which distinguishes a wider range of more and less complex objects and is more intuitive than the fractal dimension.
Alpha shapes: determining 3D shape complexity across morphologically diverse structures
TLDR
Beyond genital shape, the alpha-shapes technique holds considerable promise for new applications across evolutionary, ecological and palaeoecological disciplines, and is interpreted as being particularly sensitive to concavities in surface topology, potentially distinguishing it from other shape complexity metrics.
Shape complexity
TLDR
This work discusses several complexity measures and the corresponding complexity reduction techniques and describes how the footprint and ease of use of a data structure, or the storage size of a compressed model, vary as a function of accuracy.
Complexity of Shapes Embedded in Zn With a Bias Towards Squares
TLDR
What zero complexity implies in terms of information repetition and constructibility and what kind of shapes in addition to squares have zero complexity are discussed.
An efficiently computable metric for comparing polygonal shapes
TLDR
A method for comparing polygons that has these properties and works for both convex and nonconvex polygons and runs in time O(mn log mn) where m is the number of vertices in one polygon and n is the size of the polygons in the other.
Physical determinants of the judged complexity of shapes.
  • F. Attneave
  • Psychology
    Journal of experimental psychology
  • 1957
TLDR
In the study reported here, ratings of the complexity of nonrepresenta-tional shapes were obtained from a large number of Ss and related to measurable physical characteristics of the shapes and serve to indicate the physical variables most likely to be relevant to other tasks, like those initially mentioned, on which data are typically harder to get and less precise.
Discrepancy: Local/Global Shape Characterization with a Roundness Bias
TLDR
A local measure of deviation from a disk is proposed as the local difference between numerical solution of a PDE on the shape and an analytical expression in the form of modified Bessel function.
...
...