Measuring Cubeness of 3D Shapes

Authors:
Carlos Martinez-Ortiz;Joviša Žunić
Affiliations:
Department of Computer Science, University of Exeter, Exeter, U.K. EX4 4QF;Department of Computer Science, University of Exeter, Exeter, U.K. EX4 4QF
Venue:
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2009

Citing 9
Cited 1

n-Dimensional Moment Invariants and Conceptual Mathematical Theory of Recognition n-Dimensional Solids

IEEE Transactions on Pattern Analysis and Machine Intelligence
A search engine for 3D models

ACM Transactions on Graphics (TOG)
Rectilinearity Measurements for Polygons

IEEE Transactions on Pattern Analysis and Machine Intelligence
Measuring shape: ellipticity, rectangularity, and triangularity

Machine Vision and Applications
A New Convexity Measure Based on a Probabilistic Interpretation of Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
An easy measure of compactness for 2D and 3D shapes

Pattern Recognition
Measuring Elongation from Shape Boundary

Journal of Mathematical Imaging and Vision
Retrieving articulated 3-D models using medial surfaces

Machine Vision and Applications
Measuring Shape Circularity

CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications

Tunable cubeness measures for 3D shapes

Pattern Recognition Letters

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper we introduce a new measure for 3D shapes: cubeness. The new measure ranges over [0,1] and reaches 1 only when the given shapes is a cube. The new measure is invariant with respect to rotation, translation and scaling, and is also robust with respect to noise.