Estimating complexity of 2D shapes

Yinpeng Chen, Hari Sundaram

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper deals with the problem of estimating 2D shape complexity. This has important applications in computer vision as well as in developing efficient shape classification algorithms. We define shape complexity using correlates of Kolmogorov complexity - entropy measures of global distance and local angle, and a measure of shape randomness. We tested our algorithm on synthetic and real world datasets with excellent results. We also conducted user studies that indicate that our measure is highly correlated with human perception. They also reveal an intuitive shape sensitivity curve - simple shapes are easily distinguished by small complexity variations, while complex shapes require significant complexity differences to be differentiated.

Original languageEnglish (US)
Title of host publication2005 IEEE 7th Workshop on Multimedia Signal Processing, MMSP 2005
PublisherIEEE Computer Society
ISBN (Print)0780392892, 9780780392892
DOIs
StatePublished - Jan 1 2005
Externally publishedYes
Event2005 IEEE 7th Workshop on Multimedia Signal Processing, MMSP 2005 - Shanghai, China
Duration: Oct 30 2005Nov 2 2005

Publication series

Name2005 IEEE 7th Workshop on Multimedia Signal Processing

Other

Other2005 IEEE 7th Workshop on Multimedia Signal Processing, MMSP 2005
Country/TerritoryChina
CityShanghai
Period10/30/0511/2/05

ASJC Scopus subject areas

  • Signal Processing

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