Open AccessSet of bilateral and radial symmetry shape descriptor based on contour information
Author(s) -
Revollo Natalia V.,
Delrieux Claudio A.,
GonzálezJosé Rolando
Publication year - 2017
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2015.0413
Subject(s) - artificial intelligence , symmetry (geometry) , object (grammar) , pattern recognition (psychology) , set (abstract data type) , cognitive neuroscience of visual object recognition , computer science , homogeneous space , computer vision , shape analysis (program analysis) , identification (biology) , active shape model , mathematics , feature extraction , geometry , static analysis , botany , segmentation , biology , programming language
Form and shape descriptors are among the most useful features for object identification and recognition. Even though there exists a widely used set of shape descriptors and underlying computational methods for their evaluation, frequently they fail to distinguish among very similar objects that they appear very different to the human eye. The authors propose a new set of shape descriptors based on a finer determination of the object symmetry axes, and a more accurate estimation of the bilateral and radial symmetries. These descriptors were thoroughly tested using several synthetic and real objects with varying degrees of symmetry. The methods for axes estimation and symmetry descriptors extraction outperform the widespread shape descriptors in recognising and identifying among very similar objects.