Open AccessWhitening central projection descriptor for affine‐invariant shape description
Author(s) -
Lan Rushi,
Yang Jianwei,
Jiang Yong,
Fyfe Colin,
Song Zhan
Publication year - 2013
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2012.0094
Subject(s) - affine transformation , affine shape adaptation , invariant (physics) , affine combination , affine hull , affine coordinate system , artificial intelligence , mathematics , harris affine region detector , fourier transform , rotation (mathematics) , projection (relational algebra) , transformation (genetics) , pattern recognition (psychology) , computer vision , noise (video) , orthographic projection , computer science , algorithm , affine space , geometry , mathematical analysis , image (mathematics) , mathematical physics , biochemistry , chemistry , gene
A novel descriptor, referred to as the whitening central projection predictor (WCPD), is developed for affine‐invariant shape description. The proposed descriptor is based on central projection transform (CPT) and whitening transform (WT). Dislike contour‐based or region‐based approaches, an object is first converted to a closed curve by CPT, which is called the general curve (GC). The derived GC not only keeps the affine transform information, but also is very robust to noise. Then WT is performed to the GC with the purpose that the affine transformation is simplified to a rotation only. Finally, Fourier descriptors are employed to remove the rotation, and WCPD is obtained. One advantage of using WCPD for affine‐invariant description lies in that it is applicable to objects consisting of several components. Furthermore, the approach used on the GC is contour‐based, and is of small computational complexity. Several experiments have been conducted to evaluate the performance of the proposed method. Experimental results show that the proposed method has a powerful discrimination ability, and is more robust to noise.