@Article{JICS-17-064,
author = {Liu , ChunyanYang , Jianwei and Zhou , Chengxi},
title = {Affine Invariant Representation with Generic Polar Radius Integral Transform},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {17},
number = {1},
pages = {064--074},
abstract = {<p style="text-align: justify;">In many computer vision tasks, the extraction of features invariant to affine transform plays
an important role. To achieve better accuracy, region-based approaches usually need expensive
computation. Whereas, contour-based methods need less computation, but their performance is strongly
dependant on the boundary extraction. A method, generic polar radius integral transform (GPRIT), is
proposed to combine region-based and contour-based method together for the extraction of affine
invariant features. Polar radius integral transform and central projection transform are all special cases
of the proposed GPRIT. With GPRIT, any object is converted into a closed curve for data reduction.
Consequently, stationary wavelet transform is conducted to construct affine invariants. Several
experiments have been presented to evaluate performance of the proposed GPRIT.</p>},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22363.html}
}