2001
@article{ShC2001,
vgclass = {refpap},
author = {Yuan Shao and Mehmet Celenk},
title = {Higher-order spectra ({HOS}) invariants for shape
recognition},
journal = {Pattern Recognition},
volume = {34},
number = {11},
pages = {2097--2113},
month = {November},
year = {2001},
url = {http://dx.doi.org/10.1016/S0031-3203(00)00148-5},
abstract = {This paper describes a shape feature-based invariant
object recognition method. First, a set of features invariant to
rotation, translation, and scaling (RTS) is generated using the Radon
transform and bispectral analysis. In order to improve the noise
resistance of the invariants, the ensemble averaging technique is
introduced into the estimation of bispectra. The feature data are
further reduced to a smaller set using thresholding and principal
component analysis. The resultant feature invariants are proved to be
more reliable and discriminable in the classification stage than the
original ones. It is shown experimentally that the extracted
higher-order spectra (HOS) invariants form compact and isolated
clusters in the feature space, and that a simple minimum distance
classifier yields high classification accuracy with low SNR inputs. The
comparison study with Hu's moment invariants and Fourier descriptors
also shows that the performance of the proposed method is better than
these two methods especially in the presence of background noise. The
HOS invariants algorithm is also applied to shape-similarity-based
image indexing. A new similarity matching technique based on Tanimoto
measure is employed for fast image retrieval. The retrieval accuracy is
high as shown in the experimental results.},
}