An affine invariant function using PCA bases with an application to within-class object recognition
Tzimiropoulos, Georgios; Mitianoudis, Nikolaos; Stathaki, Tania
The problem of shape-based recognition of objects under affine transformations is considered. We focus on the construction of a robust and highly discriminative affine invariant function that can be used for within-class object recognition applications. Using the boundaries of the objects of interest, a training scheme, based on principal component analysis (PCA), is proposed to derive a set of basis functions with desired properties. The derived bases are then used for the construction of a novel affine invariant function. The proposed invariant function is evaluated for the problem of aircraft silhouette identification and appears to achieve comparable performance to a popular wavelet-based affine invariant function. At the same time, the proposed framework is much simpler than that based on wavelet analysis.
Tzimiropoulos, G., Mitianoudis, N., & Stathaki, T. (2007). An affine invariant function using PCA bases with an application to within-class object recognition.
|Conference Name||ICASSP 2007 - 2007 IEEE International Conference on Acoustics, Speech and Signal Processing|
|End Date||Apr 20, 2007|
|Publication Date||Jan 1, 2007|
|Deposit Date||Feb 1, 2016|
|Publicly Available Date||Feb 1, 2016|
|Peer Reviewed||Peer Reviewed|
|Keywords||Object Recognition, Principal Component Analysis, Wavelet Transforms|
|Additional Information||Published in: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing: proceedings: April 16-20, 2007, Hawaii Convention Center, Honolulu, Hawaii, U.S.A. Piscataway, N.J. : IEEE, 2007. ISBN: 978-1-4244-0727-9. pp. I-785-I-788, doi: 10.1109/ICASSP.2007.366025 ©2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|