Invariant object recognition using radon and fourier transforms

Guangyi Chen; Tien Dai Bui; Adam Krzyzak; Yongjia Zhao

doi:10.1007/978-3-642-39065-4_78

Invariant object recognition using radon and fourier transforms

Guangyi Chen
, Tien Dai Bui
, Adam Krzyzak
, Yongjia Zhao

School of Automation Science and Electrical Engineering

Concordia University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, an invariant algorithm for object recognition is proposed by using the Radon and Fourier transforms. It has been shown that this algorithm is invariant to the translation and rotation of pattern images. The scaling invariance can be achieved by the standard normalization techniques. Our algorithm works even when the center of the pattern object is not aligned well. This advantage is because the Fourier spectra are invariant to spatial shift in the radial direction whereas existing methods assume the centroids are aligned exactly. Experimental results show that the proposed method is better than the Zernike's moments, the dual-tree complex wavelet (DTCWT) moments, and the auto-correlation wavelet moments for one aircraft database and one shape database.

Original language	English
Title of host publication	Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings
Publisher	Springer Verlag
Pages	650-656
Number of pages	7
Edition	PART 1
ISBN (Print)	9783642390647
DOIs	https://doi.org/10.1007/978-3-642-39065-4_78
State	Published - 2013
Event	10th International Symposium on Neural Networks, ISNN 2013 - Dalian, China Duration: 4 Jul 2013 → 6 Jul 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	7951 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Symposium on Neural Networks, ISNN 2013
Country/Territory	China
City	Dalian
Period	4/07/13 → 6/07/13

Keywords

Fourier transform
Gaussian white noise
object recognition
pattern recognition
Radon transform
Zernike's moments

Access to Document

10.1007/978-3-642-39065-4_78

Cite this

Chen, G., Bui, T. D., Krzyzak, A., & Zhao, Y. (2013). Invariant object recognition using radon and fourier transforms. In Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings (PART 1 ed., pp. 650-656). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7951 LNCS, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-642-39065-4_78

Chen, Guangyi ; Bui, Tien Dai ; Krzyzak, Adam et al. / Invariant object recognition using radon and fourier transforms. Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings. PART 1. ed. Springer Verlag, 2013. pp. 650-656 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "Invariant object recognition using radon and fourier transforms",

abstract = "In this paper, an invariant algorithm for object recognition is proposed by using the Radon and Fourier transforms. It has been shown that this algorithm is invariant to the translation and rotation of pattern images. The scaling invariance can be achieved by the standard normalization techniques. Our algorithm works even when the center of the pattern object is not aligned well. This advantage is because the Fourier spectra are invariant to spatial shift in the radial direction whereas existing methods assume the centroids are aligned exactly. Experimental results show that the proposed method is better than the Zernike's moments, the dual-tree complex wavelet (DTCWT) moments, and the auto-correlation wavelet moments for one aircraft database and one shape database.",

keywords = "Fourier transform, Gaussian white noise, object recognition, pattern recognition, Radon transform, Zernike's moments",

author = "Guangyi Chen and Bui, \{Tien Dai\} and Adam Krzyzak and Yongjia Zhao",

year = "2013",

doi = "10.1007/978-3-642-39065-4\_78",

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booktitle = "Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings",

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note = "10th International Symposium on Neural Networks, ISNN 2013 ; Conference date: 04-07-2013 Through 06-07-2013",

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Chen, G, Bui, TD, Krzyzak, A & Zhao, Y 2013, Invariant object recognition using radon and fourier transforms. in Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7951 LNCS, Springer Verlag, pp. 650-656, 10th International Symposium on Neural Networks, ISNN 2013, Dalian, China, 4/07/13. https://doi.org/10.1007/978-3-642-39065-4_78

Invariant object recognition using radon and fourier transforms. / Chen, Guangyi; Bui, Tien Dai; Krzyzak, Adam et al.
Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings. PART 1. ed. Springer Verlag, 2013. p. 650-656 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7951 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Invariant object recognition using radon and fourier transforms

AU - Chen, Guangyi

AU - Bui, Tien Dai

AU - Krzyzak, Adam

AU - Zhao, Yongjia

PY - 2013

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N2 - In this paper, an invariant algorithm for object recognition is proposed by using the Radon and Fourier transforms. It has been shown that this algorithm is invariant to the translation and rotation of pattern images. The scaling invariance can be achieved by the standard normalization techniques. Our algorithm works even when the center of the pattern object is not aligned well. This advantage is because the Fourier spectra are invariant to spatial shift in the radial direction whereas existing methods assume the centroids are aligned exactly. Experimental results show that the proposed method is better than the Zernike's moments, the dual-tree complex wavelet (DTCWT) moments, and the auto-correlation wavelet moments for one aircraft database and one shape database.

AB - In this paper, an invariant algorithm for object recognition is proposed by using the Radon and Fourier transforms. It has been shown that this algorithm is invariant to the translation and rotation of pattern images. The scaling invariance can be achieved by the standard normalization techniques. Our algorithm works even when the center of the pattern object is not aligned well. This advantage is because the Fourier spectra are invariant to spatial shift in the radial direction whereas existing methods assume the centroids are aligned exactly. Experimental results show that the proposed method is better than the Zernike's moments, the dual-tree complex wavelet (DTCWT) moments, and the auto-correlation wavelet moments for one aircraft database and one shape database.

KW - Fourier transform

KW - Gaussian white noise

KW - object recognition

KW - pattern recognition

KW - Radon transform

KW - Zernike's moments

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DO - 10.1007/978-3-642-39065-4_78

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings

PB - Springer Verlag

T2 - 10th International Symposium on Neural Networks, ISNN 2013

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ER -

Chen G, Bui TD, Krzyzak A, Zhao Y. Invariant object recognition using radon and fourier transforms. In Advances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings. PART 1 ed. Springer Verlag. 2013. p. 650-656. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-39065-4_78

Invariant object recognition using radon and fourier transforms

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