Destek vektör makinesi kullanarak baǧimsiz bileşen tabanli 3B nesne tanima

O. G. Sezer; A. Erçil; M. Keskinöz

doi:10.1109/SIU.2005.1567629

Destek vektör makinesi kullanarak baǧimsiz bileşen tabanli 3B nesne tanima

Translated title of the contribution: Independent component based 3D object recognition using support vector machines

O. G. Sezer^*
, A. Erçil
, M. Keskinöz

^*Corresponding author for this work

Sabanci University

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

1 Citation (Scopus)

Abstract

In this paper, we propose an object recognition technique using higher order statistics without the combinatorial explosion of time and memory complexity. The proposed technique is a fusion of two popular algorithms in the literature, Independent Component Analysis (ICA) and Support Vector Machines (SVM). We propose to use ICA to reduce the redundancy in the images and obtain some feature vectors for every image which has lower dimensions and then make use of SVM to classify these feature vectors coming from the ICA step. Experimental results are shown for Coil-20 and an internally created database of 2D manufacturing objects.

Translated title of the contribution	Independent component based 3D object recognition using support vector machines
Original language	Turkish
Title of host publication	Proceedings of the IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005
Pages	99-102
Number of pages	4
DOIs	https://doi.org/10.1109/SIU.2005.1567629
Publication status	Published - 2005
Externally published	Yes
Event	IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005 - Kayseri, Turkey Duration: 16 May 2005 → 18 May 2005

Publication series

Name	Proceedings of the IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005
Volume	2005

Conference

Conference	IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005
Country/Territory	Turkey
City	Kayseri
Period	16/05/05 → 18/05/05

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Access to Document

10.1109/SIU.2005.1567629

Cite this

Sezer, O. G., Erçil, A., & Keskinöz, M. (2005). Destek vektör makinesi kullanarak baǧimsiz bileşen tabanli 3B nesne tanima. In Proceedings of the IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005 (pp. 99-102). Article 1567629 (Proceedings of the IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005; Vol. 2005). https://doi.org/10.1109/SIU.2005.1567629

@inproceedings{bfdf1562563f4567bb34410685cb6650,

title = "Destek vekt{\"o}r makinesi kullanarak baǧimsiz bile{\c s}en tabanli 3B nesne tanima",

abstract = "In this paper, we propose an object recognition technique using higher order statistics without the combinatorial explosion of time and memory complexity. The proposed technique is a fusion of two popular algorithms in the literature, Independent Component Analysis (ICA) and Support Vector Machines (SVM). We propose to use ICA to reduce the redundancy in the images and obtain some feature vectors for every image which has lower dimensions and then make use of SVM to classify these feature vectors coming from the ICA step. Experimental results are shown for Coil-20 and an internally created database of 2D manufacturing objects.",

author = "Sezer, \{O. G.\} and A. Er{\c c}il and M. Keskin{\"o}z",

year = "2005",

doi = "10.1109/SIU.2005.1567629",

language = "T{\"u}rk{\c c}e",

isbn = "0780392396",

series = "Proceedings of the IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005",

pages = "99--102",

booktitle = "Proceedings of the IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005",

note = "IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005 ; Conference date: 16-05-2005 Through 18-05-2005",

}

Sezer, OG, Erçil, A & Keskinöz, M 2005, Destek vektör makinesi kullanarak baǧimsiz bileşen tabanli 3B nesne tanima. in Proceedings of the IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005., 1567629, Proceedings of the IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005, vol. 2005, pp. 99-102, IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005, Kayseri, Turkey, 16/05/05. https://doi.org/10.1109/SIU.2005.1567629

Destek vektör makinesi kullanarak baǧimsiz bileşen tabanli 3B nesne tanima. / Sezer, O. G.; Erçil, A.; Keskinöz, M.
Proceedings of the IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005. 2005. p. 99-102 1567629 (Proceedings of the IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005; Vol. 2005).

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

TY - GEN

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AU - Sezer, O. G.

AU - Erçil, A.

AU - Keskinöz, M.

PY - 2005

Y1 - 2005

N2 - In this paper, we propose an object recognition technique using higher order statistics without the combinatorial explosion of time and memory complexity. The proposed technique is a fusion of two popular algorithms in the literature, Independent Component Analysis (ICA) and Support Vector Machines (SVM). We propose to use ICA to reduce the redundancy in the images and obtain some feature vectors for every image which has lower dimensions and then make use of SVM to classify these feature vectors coming from the ICA step. Experimental results are shown for Coil-20 and an internally created database of 2D manufacturing objects.

AB - In this paper, we propose an object recognition technique using higher order statistics without the combinatorial explosion of time and memory complexity. The proposed technique is a fusion of two popular algorithms in the literature, Independent Component Analysis (ICA) and Support Vector Machines (SVM). We propose to use ICA to reduce the redundancy in the images and obtain some feature vectors for every image which has lower dimensions and then make use of SVM to classify these feature vectors coming from the ICA step. Experimental results are shown for Coil-20 and an internally created database of 2D manufacturing objects.

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U2 - 10.1109/SIU.2005.1567629

DO - 10.1109/SIU.2005.1567629

M3 - Konferans katkısı

AN - SCOPUS:33846565474

SN - 0780392396

SN - 9780780392397

T3 - Proceedings of the IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005

SP - 99

EP - 102

BT - Proceedings of the IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005

T2 - IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005

Y2 - 16 May 2005 through 18 May 2005

ER -

Sezer OG, Erçil A, Keskinöz M. Destek vektör makinesi kullanarak baǧimsiz bileşen tabanli 3B nesne tanima. In Proceedings of the IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005. 2005. p. 99-102. 1567629. (Proceedings of the IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005). doi: 10.1109/SIU.2005.1567629

Destek vektör makinesi kullanarak baǧimsiz bileşen tabanli 3B nesne tanima

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