@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",
}