Human shape recognition using radon transform and regularized principal component

Author

Mahmud, A.R. ; Tahir, Nooritawati Md

Author_Institution

Fac. of Electr. Eng., Univ. Teknol. MARA, Shah Alam, Malaysia

fYear

2012

fDate

16-17 July 2012

Firstpage

298

Lastpage

302

Abstract

The aim of this study is to investigate the potential of Radon Transform and Regularized Principal Component Analysis as feature extraction for classification of human and non human. Several training algorithms are used for the neural network. The finding of the investigation shows that the best training algorithm is Lavenberg-Marquardt (LM). In addition, the execution time taken by LM is fastest among the training.The outcomes of the proposed method using LM are 0% False Rejection Rate (FRR) and 0% False Acceptance Rate (FAR)ona database of 100 images on each category.

Keywords

Radon transforms; feature extraction; image classification; principal component analysis; shape recognition; visual databases; FAR; FRR; Lavenberg-Marquardt algorithm; Radon transform; classification; false acceptance rate; false rejection rate; feature extraction; human shape recognition; image database; regularized principal component analysis; Classification algorithms; Feature extraction; Humans; Principal component analysis; Training; Transforms; Vehicles; Principal Component Analysis; Radon Transform; artificial neural network; classification rate; execution time; training algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and System Graduate Research Colloquium (ICSGRC), 2012 IEEE

Conference_Location

Shah Alam, Selangor

Print_ISBN

978-1-4673-2035-1

Type

conf

DOI

10.1109/ICSGRC.2012.6287180

Filename

6287180