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http://dspace.unimap.edu.my:80/xmlui/handle/123456789/7429
Title: | 3D object recognition using MANFIS network with orthogonal and non-orthogonal moments |
Authors: | M. Khusairi, Osman Mohd Yusoff, Mashor M. Rizal, Arshad Zuraidi, Saad khusairi@ppinang.uitm.edu.my |
Keywords: | Zernike polynomials Computer vision Feature extraction Fuzzy control Object recognition MANFIS network |
Issue Date: | 6-Mar-2009 |
Publisher: | Institute of Electrical and Electronics Engineering (IEEE) |
Citation: | p.302-306 |
Series/Report no.: | Proceedings of the 5th International Colloquium on Signal Processing & Its Applications (CSPA 2009) |
Abstract: | This paper addresses a performance analysis of two well known moments, namely Hu's moments and Zernike's moments for 3D object recognition. Hu's moments and Zernike's moments are the non-orthogonal and orthogonal moments respectively, which are commonly used as shape feature for 2D object or pattern recognition. The current study proved that with some adaptation to multiple views technique, Hu and Zernike moments are sufficient to model 3D objects. In addition, the simplicity of moments calculation reduces the processing time for feature extraction, hence increases the system efficiency. In the recognition stage, we proposed to use a neuro-fuzzy classifier called multiple adaptive network based fuzzy inference system (MANFIS) for matching and classification. The proposed method has been tested using two groups of object, polyhedral and free-form objects. The experimental results show that Zernike moments combined with MANFIS network attain the best performance in both recognitions, polyhedral and free-form objects. |
Description: | Link to publisher's homepage at http://ieeexplore.ieee.org |
URI: | http://ieeexplore.ieee.org/xpls/abs_all.jsp?=&arnumber=5069239 http://dspace.unimap.edu.my/123456789/7429 |
ISBN: | 978-1-4244-4151-8 |
Appears in Collections: | Conference Papers Mohd Yusoff Mashor, Prof. Dr. |
Files in This Item:
File | Description | Size | Format | |
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Abstract.pdf | 7.51 kB | Adobe PDF | View/Open |
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