Enhancement and assessment of WKS variance parameter for intelligent 3D shape recognition and matching based on MPSO
This paper presents an improved wave kernel signature (WKS) using the modified particle swarm optimization (MPSO)-based intelligent recognition and matching on 3D shapes. We select the first feature vector from WKS, which represents the 3D shape over the first energy scale. The choice of this vector...
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| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2017
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020695824&doi=10.1109%2fCEIT.2016.7929021&partnerID=40&md5=b6564185cf6d333d835b87ebbfa711aa http://eprints.utp.edu.my/20087/ |
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| Institution: | Universiti Teknologi Petronas |
| Summary: | This paper presents an improved wave kernel signature (WKS) using the modified particle swarm optimization (MPSO)-based intelligent recognition and matching on 3D shapes. We select the first feature vector from WKS, which represents the 3D shape over the first energy scale. The choice of this vector is to reinforce robustness against non-rigid 3D shapes. Furthermore, an optimized WKS-based method for extracting key-points from objects is introduced. Due to its discriminative power, the associated optimized WKS values with each point remain extremely stable, which allows for efficient salient features extraction. To assert our method regarding its robustness against topological deformations, experiments show that the method is discriminative and robust to data perturbed by various noises. The algorithm is evaluated by its capability to differentiate between the salient feature points and to match efficiently between similar geometric structures for the same shape in different poses. © 2016 IEEE. |
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