TY  - CONF
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020695824&doi=10.1109%2fCEIT.2016.7929021&partnerID=40&md5=b6564185cf6d333d835b87ebbfa711aa
PB  - Institute of Electrical and Electronics Engineers Inc.
N2  - 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.
KW  - Particle swarm optimization (PSO)
KW  -  3D shape recognition; Intelligent recognition; Kernel signatures; Modified particle swarm optimization; Modified particle swarm optimizations (MPSO); Shape matching; Shape recognition; Topological deformation
KW  -  Robustness (control systems)
N1  - cited By 1; Conference of 4th International Conference on Control Engineering and Information Technology, CEIT 2016 ; Conference Date: 16 December 2016 Through 18 December 2016; Conference Code:127844
A1  - Naffouti, S.E.
A1  - Aouissaoui, I.
A1  - Fougerolle, Y.
A1  - Sakly, A.
A1  - Meriaudeau, F.
Y1  - 2017///
SN  - 9781509010554
TI  - Enhancement and assessment of WKS variance parameter for intelligent 3D shape recognition and matching based on MPSO
ID  - scholars8665
ER  -