DocumentCode :
353352
Title :
Mesh construction with fast soft vector quantisation
Author :
Borghese, N. Alberto ; Ferrari, Stefano
Author_Institution :
Lab. of Human Motion Anal. & Virtual Reality, CNR, Milano, Italy
Volume :
5
fYear :
2000
fDate :
2000
Firstpage :
473
Abstract :
In this paper a method to accelerate soft vector quantisation (VQ), making it a quasi-real time procedure, is described. Through the local analysis of the data density a criterion to set a reasonable value of the parameters and to initialise the position of the reference vectors (hyper-box preprocessing), allows to cut about 75% of the iterations and to make the computational cost of each iteration constant, independent of the number of sampled points. Moreover, it makes soft VQ of possible implementation on parallel machines. Overall the processing time with hyper-box pre-processing can be brought down to 3%. This method, in conjunction with Delaunay tessellation, has been extensively applied to the construction of 3D triangular meshes from dense noisy data. Results on the reconstruction of 3D models of human faces are reported and discussed
Keywords :
computational complexity; mesh generation; neural nets; noise; vector quantisation; 3D model reconstruction; 3D triangular meshes; Delaunay tessellation; computational cost; dense noisy data; fast soft vector quantisation; human faces; hyper-box preprocessing; mesh construction; neural nets; parallel implementation; quasi-real-time procedure; reference vector position initialisation; soft VQ; Acceleration; Annealing; Computational efficiency; Data analysis; Humans; Image reconstruction; Laboratories; Motion analysis; Vector quantization; Virtual reality;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
ISSN :
1098-7576
Print_ISBN :
0-7695-0619-4
Type :
conf
DOI :
10.1109/IJCNN.2000.861514
Filename :
861514
Link To Document :
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