Three-dimensional object recognition from two-dimensional images using wavelet transforms and neural networks

被引：12

作者：

Deschenes, S ^{[1
]}

Sheng, YL

Chevrette, PC

机构：

[1] Univ Laval, Dept Phys, Ctr Opt Photon & Laser, Quebec City, PQ G1K 7P4, Canada

[2] Def Res Estab Valcartier, Quebec City, PQ, Canada

来源：

OPTICAL ENGINEERING | 1998年 / 37卷 / 03期

关键词：

three-dimensional object recognition; wavelet transforms; noise clearing; neural networks;

D O I：

10.1117/1.601908

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Three-dimensional object classification from 2-D IR images is shown. The wavelet transform is used for edge detection. Edge tracking is used for removing noise effectively in the wavelet transform. The invariant Fourier descriptor is used to describe the contour curves. Invariance under out-of-plane rotation is achieved by the feature space trajectory neural network working as a classifier. (C) 1998 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(98)01203-3].

引用

页码：763 / 770

页数：8

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