A kernel-based 3D object recognition neural network method

被引：0
|
作者：
Zhao, LW [1 ]
Luo, SW [1 ]
Huang, YP [1 ]
机构：
[1] Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China
来源：
WAVELET ANALYSIS AND ITS APPLICATIONS, AND ACTIVE MEDIA TECHNOLOGY, VOLS 1 AND 2 | 2004年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents results on appearance-based three-dimensional object recognition (3DOR) accomplished by utilizing a neural network architecture developed based on Kernel Principal Component Analysis (KPCA). The basic idea of KPCA is first map the input space into a feature space via nonlinear mapping and then compute the principal component in the feature space. In this paper, we are utilizing the KPCA technique to enhance the object recognition. Through adopting a polynomial kernel, the principal component can be computed in the space spanned by high-order correlations of input pixels. The imitate results confirmed the effectiveness of the proposed method.
引用
收藏
页码：783 / 788
页数：6
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