Object Localization of Nonlinear Active Shape Model Based on Kernel PCA

XU Tao; SUN Tong

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Object Localization of Nonlinear Active Shape Model Based on Kernel PCA

更新时间：2026-09-04

- Object Localization of Nonlinear Active Shape Model Based on Kernel PCA
- Vol. 27, Issue 12, Pages: 113-116(2010)
- 作者机构：
  
  1. 中国民航大学机构计算机科学与技术学院,天津,300300
  2. 中国民航信息技术科研基地,天津,300300
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2010
- 稿件说明：
移动端阅览
徐涛, 孙彤. 基于KPCA的非线性ASM定位方法研究[J]. 微电子学与计算机, 2010, 27(12): 113-116.

XU Tao, SUN Tong. Object Localization of Nonlinear Active Shape Model Based on Kernel PCA[J]. 2010, 27(12): 113-116.
徐涛, 孙彤. 基于KPCA的非线性ASM定位方法研究[J]. 微电子学与计算机, 2010, 27(12): 113-116. DOI：

XU Tao, SUN Tong. Object Localization of Nonlinear Active Shape Model Based on Kernel PCA[J]. 2010, 27(12): 113-116. DOI：

摘要

针对主动形状模型 (Active Shape Model

ASM) 中的建模方法——主分量分析 (Principal Component Anal-ysis

PCA) 存在容易忽略像素间非线性关系等弱点

对基于ASM的图像中目标物体定位技术进行了非线性化的研究探索.通过引入核理论

提出了一种基于核主分量分析 (Kernel PCA

KPCA) 的非线性ASM定位方法

并运用多维空间距离关系原理解决了KPCA方法无法重构原空间图像模型的难题.实验证明

此方法能够有效地实现二维图像中的目标物体定位.

Abstract

In this paper

we address the problem of principal component analysis technique which is the modeling method of active shape model.Since the principal component analysis usually ignores nonlinear relativity among a large number of pixels

kernel method is combined to research object localization.A nonlinear active shape model based on kernel principal component analysis is presented in this paper.The proposed method reconstructs the pre-image in input space through the idea in multidimensional scaling.The validity of this method is demonstrated by the results of preliminary experiments.

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Institute of Microelectronics, Chinese Academy of Sciences

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