Comparing three PCA-based methods for the 3D visualization of imaging spectroscopy data

被引：0

作者：

Broersen, A ^{[1
]}

van Liere, R ^{[1
]}

Heeren, RMA ^{[1
]}

机构：

[1] CWI, NL-1009 AB Amsterdam, Netherlands

来源：

PROCEEDINGS OF THE FIFTH IASTED INTERNATIONAL CONFERENCE ON VISUALIZATION, IMAGING, AND IMAGE PROCESSING | 2005年

关键词：

image processing and analysis; pattern analysis and recognition; transfer function; imaging spectroscopy; principal component analysis and multidimensional;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we compare the quality of three different principle component analysis (PCA) based methods to generate transfer functions for the 3D visualization of imaging spectroscopy data. We discuss three criteria for judging the quality of features in these visualizations. These criteria are used to interpret visualizations of features in the brain of the snail Lymnaea Stagnalis. We show that the PCA method that uses model additional information, clearly results in superior visualizations.

引用

页码：540 / 545

页数：6

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