Automatic masking in multivariate image analysis using support vector machines

被引：20
|
作者：
Liu, JJ [1 ]
Bharati, MH [1 ]
Dunn, KG [1 ]
MacGregor, JF [1 ]
机构：
[1] McMaster Univ, Dept Chem Engn, Hamilton, ON L8S 4L7, Canada
来源：
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2005年 / 79卷 / 1-2期
关键词：
lumber grading; multivariate image analysis; multidimensional masks; hyperspectral images; near infra red images; pattern recognition; support vector machines;
D O I：
10.1016/j.chemolab.2005.03.004
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A new masking method for Multivariate image Analysis (MIA) is proposed. By interpreting the masking process in MIA as a classification problem it is possible to find masks automatically using any classification method and it is shown that Support Vector Machines (SVMs) are a good candidate for this purpose. An easy to use, iterative mask-building scheme, based on SVMs, is presented and used to find multidimensional masks for selected features in the Near Infra Red (NIR) imaging of lumber. Although the automated masking procedure can be used to obtain better masks in two-dimensional score spaces, typically obtained from RGB images, the real value of the procedure lies in its ability to easily obtain better-performing multidimensional masks from hyperspectral images. (c) 2005 Elsevier B.V. All rights reserved.
引用
收藏
页码：42 / 54
页数：13
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