.png){kind=icon}
返回
Automatic masking in multivariate image analysis using support vector machines
.png){kind=icon}
PRE
AI
DOI：10.1016/j.chemolab.2005.03.004.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
lumber grading
multivariate image analysis
multidimensional masks
hyperspectral images
near infra red images
pattern recognition
support vector machines
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.8
论文数:
4.6K
被引数:
1.2W
机构
暂无机构信息
引用论文
.png){kind=icon}
A flexible classification approach with optimal generalisation performance: support vector machines
PREAI
分享收藏A dynamic programming approach to missing data estimation using neural networks使用神经网络进行缺失数据估计的动态规划方法
PREAI
分享收藏分享收藏 没有更多内容
没有更多内容