Kernel Two-Dimensional Nonnegative Matrix Factorization: A New Method to Target Detection for UUV Vision System
Kernel Two-Dimensional Nonnegative Matrix Factorization: A New Method to Target Detection for UUV Vision System
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核二维非负矩阵分解：UUV视觉系统目标检测的新方法
DOI：
10.1155/2020/9454261
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发表时间：
2020-01
期刊：
影响因子：
2.3
通讯作者：
Tianhao Jiang
中科院分区：
文献类型：
--
作者：
Jian Xu;Pengfei Bi;Xue Du;Juan Li;Tianhao Jiang
研究了一种先进的基于无人水下航行器（UUV）视觉系统的水下目标智能识别方法。该方法称为核二维非负矩阵分解（K2 DNMF），可进一步提高UUV视觉系统的水下作业能力。（1）K2 DNMF利用核方法对二维图像数据进行行、列方向的矩阵分解，将原始的低维非线性空间变换为高维线性空间;（2）在K2 DNMF方法中，通过对行基矩阵和列基矩阵的正交约束，可以得到对原始数据的良好子空间逼近;（3）利用列基矩阵和行基矩阵提取水下目标图像的特征信息，设计有效的分类器进行水下目标识别;（4）对UUV视觉系统采集的三组测试样品进行了一系列相关实验，实验结果表明，K2 DNMF方法比传统的水下目标识别方法具有更高的整体目标检测精度。
This paper studies an advanced intelligent recognition method of underwater target based on unmanned underwater vehicle (UUV) vision system. This method is called kernel two-dimensional nonnegative matrix factorization (K2DNMF) which can further improve underwater operation capability of the UUV vision system. Our contributions can be summarized as follows: (1) K2DNMF intends to use the kernel method for the matrix factorization both on the column and row directions of the two-dimensional image data in order to transform the original low-dimensional space with nonlinearity into a higher dimensional space with linearity; (2) In the K2DNMF method, a good subspace approximation to the original data can be obtained by the orthogonal constraint on column basis matrix and row basis matrix; (3) The column basis matrix and row basis matrix can extract the feature information of underwater target images, and an effective classifier is designed to perform underwater target recognition; (4) A series of related experiments were performed on three sets of test samples collected by the UUV vision system, the experimental results demonstrate that K2DNMF has higher overall target detection accuracy than the traditional underwater target recognition methods.
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影响因子：
2.1
作者：
Jong-Min Lee;S. Qin;In-Beum Lee
通讯作者：
Jong-Min Lee;S. Qin;In-Beum Lee
DOI：
10.1109/chicc.2015.7260249
发表时间：
2015-07
期刊：
2015 34th Chinese Control Conference (CCC)
影响因子：
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影响因子：
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作者：
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影响因子：
10.6
作者：
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通讯作者：
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影响因子：
1.6
作者：
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通讯作者：
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