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Visual features extraction and types classification of seabed sediments
Li Y(李岩); Xia, Chunlei; Huang Y(黄琰); Ge LY(葛利亚); Tian Y(田宇)
作者部门水下机器人研究室
会议名称The 7th International Conference on Intelligent Robotics and Application (ICIRA2014)
会议日期December 17-20, 2014.
会议地点Guangzhou, China
会议主办者South China University of Technology, China
会议录名称Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
出版者Springer Verlag
出版地Berlin
2014
页码153-160
收录类别EI ; CPCI(ISTP)
EI收录号20144800259764
WOS记录号WOS:000354872700015
产权排序1
ISSN号0302-9743
ISBN号978-3-319-13965-4
关键词Seabed Sediments Underwater Vehicle Visual Features Fractal Dimension Gray-level Co-occurrence Matrix Svms
摘要The purpose of this research is to define and extract the visual features of the seabed sediments to improve the autonomous ability of a underwater vehicle while implementing exploring missions. A scheme of seabed image classification is proposed to identify three types of seabed sediments. The texture features of images are stable and robust visual features in underwater environment comparing with general visual features, and which are described by using gray-level co-occurrence matrix and fractal dimension. Subsequently, for purpose of evaluation, a supervised non-parametric statistical learning technique, support vector machines (SVMs), is applied to verify the availability of extracted texture features on seabed sediments classification. The presented results of seabed type recognition justify the proposed features extracted method valid to seabed type recognition.
语种英语
引用统计
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/15324
专题水下机器人研究室
通讯作者Li Y(李岩)
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation, CAS, Shenyang, China
2.The Research Center of Coastal Environmental Engineering and Technology of Shandong Province, Yantai Institute of Coastal Zone Research, CAS, Yantai, China
3.University of Chinese Academy of Sciences, Beijing, China
推荐引用方式
GB/T 7714
Li Y,Xia, Chunlei,Huang Y,et al. Visual features extraction and types classification of seabed sediments[C]//South China University of Technology, China. Berlin:Springer Verlag,2014:153-160.
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