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A Novel Visual Classification Method of Seabed Sediments
Li Y(李岩); Xia, Chunlei; Zhu PQ(祝普强); Huang Y(黄琰); Ge LY(葛利亚)
作者部门水下机器人研究室
会议名称OCEANS'14 MTS/IEEE St. John's
会议日期September 14-19, 2014
会议地点St. John's, Canada
会议录名称OCEANS'14 MTS/IEEE St. John's
出版者IEEE
出版地Piscataway, NJ, USA
2014
页码1-4
收录类别EI ; CPCI(ISTP)
EI收录号20150500475708
WOS记录号WOS:000369848800038
产权排序1
ISBN号978-1-4799-4918-2
关键词Seabed Images Fractal Dimension Gray-level Cooccurrence Matrix Self-organizing Map Underwater Vehicle Robot Vision
摘要This study aims at the autonomous seafloor surveillance by underwater vehicles based on computer vision techniques. A novel scheme of seabed image classification is proposed to identify three types of seabed sediments. The texture features of seabed sediments were described by using gray-level co-occurrence matrix and fractal dimension. Subsequently, an unsupervised learning method, Self-Organizing Map, was applied to analyze the seabed images with the extracted texture features. The experimental results demonstrated that the proposed texture feature descriptors were feasible and effective to category the three types of seabed images.
语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/15307
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.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,Zhu PQ,et al. A Novel Visual Classification Method of Seabed Sediments[C]. Piscataway, NJ, USA:IEEE,2014:1-4.
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