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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)
Department水下机器人研究室
Conference NameOCEANS'14 MTS/IEEE St. John's
Conference DateSeptember 14-19, 2014
Conference PlaceSt. John's, Canada
Source PublicationOCEANS'14 MTS/IEEE St. John's
PublisherIEEE
Publication PlacePiscataway, NJ, USA
2014
Pages1-4
Indexed ByEI ; CPCI(ISTP)
EI Accession number20150500475708
WOS IDWOS:000369848800038
Contribution Rank1
ISBN978-1-4799-4918-2
KeywordSeabed Images Fractal Dimension Gray-level Cooccurrence Matrix Self-organizing Map Underwater Vehicle Robot Vision
AbstractThis 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.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/15307
Collection水下机器人研究室
Corresponding AuthorLi Y(李岩)
Affiliation1.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
Recommended Citation
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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