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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)
Department水下机器人研究室
Conference NameThe 7th International Conference on Intelligent Robotics and Application (ICIRA2014)
Conference DateDecember 17-20, 2014.
Conference PlaceGuangzhou, China
Author of SourceSouth China University of Technology, China
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Publication PlaceBerlin
2014
Pages153-160
Indexed ByEI ; CPCI(ISTP)
EI Accession number20144800259764
WOS IDWOS:000354872700015
Contribution Rank1
ISSN0302-9743
ISBN978-3-319-13965-4
KeywordSeabed Sediments Underwater Vehicle Visual Features Fractal Dimension Gray-level Co-occurrence Matrix Svms
AbstractThe 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.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/15324
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.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
Recommended Citation
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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