Visual features extraction and types classification of seabed sediments
文献类型：会议论文
| 作者 | Li Y(李岩) ; Xia, Chunlei; Huang Y(黄琰); Ge LY(葛利亚); Tian Y(田宇)
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| 出版日期 | 2014 |
| 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 |
| 关键词 | Seabed sediments Underwater vehicle Visual features Fractal dimension Gray-level co-occurrence matrix SVMs |
| 页码 | 153-160 |
| 通讯作者 | 李岩 |
| 中文摘要 | 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. |
| 收录类别 | EI ; CPCI（ISTP） |
| 产权排序 | 1 |
| 会议主办者 | South China University of Technology, China |
| 会议录 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
 |
| 会议录出版者 | Springer Verlag |
| 会议录出版地 | Berlin |
| 语种 | 英语 |
| ISSN号 | 0302-9743 |
| ISBN号 | 978-3-319-13965-4 |
| WOS记录号 | WOS:000354872700015 |
| 源URL | [http://ir.sia.cn/handle/173321/15324]  |
| 专题 | 沈阳自动化研究所_水下机器人研究室 |
| 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]. 见:The 7th International Conference on Intelligent Robotics and Application (ICIRA2014). Guangzhou, China. December 17-20, 2014.. |
入库方式： OAI收割
来源：沈阳自动化研究所
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