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Mine like object detection and recognition based on intrackability and improved BOW
Yu SQ(余思泉); Shao, Jinxin; Han Z(韩志); Gao L(高雷); Lin Y(林扬); Tang YD(唐延东); Wu CD(吴成东)
作者部门机器人学研究室
会议名称6th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 2016
会议日期June 19-22, 2016
会议地点Chengdu, China
会议录名称6th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 2016
出版者IEEE
出版地Piscataway, NJ, USA
2016
页码222-227
收录类别EI ; CPCI(ISTP)
EI收录号20164302940167
WOS记录号WOS:000389835200042
产权排序1
ISSN号2379-7711
ISBN号978-1-5090-2732-3
摘要In this paper, we present an automatic system of mine like object detection and recognition for sonar videos. This system is implemented with two main methods. One is the object detection and segmentation with intrackability, another is object recognition of mine like based on improved BOW algorithm and Support Vector Machine (SVM). Intrackability is defined by the concept of entropy, and can reflect the difficulty and uncertainty in tracking certain elements on the time axis. Therefore, our segmentation and detection method can effectively eliminate complex noise in sonar image to guarantee the more accurate object segmentation and detection. In our recognition method of mine like object, we use an improved BOW and SVM to implement the more accurate recognition for mine like objects. In the method, an improved BOW algorithm is utilized for image feature extraction, due to that it can represent local and global feature of image in a more comprehensive way; and then the object recognition is implemented with SVM. Our extensive experiments show that our system can accurately detect and recognize mine like objects in real-time.
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/173321/19304
专题机器人学研究室
通讯作者Han Z(韩志)
作者单位1.School of Information Science and Engineering, Northeastern University, Shenyang, 110819, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
推荐引用方式
GB/T 7714
Yu SQ,Shao, Jinxin,Han Z,et al. Mine like object detection and recognition based on intrackability and improved BOW[C]. Piscataway, NJ, USA:IEEE,2016:222-227.
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