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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)
Department机器人学研究室
Conference Name6th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 2016
Conference DateJune 19-22, 2016
Conference PlaceChengdu, China
Source Publication6th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, IEEE-CYBER 2016
PublisherIEEE
Publication PlacePiscataway, NJ, USA
2016
Pages222-227
Indexed ByEI ; CPCI(ISTP)
EI Accession number20164302940167
WOS IDWOS:000389835200042
Contribution Rank1
ISSN2379-7711
ISBN978-1-5090-2732-3
AbstractIn 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.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/19304
Collection机器人学研究室
Corresponding AuthorHan Z(韩志)
Affiliation1.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
Recommended Citation
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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