Retracted: Target Recognition Method of Rehabilitation Robot Based on Image Local Features
Target recognition technology is an important topic in the field of artificial intelligence, which is widely used in fields such as medicine, robot vision, highway traffic, and VR technology. In the robot vision system, the target recognition technology requires high precision, real-time, and practi...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Online Access: | https://ieeexplore.ieee.org/document/9184007/ |
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| _version_ | 1850085761888550912 |
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| author | Xing Li Tianbao Wu |
| author_facet | Xing Li Tianbao Wu |
| author_sort | Xing Li |
| collection | DOAJ |
| description | Target recognition technology is an important topic in the field of artificial intelligence, which is widely used in fields such as medicine, robot vision, highway traffic, and VR technology. In the robot vision system, the target recognition technology requires high precision, real-time, and practicality, and it must be able to cope with the harsh recognition environment on site. This paper mainly studies the target recognition method of rehabilitation robot based on image local features. In this paper, a rehabilitation robot recognition system is established based on the local features of the image and the target recognition technology. Firstly, after collecting or processing the initially prepared images, feature extraction is performed, and the feature vectors extracted from the features are classified and recognized and then transferred to the recognition model of the rehabilitation robot. Then, according to the functional requirements of the rehabilitation robot recognition system, the “one-to-many” rehabilitation mode and real-time system monitoring are realized to accurately identify the target state. Finally, the Harris algorithm is used to convert the predetermined image set into a grayscale image, and the Gaussian distribution is used uniformly to determine the location and proportion of the feature points, and extract the corresponding image. The Harris matching method used in this article is very effective and has the highest accuracy. The number of correct matches accounted for 58.3%. Compared with the SIFT algorithm and the Harris+SIFT algorithm, the accuracy rate increases by 12.3% and 14.4%, respectively. The recall rate increased by 13.2% and 8.9% respectively. The experimental results show that the target recognition technology of rehabilitation robot based on image local features is more accurate than general technology. |
| format | Article |
| id | doaj-art-9ffa4d3757844adca8867dcf20155e55 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9ffa4d3757844adca8867dcf20155e552025-08-20T02:43:38ZengIEEEIEEE Access2169-35362020-01-01816060716061510.1109/ACCESS.2020.30208799184007Retracted: Target Recognition Method of Rehabilitation Robot Based on Image Local FeaturesXing Li0Tianbao Wu1https://orcid.org/0000-0001-6377-6381School of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan, ChinaSchool of Information Science and Technology, Xiamen University Tan Kah Kee College, Zhangzhou, ChinaTarget recognition technology is an important topic in the field of artificial intelligence, which is widely used in fields such as medicine, robot vision, highway traffic, and VR technology. In the robot vision system, the target recognition technology requires high precision, real-time, and practicality, and it must be able to cope with the harsh recognition environment on site. This paper mainly studies the target recognition method of rehabilitation robot based on image local features. In this paper, a rehabilitation robot recognition system is established based on the local features of the image and the target recognition technology. Firstly, after collecting or processing the initially prepared images, feature extraction is performed, and the feature vectors extracted from the features are classified and recognized and then transferred to the recognition model of the rehabilitation robot. Then, according to the functional requirements of the rehabilitation robot recognition system, the “one-to-many” rehabilitation mode and real-time system monitoring are realized to accurately identify the target state. Finally, the Harris algorithm is used to convert the predetermined image set into a grayscale image, and the Gaussian distribution is used uniformly to determine the location and proportion of the feature points, and extract the corresponding image. The Harris matching method used in this article is very effective and has the highest accuracy. The number of correct matches accounted for 58.3%. Compared with the SIFT algorithm and the Harris+SIFT algorithm, the accuracy rate increases by 12.3% and 14.4%, respectively. The recall rate increased by 13.2% and 8.9% respectively. The experimental results show that the target recognition technology of rehabilitation robot based on image local features is more accurate than general technology.https://ieeexplore.ieee.org/document/9184007/ |
| spellingShingle | Xing Li Tianbao Wu Retracted: Target Recognition Method of Rehabilitation Robot Based on Image Local Features IEEE Access |
| title | Retracted: Target Recognition Method of Rehabilitation Robot Based on Image Local Features |
| title_full | Retracted: Target Recognition Method of Rehabilitation Robot Based on Image Local Features |
| title_fullStr | Retracted: Target Recognition Method of Rehabilitation Robot Based on Image Local Features |
| title_full_unstemmed | Retracted: Target Recognition Method of Rehabilitation Robot Based on Image Local Features |
| title_short | Retracted: Target Recognition Method of Rehabilitation Robot Based on Image Local Features |
| title_sort | retracted target recognition method of rehabilitation robot based on image local features |
| url | https://ieeexplore.ieee.org/document/9184007/ |
| work_keys_str_mv | AT xingli retractedtargetrecognitionmethodofrehabilitationrobotbasedonimagelocalfeatures AT tianbaowu retractedtargetrecognitionmethodofrehabilitationrobotbasedonimagelocalfeatures |