A Feature Points Extraction Algorithm Based on Adaptive Information Entropy
Feature points loss and images mismatch in the variation of light intensity, weak texture and large angle rotation for the feature points extraction of ORB-SLAM2 are severe. To deal with the problem, a feature points extraction algorithm based on adaptive information entropy, i.e., Adaptive Informat...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9138375/ |
| _version_ | 1818566845855170560 |
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| author | Dan Yin Siwei Zhou Pengcheng Wang Manling Lin Hui Song Feng Ke Kaiqing Luo |
| author_facet | Dan Yin Siwei Zhou Pengcheng Wang Manling Lin Hui Song Feng Ke Kaiqing Luo |
| author_sort | Dan Yin |
| collection | DOAJ |
| description | Feature points loss and images mismatch in the variation of light intensity, weak texture and large angle rotation for the feature points extraction of ORB-SLAM2 are severe. To deal with the problem, a feature points extraction algorithm based on adaptive information entropy, i.e., Adaptive Information Entropy Feature (AIEF) algorithm is proposed. According to the information entropy, the image blocks with less information are removed and those with more texture image information and larger gradient are selected. Then an adaptive algorithm is used to automatically calculate the optimal threshold of the image information entropy. The image blocks are homogenized to avoid that the extracted feature points are too dense and getting stuck is prevented, which makes the algorithm more robust. Finaly validation is performed using the Oxford standard data set and the performances of the AIEF algorithm are compared with those of the SIFT, SURF, and ORB-SLAM2 algorithms. Experimental results on the Oxford standard data set demonstrate that the AIEF algorithm outperforms the traditional counterparts in terms of processing time, number of feature points, correct matching number and correct matching rate. |
| first_indexed | 2024-12-14T01:58:50Z |
| format | Article |
| id | doaj.art-e5a893d1fc1248a78187023e782ac500 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T01:58:50Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e5a893d1fc1248a78187023e782ac5002022-12-21T23:21:06ZengIEEEIEEE Access2169-35362020-01-01812713412714110.1109/ACCESS.2020.30084579138375A Feature Points Extraction Algorithm Based on Adaptive Information EntropyDan Yin0https://orcid.org/0000-0003-3680-5997Siwei Zhou1https://orcid.org/0000-0001-5301-7365Pengcheng Wang2https://orcid.org/0000-0001-5398-5956Manling Lin3https://orcid.org/0000-0002-0367-2123Hui Song4https://orcid.org/0000-0002-0458-5136Feng Ke5https://orcid.org/0000-0003-0043-1655Kaiqing Luo6https://orcid.org/0000-0002-6278-0917School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, ChinaSchool of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, ChinaSchool of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, ChinaSchool of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, ChinaSchool of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, ChinaSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou, ChinaSchool of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, ChinaFeature points loss and images mismatch in the variation of light intensity, weak texture and large angle rotation for the feature points extraction of ORB-SLAM2 are severe. To deal with the problem, a feature points extraction algorithm based on adaptive information entropy, i.e., Adaptive Information Entropy Feature (AIEF) algorithm is proposed. According to the information entropy, the image blocks with less information are removed and those with more texture image information and larger gradient are selected. Then an adaptive algorithm is used to automatically calculate the optimal threshold of the image information entropy. The image blocks are homogenized to avoid that the extracted feature points are too dense and getting stuck is prevented, which makes the algorithm more robust. Finaly validation is performed using the Oxford standard data set and the performances of the AIEF algorithm are compared with those of the SIFT, SURF, and ORB-SLAM2 algorithms. Experimental results on the Oxford standard data set demonstrate that the AIEF algorithm outperforms the traditional counterparts in terms of processing time, number of feature points, correct matching number and correct matching rate.https://ieeexplore.ieee.org/document/9138375/Adaptive algorithminformation entropyimage matchingfeature extraction |
| spellingShingle | Dan Yin Siwei Zhou Pengcheng Wang Manling Lin Hui Song Feng Ke Kaiqing Luo A Feature Points Extraction Algorithm Based on Adaptive Information Entropy IEEE Access Adaptive algorithm information entropy image matching feature extraction |
| title | A Feature Points Extraction Algorithm Based on Adaptive Information Entropy |
| title_full | A Feature Points Extraction Algorithm Based on Adaptive Information Entropy |
| title_fullStr | A Feature Points Extraction Algorithm Based on Adaptive Information Entropy |
| title_full_unstemmed | A Feature Points Extraction Algorithm Based on Adaptive Information Entropy |
| title_short | A Feature Points Extraction Algorithm Based on Adaptive Information Entropy |
| title_sort | feature points extraction algorithm based on adaptive information entropy |
| topic | Adaptive algorithm information entropy image matching feature extraction |
| url | https://ieeexplore.ieee.org/document/9138375/ |
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