Research on Feature Extraction Method of Indoor Visual Positioning Image Based on Area Division of Foreground and Background
In the process of indoor visual positioning and navigation, difficult points often exist in corridors, stairwells, and other scenes that contain large areas of white walls, strong consistent background, and sparse feature points. Aiming at the problem of positioning and navigation in the real physic...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/10/6/402 |
| _version_ | 1797530560493518848 |
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| author | Ping Zheng Danyang Qin Bing Han Lin Ma Teklu Merhawit Berhane |
| author_facet | Ping Zheng Danyang Qin Bing Han Lin Ma Teklu Merhawit Berhane |
| author_sort | Ping Zheng |
| collection | DOAJ |
| description | In the process of indoor visual positioning and navigation, difficult points often exist in corridors, stairwells, and other scenes that contain large areas of white walls, strong consistent background, and sparse feature points. Aiming at the problem of positioning and navigation in the real physical world where the walls with sparse feature points are difficult to be filled with pictures, this paper designs a feature extraction method, ARAC (Adaptive Region Adjustment based on Consistency) using Free and Open-Source Software and tools. It divides the image into foreground and background and extracts their features respectively, to achieve not only retain positioning information but also focus more energy on the foreground area which is favourable for navigation. In the test phase, under the combined conditions of illumination, scale and affine changes, the feature matching maps by the feature extraction algorithm proposed in this paper are compared with those by SIFT and SURF. Experiments show that the number of correctly matched feature pairs obtained by ARAC is better than SIFT and SURF, and whose time of feature extraction and matching is comparable to SURF, which verifies the accuracy and efficiency of the ARAC feature extraction method. |
| first_indexed | 2024-03-10T10:30:45Z |
| format | Article |
| id | doaj.art-e3ec5434144c439a81b808863dfbbf0a |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T10:30:45Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-e3ec5434144c439a81b808863dfbbf0a2023-11-21T23:43:18ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-06-0110640210.3390/ijgi10060402Research on Feature Extraction Method of Indoor Visual Positioning Image Based on Area Division of Foreground and BackgroundPing Zheng0Danyang Qin1Bing Han2Lin Ma3Teklu Merhawit Berhane4Department of Electronic and Communication Engineering, Heilongjiang University, Harbin 150080, ChinaDepartment of Electronic and Communication Engineering, Heilongjiang University, Harbin 150080, ChinaDepartment of Electronic and Communication Engineering, Heilongjiang University, Harbin 150080, ChinaDepartment of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, ChinaDepartment of Computer and Information Sciences, Dire-Dawa Institute of Technology, Dire Dawa 3000, EthiopiaIn the process of indoor visual positioning and navigation, difficult points often exist in corridors, stairwells, and other scenes that contain large areas of white walls, strong consistent background, and sparse feature points. Aiming at the problem of positioning and navigation in the real physical world where the walls with sparse feature points are difficult to be filled with pictures, this paper designs a feature extraction method, ARAC (Adaptive Region Adjustment based on Consistency) using Free and Open-Source Software and tools. It divides the image into foreground and background and extracts their features respectively, to achieve not only retain positioning information but also focus more energy on the foreground area which is favourable for navigation. In the test phase, under the combined conditions of illumination, scale and affine changes, the feature matching maps by the feature extraction algorithm proposed in this paper are compared with those by SIFT and SURF. Experiments show that the number of correctly matched feature pairs obtained by ARAC is better than SIFT and SURF, and whose time of feature extraction and matching is comparable to SURF, which verifies the accuracy and efficiency of the ARAC feature extraction method.https://www.mdpi.com/2220-9964/10/6/402visual positioningindoor navigationfeature extractionfeature matchingFree and Open-Source Software |
| spellingShingle | Ping Zheng Danyang Qin Bing Han Lin Ma Teklu Merhawit Berhane Research on Feature Extraction Method of Indoor Visual Positioning Image Based on Area Division of Foreground and Background ISPRS International Journal of Geo-Information visual positioning indoor navigation feature extraction feature matching Free and Open-Source Software |
| title | Research on Feature Extraction Method of Indoor Visual Positioning Image Based on Area Division of Foreground and Background |
| title_full | Research on Feature Extraction Method of Indoor Visual Positioning Image Based on Area Division of Foreground and Background |
| title_fullStr | Research on Feature Extraction Method of Indoor Visual Positioning Image Based on Area Division of Foreground and Background |
| title_full_unstemmed | Research on Feature Extraction Method of Indoor Visual Positioning Image Based on Area Division of Foreground and Background |
| title_short | Research on Feature Extraction Method of Indoor Visual Positioning Image Based on Area Division of Foreground and Background |
| title_sort | research on feature extraction method of indoor visual positioning image based on area division of foreground and background |
| topic | visual positioning indoor navigation feature extraction feature matching Free and Open-Source Software |
| url | https://www.mdpi.com/2220-9964/10/6/402 |
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