{"title":"相似图像匹配中的异常值抑制","authors":"Qingqing Chen , Junfeng Yao","doi":"10.1016/j.vrih.2023.02.004","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Image matching is crucial in numerous computer vision tasks such as 3D reconstruction and simultaneous visual localization and mapping. The accuracy of the matching significantly impacted subsequent studies. Because of their local similarity, when image pairs contain comparable patterns but feature pairs are positioned differently, incorrect recognition can occur as global motion consistency is disregarded.</p></div><div><h3>Methods</h3><p>This study proposes an image-matching filtering algorithm based on global motion consistency. It can be used as a subsequent matching filter for the initial matching results generated by other matching algorithms based on the principle of motion smoothness. A particular matching algorithm can first be used to perform the initial matching; then, the rotation and movement information of the global feature vectors are combined to effectively identify outlier matches. The principle is that if the matching result is accurate, the feature vectors formed by any matched point should have similar rotation angles and moving distances. Thus, global motion direction and global motion distance consistencies were used to reject outliers caused by similar patterns in different locations.</p></div><div><h3>Results</h3><p>Four datasets were used to test the effectiveness of the proposed method. Three datasets with similar patterns in different locations were used to test the results for similar images that could easily be incorrectly matched by other algorithms, and one commonly used dataset was used to test the results for the general image-matching problem. The experimental results suggest that the proposed method is more accurate than other state-of-the-art algorithms in identifying mismatches in the initial matching set.</p></div><div><h3>Conclusions</h3><p>The proposed outlier rejection matching method can significantly improve the matching accuracy for similar images with locally similar feature pairs in different locations and can provide more accurate matching results for subsequent computer vision tasks.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"5 2","pages":"Pages 171-187"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Outliers rejection in similar image matching\",\"authors\":\"Qingqing Chen , Junfeng Yao\",\"doi\":\"10.1016/j.vrih.2023.02.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Image matching is crucial in numerous computer vision tasks such as 3D reconstruction and simultaneous visual localization and mapping. The accuracy of the matching significantly impacted subsequent studies. Because of their local similarity, when image pairs contain comparable patterns but feature pairs are positioned differently, incorrect recognition can occur as global motion consistency is disregarded.</p></div><div><h3>Methods</h3><p>This study proposes an image-matching filtering algorithm based on global motion consistency. It can be used as a subsequent matching filter for the initial matching results generated by other matching algorithms based on the principle of motion smoothness. A particular matching algorithm can first be used to perform the initial matching; then, the rotation and movement information of the global feature vectors are combined to effectively identify outlier matches. The principle is that if the matching result is accurate, the feature vectors formed by any matched point should have similar rotation angles and moving distances. Thus, global motion direction and global motion distance consistencies were used to reject outliers caused by similar patterns in different locations.</p></div><div><h3>Results</h3><p>Four datasets were used to test the effectiveness of the proposed method. Three datasets with similar patterns in different locations were used to test the results for similar images that could easily be incorrectly matched by other algorithms, and one commonly used dataset was used to test the results for the general image-matching problem. The experimental results suggest that the proposed method is more accurate than other state-of-the-art algorithms in identifying mismatches in the initial matching set.</p></div><div><h3>Conclusions</h3><p>The proposed outlier rejection matching method can significantly improve the matching accuracy for similar images with locally similar feature pairs in different locations and can provide more accurate matching results for subsequent computer vision tasks.</p></div>\",\"PeriodicalId\":33538,\"journal\":{\"name\":\"Virtual Reality Intelligent Hardware\",\"volume\":\"5 2\",\"pages\":\"Pages 171-187\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Virtual Reality Intelligent Hardware\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2096579623000116\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Virtual Reality Intelligent Hardware","FirstCategoryId":"1093","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2096579623000116","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
Image matching is crucial in numerous computer vision tasks such as 3D reconstruction and simultaneous visual localization and mapping. The accuracy of the matching significantly impacted subsequent studies. Because of their local similarity, when image pairs contain comparable patterns but feature pairs are positioned differently, incorrect recognition can occur as global motion consistency is disregarded.
Methods
This study proposes an image-matching filtering algorithm based on global motion consistency. It can be used as a subsequent matching filter for the initial matching results generated by other matching algorithms based on the principle of motion smoothness. A particular matching algorithm can first be used to perform the initial matching; then, the rotation and movement information of the global feature vectors are combined to effectively identify outlier matches. The principle is that if the matching result is accurate, the feature vectors formed by any matched point should have similar rotation angles and moving distances. Thus, global motion direction and global motion distance consistencies were used to reject outliers caused by similar patterns in different locations.
Results
Four datasets were used to test the effectiveness of the proposed method. Three datasets with similar patterns in different locations were used to test the results for similar images that could easily be incorrectly matched by other algorithms, and one commonly used dataset was used to test the results for the general image-matching problem. The experimental results suggest that the proposed method is more accurate than other state-of-the-art algorithms in identifying mismatches in the initial matching set.
Conclusions
The proposed outlier rejection matching method can significantly improve the matching accuracy for similar images with locally similar feature pairs in different locations and can provide more accurate matching results for subsequent computer vision tasks.
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