Image registration algorithm using Mexican hat function-based operator and grouped feature matching strategy.
Feature detection and matching are crucial for robust and reliable image registration. Although many methods have been developed, they commonly focus on only one class of image features. The methods that combine two or more classes of features are still novel and significant. In this work, methods f...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3994077?pdf=render |
| _version_ | 1818954709384298496 |
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| author | Feng Jin Dazheng Feng |
| author_facet | Feng Jin Dazheng Feng |
| author_sort | Feng Jin |
| collection | DOAJ |
| description | Feature detection and matching are crucial for robust and reliable image registration. Although many methods have been developed, they commonly focus on only one class of image features. The methods that combine two or more classes of features are still novel and significant. In this work, methods for feature detection and matching are proposed. A Mexican hat function-based operator is used for image feature detection, including the local area detection and the feature point detection. For the local area detection, we use the Mexican hat operator for image filtering, and then the zero-crossing points are extracted and merged into the area borders. For the feature point detection, the Mexican hat operator is performed in scale space to get the key points. After the feature detection, an image registration is achieved by using the two classes of image features. The feature points are grouped according to a standardized region that contains correspondence to the local area, precise registration is achieved eventually by the grouped points. An image transformation matrix is estimated by the feature points in a region and then the best one is chosen through competition of a set of the transformation matrices. This strategy has been named the Grouped Sample Consensus (GCS). The GCS has also ability for removing the outliers effectively. The experimental results show that the proposed algorithm has high registration accuracy and small computational volume. |
| first_indexed | 2024-12-20T10:26:29Z |
| format | Article |
| id | doaj.art-7a99e3d94f6c4d74abb5f405f3c9fb34 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-20T10:26:29Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-7a99e3d94f6c4d74abb5f405f3c9fb342022-12-21T19:43:49ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0194e9557610.1371/journal.pone.0095576Image registration algorithm using Mexican hat function-based operator and grouped feature matching strategy.Feng JinDazheng FengFeature detection and matching are crucial for robust and reliable image registration. Although many methods have been developed, they commonly focus on only one class of image features. The methods that combine two or more classes of features are still novel and significant. In this work, methods for feature detection and matching are proposed. A Mexican hat function-based operator is used for image feature detection, including the local area detection and the feature point detection. For the local area detection, we use the Mexican hat operator for image filtering, and then the zero-crossing points are extracted and merged into the area borders. For the feature point detection, the Mexican hat operator is performed in scale space to get the key points. After the feature detection, an image registration is achieved by using the two classes of image features. The feature points are grouped according to a standardized region that contains correspondence to the local area, precise registration is achieved eventually by the grouped points. An image transformation matrix is estimated by the feature points in a region and then the best one is chosen through competition of a set of the transformation matrices. This strategy has been named the Grouped Sample Consensus (GCS). The GCS has also ability for removing the outliers effectively. The experimental results show that the proposed algorithm has high registration accuracy and small computational volume.http://europepmc.org/articles/PMC3994077?pdf=render |
| spellingShingle | Feng Jin Dazheng Feng Image registration algorithm using Mexican hat function-based operator and grouped feature matching strategy. PLoS ONE |
| title | Image registration algorithm using Mexican hat function-based operator and grouped feature matching strategy. |
| title_full | Image registration algorithm using Mexican hat function-based operator and grouped feature matching strategy. |
| title_fullStr | Image registration algorithm using Mexican hat function-based operator and grouped feature matching strategy. |
| title_full_unstemmed | Image registration algorithm using Mexican hat function-based operator and grouped feature matching strategy. |
| title_short | Image registration algorithm using Mexican hat function-based operator and grouped feature matching strategy. |
| title_sort | image registration algorithm using mexican hat function based operator and grouped feature matching strategy |
| url | http://europepmc.org/articles/PMC3994077?pdf=render |
| work_keys_str_mv | AT fengjin imageregistrationalgorithmusingmexicanhatfunctionbasedoperatorandgroupedfeaturematchingstrategy AT dazhengfeng imageregistrationalgorithmusingmexicanhatfunctionbasedoperatorandgroupedfeaturematchingstrategy |