Scale-Invariant Feature Transform Algorithm with Fast Approximate Nearest Neighbor
There is a great deal of systems dealing with image processing that are being used and developed on a daily basis. Those systems need the deployment of some basic operations such as detecting the Regions of Interest and matching those regions, in addition to the description of their properties. Thos...
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| Language: | English |
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University of Baghdad, College of Science for Women
2017-09-01
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| Series: | مجلة بغداد للعلوم |
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| Online Access: | http://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/2405 |
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| author | Baghdad Science Journal |
| author_facet | Baghdad Science Journal |
| author_sort | Baghdad Science Journal |
| collection | DOAJ |
| description | There is a great deal of systems dealing with image processing that are being used and developed on a daily basis. Those systems need the deployment of some basic operations such as detecting the Regions of Interest and matching those regions, in addition to the description of their properties. Those operations play a significant role in decision making which is necessary for the next operations depending on the assigned task.
In order to accomplish those tasks, various algorithms have been introduced throughout years. One of the most popular algorithms is the Scale Invariant Feature Transform (SIFT). The efficiency of this algorithm is its performance in the process of detection and property description, and that is due to the fact that it operates on a big number of key-points, the only drawback it has is that it is rather time consuming.
In the suggested approach, the system deploys SIFT to perform its basic tasks of matching and description is focused on minimizing the number of key-points which is performed via applying Fast Approximate Nearest Neighbor algorithm, which will reduce the redundancy of matching leading to speeding up the process.
The proposed application has been evaluated in terms of two criteria which are time and accuracy, and has accomplished a percentage of accuracy of up to 100%, in addition to speeding up the processes of matching and description. |
| format | Article |
| id | doaj-art-915084f8ac8f4062ba91c19ffad17c21 |
| institution | Kabale University |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2017-09-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-915084f8ac8f4062ba91c19ffad17c212025-08-20T03:33:41ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862017-09-0114310.21123/bsj.14.3.651-661Scale-Invariant Feature Transform Algorithm with Fast Approximate Nearest NeighborBaghdad Science JournalThere is a great deal of systems dealing with image processing that are being used and developed on a daily basis. Those systems need the deployment of some basic operations such as detecting the Regions of Interest and matching those regions, in addition to the description of their properties. Those operations play a significant role in decision making which is necessary for the next operations depending on the assigned task. In order to accomplish those tasks, various algorithms have been introduced throughout years. One of the most popular algorithms is the Scale Invariant Feature Transform (SIFT). The efficiency of this algorithm is its performance in the process of detection and property description, and that is due to the fact that it operates on a big number of key-points, the only drawback it has is that it is rather time consuming. In the suggested approach, the system deploys SIFT to perform its basic tasks of matching and description is focused on minimizing the number of key-points which is performed via applying Fast Approximate Nearest Neighbor algorithm, which will reduce the redundancy of matching leading to speeding up the process. The proposed application has been evaluated in terms of two criteria which are time and accuracy, and has accomplished a percentage of accuracy of up to 100%, in addition to speeding up the processes of matching and description.http://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/2405Invariant Features, Object Recognition, Scale Invariance, Image Matching, Sift Algorithm. |
| spellingShingle | Baghdad Science Journal Scale-Invariant Feature Transform Algorithm with Fast Approximate Nearest Neighbor مجلة بغداد للعلوم Invariant Features, Object Recognition, Scale Invariance, Image Matching, Sift Algorithm. |
| title | Scale-Invariant Feature Transform Algorithm with Fast Approximate Nearest Neighbor |
| title_full | Scale-Invariant Feature Transform Algorithm with Fast Approximate Nearest Neighbor |
| title_fullStr | Scale-Invariant Feature Transform Algorithm with Fast Approximate Nearest Neighbor |
| title_full_unstemmed | Scale-Invariant Feature Transform Algorithm with Fast Approximate Nearest Neighbor |
| title_short | Scale-Invariant Feature Transform Algorithm with Fast Approximate Nearest Neighbor |
| title_sort | scale invariant feature transform algorithm with fast approximate nearest neighbor |
| topic | Invariant Features, Object Recognition, Scale Invariance, Image Matching, Sift Algorithm. |
| url | http://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/2405 |
| work_keys_str_mv | AT baghdadsciencejournal scaleinvariantfeaturetransformalgorithmwithfastapproximatenearestneighbor |