Maximum Entropy Threshold Segmentation for Target Matching Using Speeded-Up Robust Features
This paper proposes a 2-dimensional (2D) maximum entropy threshold segmentation (2DMETS) based speeded-up robust features (SURF) approach for image target matching. First of all, based on the gray level of each pixel and the average gray level of its neighboring pixels, we construct a 2D gray histog...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2014/768519 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832558848762707968 |
|---|---|
| author | Mu Zhou Xia Hong Zengshan Tian Huining Dong Mingchun Wang Kunjie Xu |
| author_facet | Mu Zhou Xia Hong Zengshan Tian Huining Dong Mingchun Wang Kunjie Xu |
| author_sort | Mu Zhou |
| collection | DOAJ |
| description | This paper proposes a 2-dimensional (2D) maximum entropy threshold segmentation (2DMETS) based speeded-up robust features (SURF) approach for image target matching. First of all, based on the gray level of each pixel and the average gray level of its neighboring pixels, we construct a 2D gray histogram. Second, by the target and background segmentation, we localize the feature points at the interest points which have the local extremum of box filter responses. Third, from the 2D Haar wavelet responses, we generate the 64-dimensional (64D) feature point descriptor vectors. Finally, we perform the target matching according to the comparisons of the 64D feature point descriptor vectors. Experimental results show that our proposed approach can effectively enhance the target matching performance, as well as preserving the real-time capacity. |
| format | Article |
| id | doaj-art-303c971501db42b89b91f2d54c376a5b |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-303c971501db42b89b91f2d54c376a5b2025-02-03T01:31:19ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552014-01-01201410.1155/2014/768519768519Maximum Entropy Threshold Segmentation for Target Matching Using Speeded-Up Robust FeaturesMu Zhou0Xia Hong1Zengshan Tian2Huining Dong3Mingchun Wang4Kunjie Xu5Chongqing Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaChongqing Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaChongqing Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaChongqing Laboratory of Material Physics and Information Display, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaChongqing Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaGraduate Telecommunications and Networking Program, University of Pittsburgh, Pittsburgh, PA 15260, USAThis paper proposes a 2-dimensional (2D) maximum entropy threshold segmentation (2DMETS) based speeded-up robust features (SURF) approach for image target matching. First of all, based on the gray level of each pixel and the average gray level of its neighboring pixels, we construct a 2D gray histogram. Second, by the target and background segmentation, we localize the feature points at the interest points which have the local extremum of box filter responses. Third, from the 2D Haar wavelet responses, we generate the 64-dimensional (64D) feature point descriptor vectors. Finally, we perform the target matching according to the comparisons of the 64D feature point descriptor vectors. Experimental results show that our proposed approach can effectively enhance the target matching performance, as well as preserving the real-time capacity.http://dx.doi.org/10.1155/2014/768519 |
| spellingShingle | Mu Zhou Xia Hong Zengshan Tian Huining Dong Mingchun Wang Kunjie Xu Maximum Entropy Threshold Segmentation for Target Matching Using Speeded-Up Robust Features Journal of Electrical and Computer Engineering |
| title | Maximum Entropy Threshold Segmentation for Target Matching Using Speeded-Up Robust Features |
| title_full | Maximum Entropy Threshold Segmentation for Target Matching Using Speeded-Up Robust Features |
| title_fullStr | Maximum Entropy Threshold Segmentation for Target Matching Using Speeded-Up Robust Features |
| title_full_unstemmed | Maximum Entropy Threshold Segmentation for Target Matching Using Speeded-Up Robust Features |
| title_short | Maximum Entropy Threshold Segmentation for Target Matching Using Speeded-Up Robust Features |
| title_sort | maximum entropy threshold segmentation for target matching using speeded up robust features |
| url | http://dx.doi.org/10.1155/2014/768519 |
| work_keys_str_mv | AT muzhou maximumentropythresholdsegmentationfortargetmatchingusingspeededuprobustfeatures AT xiahong maximumentropythresholdsegmentationfortargetmatchingusingspeededuprobustfeatures AT zengshantian maximumentropythresholdsegmentationfortargetmatchingusingspeededuprobustfeatures AT huiningdong maximumentropythresholdsegmentationfortargetmatchingusingspeededuprobustfeatures AT mingchunwang maximumentropythresholdsegmentationfortargetmatchingusingspeededuprobustfeatures AT kunjiexu maximumentropythresholdsegmentationfortargetmatchingusingspeededuprobustfeatures |