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An improved template matching method for object detection

Journal Article


Abstract


  • This paper presents an improved template matching method that combines both spatial and orientation information in a simple and effective way. The spatial information is obtained through a generalized distance transform (GDT) that weights the distance transform more on the strong edge pixels and the orientation information is represented as an orientation map (OM) which is calculated from local gradient. We applied the proposed method to detect humans, cars, and maple leaves from images. The experimental results have shown that the proposed method outperforms the existing template matching methods and is robust against cluttered background.

Publication Date


  • 2010

Citation


  • Nguyen, D., Li, W. & Ogunbona, P. (2010). An improved template matching method for object detection. S. Maybank, R. Taniguchi & H. Zha In Computer Vision - ACCV 2009: 9th Asian Conference on Computer Vision, 23-27 September, Xi'an, China. Lecture Notes in Computer Science, 5996 193-202.

Scopus Eid


  • 2-s2.0-78650460332

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2190

Has Global Citation Frequency


Number Of Pages


  • 9

Start Page


  • 193

End Page


  • 202

Volume


  • 5996

Place Of Publication


  • Germany

Abstract


  • This paper presents an improved template matching method that combines both spatial and orientation information in a simple and effective way. The spatial information is obtained through a generalized distance transform (GDT) that weights the distance transform more on the strong edge pixels and the orientation information is represented as an orientation map (OM) which is calculated from local gradient. We applied the proposed method to detect humans, cars, and maple leaves from images. The experimental results have shown that the proposed method outperforms the existing template matching methods and is robust against cluttered background.

Publication Date


  • 2010

Citation


  • Nguyen, D., Li, W. & Ogunbona, P. (2010). An improved template matching method for object detection. S. Maybank, R. Taniguchi & H. Zha In Computer Vision - ACCV 2009: 9th Asian Conference on Computer Vision, 23-27 September, Xi'an, China. Lecture Notes in Computer Science, 5996 193-202.

Scopus Eid


  • 2-s2.0-78650460332

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2190

Has Global Citation Frequency


Number Of Pages


  • 9

Start Page


  • 193

End Page


  • 202

Volume


  • 5996

Place Of Publication


  • Germany