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Radar target detection based on methods of image pattern matching

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper studies the performance of pattern matching algorithms with the goal of the detection and tracking of vessel targets in maritime radar images. We compare different methods using a database which consists of radar images from two different manufactures. The data covers a timespan of roughly 4 hours with a one second time resolution. The performance of 3 template matching and 5 feature detector algorithms is tested and the most suitable algorithm is chosen for further optimizations. These optimizations are based on the properties of the radar images and the properties of the radar target.
Rocznik
Strony
162--167
Opis fizyczny
Bibliogr. 14 poz., rys., tab.
Twórcy
autor
  • German Aerospace Center (DLR) 17235, Neustrelitz, Kalkhorstweg 53, Germany
autor
  • German Aerospace Center (DLR) 17235, Neustrelitz, Kalkhorstweg 53, Germany
autor
  • German Aerospace Center (DLR) 17235, Neustrelitz, Kalkhorstweg 53, Germany
Bibliografia
  • 1. JIAO Z., LUO Z.: The Analysis of the Limitation of the ARPA. Navigation College, Wuhan University of Technology. Reports 2007-6 (Notice: published in Chinese language), 2007.
  • 2. Compact external frame grabber – VGA2USB LR. Epiphan Systems Inc., Ottawa, Ontario 2009.
  • 3. VisionMaster FT Series of Naval Radars. Northrop Grumman Corporation – Sperry Marine.
  • 4. Radarpilot 1100 specification. SAM Electronics GmbH. Hamburg, Germany.
  • 5. LAGANIERE R.: OpenCV 2 Computer Vision Application Programming Cookbook. Published by Packt Publishing Ltd, Olton Birmingham 2011, UK.
  • 6. BABBAR G., BAJAJ P., CHAWLA A., GOGNA M.: A comparative study of image matching algorithms. International Journal of Information, Technology and Knowledge Management, July-December, 2(2), 2010, 337–339.
  • 7. SZELISKI R.: Computer Vision: Algorithms and Applications. Springer, London Dordrecht Heidelberg New York 2010.
  • 8. LOWE D.G.: Distintive image feature from scale-invariant keypoints. International Journal of Computer Vision 60(2), 2004, 91–110.
  • 9. BAY H., ESS A., TUYTELAARS T., VAN GOOL L.: Speeded-up robust features (SURF); Computer Vision ECCV 2006, Vol. 3951, Lecture Notes in Computer Science, 2006, 404–417.
  • 10. HARRIS C.: A combined corner and edge detector; Fourth Alvey Vision Conference, Manchester, UK. p. 147–151.
  • 11. TRAJKOVIC M., HEDLEY M.: FAST corner detector. Image and Vision Computing 16, 1998, 75–87.
  • 12. BRUNELLI R.: Template Matching Techniques in Computer Vision: Theory and Practice. John Published by Wiley and Sons Ltd, United Kingdom 2009.
  • 13. GUERRERO M.: A Comparative Study of Three Image Matcing Algorithms: Sift, Surf, and Fast. All Graduate Theses and Dissertations, Paper 1040, Utah State University, 2011.
  • 14. Museum C. o. C.B. Ken Warby – the fastest man on water. Official Newsletter of the Concord Heritage Socienty March 2011, No. 176.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7f2dc396-b73c-453b-81eb-1e04a31d6ef9
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