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Volume 27 Issue 6
November  2012
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Article Contents
Abstract
References
Qing-Liang Lin, Bin Sheng, Yang Shen, Zhi-Feng Xie, Zhi-Hua Chen, Li-Zhuang Ma. Fast Image Correspondence with Global Structure Projection[J]. Journal of Computer Science and Technology, 2012, 27(6): 1281-1288. DOI: 10.1007/s11390-012-1304-2
Citation: Qing-Liang Lin, Bin Sheng, Yang Shen, Zhi-Feng Xie, Zhi-Hua Chen, Li-Zhuang Ma. Fast Image Correspondence with Global Structure Projection[J]. Journal of Computer Science and Technology, 2012, 27(6): 1281-1288. DOI: 10.1007/s11390-012-1304-2
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Fast Image Correspondence with Global Structure Projection

  • 1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
    2. Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237 China

Funds: This work was supported by the National Natural Science Foundation of China under Grant Nos. 61133009, 61073089, the Innovation Program of the Science and Technology Commission of Shanghai Municipality of China under Grant No. 10511501200, and the Open Project Program of the National Laboratory of Pattern Recognition of China.
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  • Received Date: November 24, 2011
  • Revised Date: September 16, 2012
  • Published Date: November 04, 2012
    Abstract
  • This paper presents a method for recognizing images with flat objects based on global keypoint structure correspondence. This technique works by two steps: reference keypoint selection and structure projection. The using of global keypoint structure is an extension of an orderless bag-of-features image representation, which is utilized by the proposed matching technique for computation efficiency. Specifically, our proposed method excels in the dataset of images containing "flat objects" such as CD covers, books, newspaper. The efficiency and accuracy of our proposed method has been tested on a database of nature pictures with flat objects and other kind of objects. The result shows our method works well in both occasions.
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    • References (16)
  • [1]
    Lowe D. Distinctive image features from scale-invariant key-points. International Journal of Computer Vision, 2004,60(2): 91-110.
    [2]
    Bay H, Ess A, Tuytelaars T, Van Gool L. Speeded-up robustfeatures (SURF). Computer Vision and Image Understand-ing, 2008, 110(3): 346-359.
    [3]
    Keysers D, Deselaers T, Ney H. Pixel-to-pixel matching forimage recognition using Hungarian graph matching. In Proc.the 26th DAGM, Aug. 30-Sept. 4, 2004, pp.154-162.
    [4]
    Lazebnik S, Schmid C, Ponce J. Beyond bags of features:spatial pyramid matching for recognizing natural scene cate-gories. In Proc. the 19th CVPR, Jun. 2006, pp.2169-2178.
    [5]
    Förstner W. A feature based correspondence algorithm for im-age matching. Int. Archives of Photogrammetry and RemoteSensing, 1986, 26(3): 150-166.
    [6]
    Bajramovic F, Koch M, Denzler J. Experimental comparisonof wide baseline correspondence algorithms for multi cameracalibration. In Proc. VISAPP, Feb. 2009, pp.458-463.
    [7]
    Mikolajczyk K, Schmid C. An affine invariant interest pointdetector. In Proc. the 7th ECCV, May 27-June 2, 2002,pp.128-142.
    [8]
    Mikolajczyk K, Tuytelaars T, Schmid C, Zisserman A, MatasJ, Schaffalitzky F, Kadir T, Van Gool L. A comparison ofaffine region detectors. International Journal of ComputerVision, 2005, 65(1-2): 43-72.
    [9]
    Harris C, Stephens M. A combined corner and edge detector.In Proc. the 4th Alvey vision conference, Aug. 198, pp.147-151.
    [10]
    Mikolajczyk K, Schmid C. A performance evaluation of lo-cal descriptors. IEEE Transactions on Pattern Analysis andMachine Intelligence, 2005, 27(10): 1615-1630.
    [11]
    Salton G, McGill M. Introduction to Modern Information Re-trieval. New York, USA: McGraw-Hill Book Company, 1983.
    [12]
    Liu Y J, Luo X, Xuan Y M, Chen W F, Fu X L. Image retar-geting quality assessment. Computer Graphics Forum 2011,30(2): 583-592.
    [13]
    Cheng M M, Zhang G X, Mitra N J, Huang X, Hu S M.Global contrast based salient region detection. In Proc. the24th CVPR, Jun. 2011, pp.409-416.
    [14]
    Friedman J, Bentley J, Finkel R. An algorithm for finding bestmatches in logarithmic expected time. ACM Trans. Math.Softw., 1977, 3(3): 209-226.
    [15]
    Beis J, Lowe D. Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In Proc. the1997 CVPR, Jun. 1997, pp.1000-1006.
    [16]
    Nist閞 D, Stew閚ius H. Scalable recognition with a vocabularytree. In Proc. the 19th CVPR, Jun. 2006, pp.2161-2168.
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