Fast-Match: Fast Affine Template Matching

Fast-Match is a fast algorithm for approximate template matching under 2D affine transformations that minimizes the Sum-of-Absolute-Differences (SAD) error measure. There is a huge number of transformations to consider but we prove that they can be sampled using a density that depends on the smoothn...

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Vydáno v:	International journal of computer vision Ročník 121; číslo 1; s. 111 - 125
Hlavní autoři:	Korman, Simon, Reichman, Daniel, Tsur, Gilad, Avidan, Shai
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.01.2017 Springer Springer Nature B.V
Témata:	Affine transformations Algorithms Analysis Approximation Artificial Intelligence Computer Imaging Computer Science Computer vision Errors Experiments Image Processing and Computer Vision Image processing systems Mathematical analysis Pattern Recognition Pattern Recognition and Graphics Studies Template matching Transformations Vision Image matching Template matching Sublinear algorithms Pattern matching
ISSN:	0920-5691, 1573-1405
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Abstract	Fast-Match is a fast algorithm for approximate template matching under 2D affine transformations that minimizes the Sum-of-Absolute-Differences (SAD) error measure. There is a huge number of transformations to consider but we prove that they can be sampled using a density that depends on the smoothness of the image. For each potential transformation, we approximate the SAD error using a sublinear algorithm that randomly examines only a small number of pixels. We further accelerate the algorithm using a branch-and-bound-like scheme. As images are known to be piecewise smooth, the result is a practical affine template matching algorithm with approximation guarantees, that takes a few seconds to run on a standard machine. We perform several experiments on three different datasets, and report very good results.
AbstractList	Fast-Match is a fast algorithm for approximate template matching under 2D affine transformations that minimizes the Sum-of-Absolute-Differences (SAD) error measure. There is a huge number of transformations to consider but we prove that they can be sampled using a density that depends on the smoothness of the image. For each potential transformation, we approximate the SAD error using a sublinear algorithm that randomly examines only a small number of pixels. We further accelerate the algorithm using a branch-and-bound-like scheme. As images are known to be piecewise smooth, the result is a practical affine template matching algorithm with approximation guarantees, that takes a few seconds to run on a standard machine. We perform several experiments on three different datasets, and report very good results.
Audience	Academic
Author	Reichman, Daniel Tsur, Gilad Avidan, Shai Korman, Simon
Author_xml	– sequence: 1 givenname: Simon surname: Korman fullname: Korman, Simon email: simon.korman@gmail.com organization: Computer Science Department, University of California, Tel Aviv University – sequence: 2 givenname: Daniel surname: Reichman fullname: Reichman, Daniel organization: Institute of Cognitive and Brain Sciences, University of California, Weizmann Institute – sequence: 3 givenname: Gilad surname: Tsur fullname: Tsur, Gilad organization: Weizmann Institute, Yahoo Research Labs – sequence: 4 givenname: Shai surname: Avidan fullname: Avidan, Shai organization: School of Electrical Engineering, Tel Aviv University
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Cites_doi	10.1007/978-3-540-76390-1_43 10.1109/TPAMI.2010.165 10.1109/CVPR.2007.383430 10.1007/s11263-009-0275-4 10.1109/TPAMI.2005.188 10.1145/358669.358692 10.1109/TPAMI.2011.106 10.1007/978-3-540-45198-3_31 10.1109/CVPR.1997.609405 10.1016/S0167-8655(01)00099-X 10.1016/0042-6989(96)00002-8 10.1007/s11263-005-3848-x 10.1007/s11263-011-0509-0 10.1117/12.55885 10.1109/FOCS.2010.52 10.1023/B:VISI.0000011205.11775.fd 10.1109/ICCV.2009.5459155 10.1016/S0031-3203(02)00124-3 10.1145/293347.293348 10.1137/080732730 10.1007/978-3-540-77129-6_13 10.1023/B:VISI.0000029664.99615.94 10.1109/TPAMI.2014.2321376
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References_xml	– reference: EveringhamMVan GoolLWilliamsCKWinnJZissermanAThe pascal visual object classes (VOC) challengeInternational Journal of Computer Vision (IJCV)201088230333810.1007/s11263-009-0275-4 – reference: Fredriksson, K. (2001). Rotation invariant template matching. Ph.D. thesis, University of Helsinki. – reference: Korman, S., Reichman, D., & Tsur, G. (2011). Tight approximation of image matching. arXiv preprint arXiv:1111.1713. – reference: Tsur, G., & Ron, D. (2010). Testing properties of sparse images. In Symposium on Foundations of Computer Science (FOCS) (pp. 468–477). IEEE. – reference: Kim, H.Y., & De Araújo, S.A. (2007). Grayscale template-matching invariant to rotation, scale, translation, brightness and contrast. In Advances in image and video technology (AIVT) (pp. 100–113). Springer – reference: Shao, H., Svoboda, T., & Van Gool, L. (2003). Zubud-zurich buildings database for image based recognition. Swiss Federal Institute of Technology, Switzerland, Technical Report (Vol. 260). – reference: Tsai, D. M., & Chiang, C. H. (2002). Rotation-invariant pattern matching using wavelet decomposition. Pattern Recognition Letters, 23(1), 191–201. – reference: AryaSMountDNetanyahuNSilvermanRWuAAn optimal algorithm for approximate nearest neighbor searchingJournal of the ACM1998456891923167884610.1145/293347.2933481065.68650 – reference: YaoCHChenSYRetrieval of translated, rotated and scaled color texturesPattern Recognition200336491392910.1016/S0031-3203(02)00124-3 – reference: Alexe, B., Petrescu, V., & Ferrari, V. (2011). Exploiting spatial overlap to efficiently compute appearance distances between image windows. In Advances in neural information processing systems (NIPS) (pp. 2735–2743). – reference: Raskhodnikova, S. (2003). Approximate testing of visual properties. In Workshop on randomization and approximation techniques in computer science, (RANDOM) (pp. 370–381). – reference: Rucklidge, W. (1997). Efficient guaranteed search for gray-level patterns. In Computer vision and pattern recognition (CVPR) (pp. 717–723). IEEE. – reference: FuhCSMaragosPMotion displacement estimation using an affine model for image matchingOptical Engineering199130788188710.1117/12.55885 – reference: Korman, S., Reichman, D., Tsur, G., & Avidan, S. Fast-Match webpage. www.eng.tau.ac.il/~simonk/FastMatch. – reference: Seitz, S.M., & Baker, S. (2009) Filter flow. In International conference on computer vision (ICCV) (pp. 143–150). 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Snippet	Fast-Match is a fast algorithm for approximate template matching under 2D affine transformations that minimizes the Sum-of-Absolute-Differences (SAD) error...
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SubjectTerms	Affine transformations Algorithms Analysis Approximation Artificial Intelligence Computer Imaging Computer Science Computer vision Errors Experiments Image Processing and Computer Vision Image processing systems Mathematical analysis Pattern Recognition Pattern Recognition and Graphics Studies Template matching Transformations Vision
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Title	Fast-Match: Fast Affine Template Matching
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