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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| Veröffentlicht in: | International journal of computer vision Jg. 121; H. 1; S. 111 - 125 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.01.2017
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| 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. |
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| 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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| Keywords | Image matching Template matching Sublinear algorithms Pattern matching |
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| References | 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 YaoCHChenSYRetrieval of translated, rotated and scaled color texturesPattern Recognition200336491392910.1016/S0031-3203(02)00124-3 KleinerIKerenDNewmanIBen-ZwiOApplying property testing to an image partitioning problemPattern Analysis and Machine Intelligence (PAMI)201133225626510.1109/TPAMI.2010.165 MujaMLoweDGScalable nearest neighbor algorithms for high dimensional dataIEEE Transactions on Pattern Analysis and Machine Intelligence201436112227224010.1109/TPAMI.2014.2321376 Pele, O., & Werman, M. (2007). Accelerating pattern matching or how much can you slide?. In Asian conference on computer vision (ACCV) (pp. 435–446). Springer. OuyangWTombariFMattocciaSDi StefanoLChamWKPerformance evaluation of full search equivalent pattern matching algorithmsPattern Analysis and Machine Intelligence (PAMI)201234112714310.1109/TPAMI.2011.106 Tsur, G., & Ron, D. (2010). Testing properties of sparse images. In Symposium on Foundations of Computer Science (FOCS) (pp. 468–477). IEEE. MorelJMYuGASIFT: A new framework for fully affine invariant image comparisonSIAM Journal on Imaging Sciences200922438469251991910.1137/0807327301181.68252 EveringhamMVan GoolLWilliamsCKWinnJZissermanAThe pascal visual object classes (VOC) challengeInternational Journal of Computer Vision (IJCV)201088230333810.1007/s11263-009-0275-4 HartleyRZissermanAMultiple view geometry in computer vision2003CambridgeCambridge University Press0956.68149 Rucklidge, W. (1997). Efficient guaranteed search for gray-level patterns. In Computer vision and pattern recognition (CVPR) (pp. 717–723). IEEE. Tsai, D. M., & Chiang, C. H. (2002). Rotation-invariant pattern matching using wavelet decomposition. Pattern Recognition Letters, 23(1), 191–201. TianYNarasimhanSGGlobally optimal estimation of nonrigid image distortionInternational Journal of Computer Vision (IJCV)2012983279302291705510.1007/s11263-011-0509-01254.68284 AryaSMountDNetanyahuNSilvermanRWuAAn optimal algorithm for approximate nearest neighbor searchingJournal of the ACM1998456891923167884610.1145/293347.2933481065.68650 FuhCSMaragosPMotion displacement estimation using an affine model for image matchingOptical Engineering199130788188710.1117/12.55885 Seitz, S.M., & Baker, S. (2009) Filter flow. In International conference on computer vision (ICCV) (pp. 143–150). IEEE. Fredriksson, K. (2001). Rotation invariant template matching. Ph.D. thesis, University of Helsinki. Wang, Q., & You, S. (2007). Real-time image matching based on multiple view kernel projection. In IEEE conference on computer vision and pattern recognition, 2007. CVPR’07 (pp. 1–8). IEEE. Korman, S., Reichman, D., Tsur, G., & Avidan, S. Fast-Match webpage. www.eng.tau.ac.il/~simonk/FastMatch. LoweDGDistinctive image features from scale-invariant keypointsInternational Journal of Computer Vision (IJCV)20046029111010.1023/B:VISI.0000029664.99615.94 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). 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). Korman, S., Reichman, D., & Tsur, G. (2011). Tight approximation of image matching. arXiv preprint arXiv:1111.1713. Raskhodnikova, S. (2003). Approximate testing of visual properties. In Workshop on randomization and approximation techniques in computer science, (RANDOM) (pp. 370–381). BakerSMatthewsILucas-kanade 20 years on: A unifying frameworkInternational Journal of Computer Vision (IJCV)200456322125510.1023/B:VISI.0000011205.11775.fd MikolajczykKSchmidCA performance evaluation of local descriptorsPattern Analysis and Machine Intelligence (PAMI)200527101615163010.1109/TPAMI.2005.188 FischlerMABollesRCRandom sample consensus: A paradigm for model fitting with applications to image analysis and automated cartographyCommunications of the ACM198124638139561815810.1145/358669.358692 van der SchaafAvan HaterenJModelling the power spectra of natural images: Statistics and informationVision Research199636172759277010.1016/0042-6989(96)00002-8 Lucas, B.D., Kanade, T., et al. (1981) An iterative image registration technique with an application to stereo vision. In IJCAI (Vol. 81, pp. 674–679). MikolajczykKTuytelaarsTSchmidCZissermanAMatasJSchaffalitzkyFKadirTVan GoolLA comparison of affine region detectorsInternational Journal of Computer Vision (IJCV)2005651–2437210.1007/s11263-005-3848-x W Ouyang (926_CR19) 2012; 34 K Mikolajczyk (926_CR16) 2005; 65 JM Morel (926_CR17) 2009; 2 M Everingham (926_CR4) 2010; 88 I Kleiner (926_CR10) 2011; 33 926_CR12 CH Yao (926_CR30) 2003; 36 926_CR11 926_CR14 926_CR9 DG Lowe (926_CR13) 2004; 60 K Mikolajczyk (926_CR15) 2005; 27 M Muja (926_CR18) 2014; 36 926_CR1 S Baker (926_CR3) 2004; 56 926_CR6 926_CR29 926_CR23 MA Fischler (926_CR5) 1981; 24 926_CR24 926_CR21 926_CR22 CS Fuh (926_CR7) 1991; 30 926_CR27 S Arya (926_CR2) 1998; 45 926_CR26 A Schaaf van der (926_CR28) 1996; 36 R Hartley (926_CR8) 2003 926_CR20 Y Tian (926_CR25) 2012; 98 |
| 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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| 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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