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Affine Registration

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Computer Vision

Synonyms

Affine alignment

Related Concepts

Affine Camera; Rigid Registration

Definition

The goal of affine registration is to find the affine transformation that best maps one data set (e.g., image, set of points) onto another.

Background

In many situations data is acquired at different times, in different coordinate systems, or from different sensors. Such data can include sparse sets of points and images both in 2D and 3D, but the concepts generalize also to higher dimensions and other primitives. Registration means to bring these data sets into alignment, i.e., to find the “best” transformation that maps one set of data onto another, here using an affine transformation. For the sake of brevity, in this entry only the registration of two data sets is discussed, although approaches exist for finding consistent transformations that align more than two sets at once (e.g., [1]). While intuitively in 1D affine transformations compensate for scale and offset, in any dimension they can...

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References

  1. Eggert DW, Fitzgibbon AW, Fisher RB (1998) Simultaneous registration of multiple range views for use in reverse engineering of CAD models. Comput Vis Image Underst 69(3): 253–272

    Article  Google Scholar 

  2. Mundy JL, Zisserman A (eds) (1992) Geometric invariance in computer vision. MIT, Cambridge

    Google Scholar 

  3. Fitzgibbon AW (2003) Robust registration of 2d and 3d point sets. Image and Vis Computing 21:1145–1153

    Article  Google Scholar 

  4. Rousseeuw PJ (1984) Least median of squares regression. J Am Stat Assoc 79(388):871–880

    Article  MathSciNet  MATH  Google Scholar 

  5. Fischler M, Bolles R (1981) RANdom SAmpling Consensus: a paradigm for model fitting with application to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  MathSciNet  Google Scholar 

  6. Besl PJ, McKay ND (1992) A method for registration of 3-d shapes. IEEE Trans Pattern Anal Mach Intell 14:239–256

    Article  Google Scholar 

  7. Zhang Z (1994) Iterative point matching for registration of free-form curves and surfaces. Int J Comput Vis 13:119–152

    Article  Google Scholar 

  8. Feldmar J, Ayache N (1994) Rigid and affine registration of smooth surfaces using differential properties. In: Eklundh JO (ed) European conference on computer vision (ECCV ’94). Volume 801 of lecture notes in computer science. Springer, Berlin/Heidelberg, pp 396–406. doi:10.1007/BFb0028371

    Google Scholar 

  9. Rusinkiewicz S, Levoy M (2001) 3-D digital imaging and modeling. Proceedings Third International Conference on, Efficient variants of the ICP algorithm, 145–152. doi:10.1109/IM.2001.924423

    Google Scholar 

  10. Obdrzálek S, Matas J (2002) Local affine frames for image retrieval. Proceedings of the International Conference on Image and Video Retrieval, CIVR ’02. Springer, London, pp 318–327

    Google Scholar 

  11. Ho J, Yang MH (2011) On affine registration of planar point sets using complex numbers. Comput Vis Image Underst 115(1):50–58

    Article  Google Scholar 

  12. Tuytelaars T, Mikolajczyk K (2008) Local invariant feature detectors: A survey. Found Trends Comput Graph Vis 3(3):177–280

    Article  Google Scholar 

  13. Mikolajczyk K, Tuytelaars T, Schmid C, Zisserman A, Matas J, Schaffalitzky F, Kadir T, van Gool L (2005) A comparison of affine region detectors. Int J Comput Vis 65(1–2):43–72

    Article  Google Scholar 

  14. Lucas BD, Kanade T (1981) An iterative image registration technique with an application to stereo vision. Proceedings of the 7th international joint conference on Artificial intelligence, 2(6):674–679

    Google Scholar 

  15. Köser K, Koch R (2008) Exploiting uncertainty propagation in gradient-based image registration. Proc Br Mach Vis Conf 83–92

    Google Scholar 

  16. Baker S, Matthews I (2004) Lucas-kanade 20 years on: A unifying framework. Int J Comput Vis 56(1): 221–255

    Article  Google Scholar 

  17. Viola PA III, WMW (1997) Alignment by maximization of mutual information. Int J Comput Vis 24(2):137–154

    Article  Google Scholar 

  18. Zitov B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21:977–1000

    Article  Google Scholar 

  19. Szeliski R (2006) Image alignment and stitching: A tutorial. Found Trends Comput Graph Comput Vis 2

    Google Scholar 

  20. J Shi, Tomasi C (1994) Good features to track. IEEE Conf Comput Vis Pattern Recognit 593–600

    Google Scholar 

  21. Köser K, Koch R (2008) Differential spatial resection – pose estimation using a single local image feature. Eur Conf 278 Comput Vis (LNCS 53025305), 312–325

    Google Scholar 

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Köser, K. (2014). Affine Registration. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_122

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