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International Journal of Computer Vision

, Volume 13, Issue 1, pp 7–32 | Cite as

A study of affine matching with bounded sensor error

  • W. Eric L. Grimson
  • Daniel P. Huttenlocher
  • David W. Jacobs
Article

Abstract

Affine transformations of the plane have been used in a number of model-based recognition systems. Because the underlying mathematics are based on exact data, in practice various heuristics are used to adapt the methods to real data where there is positional uncertainty. This paper provides a precise analysis of affine point matching under uncertainty. We obtain an expression for the range of affine-invariant values that are consistent with a given set of four points, where each image point lies in an ∈-disc of uncertainty. This range is shown to depend on the actualx-y-positions of the data points. In other words, given uncertainty in the data there are no representations that are invariant with respect to the Cartesian coordinate system of the data. This is problematic for methods, such as geometric hashing, that are based on affine-invariant representations. We also analyze the effect that uncertainty has on the probability that recognition methods using affine transformations will find false positive matches. We find that there is a significant probability of false positives with even moderate levels of sensor error, suggesting the importance of good verification techniques and good grouping techniques.

Keywords

Computer Vision Recognition System Cartesian Coordinate System Significant Probability Precise Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 1994

Authors and Affiliations

  • W. Eric L. Grimson
    • 1
  • Daniel P. Huttenlocher
    • 2
    • 3
  • David W. Jacobs
    • 4
  1. 1.AI Lab, Massachusetts Institute of TechnologyCambridgeUSA
  2. 2.Department of Computer ScienceCornell UniversityIthacaUSA
  3. 3.Xerox PARCPalo AltoUSA
  4. 4.NEC Research InstitutePrincetonUSA

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