Robust Fault Matched Optical Flow Detection Using 2D Histogram

  • Jaechoon Chon
  • Hyongsuk Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3982)


This paper propose an algorithm by which to achieve robust outlier detection without fitting camera models. This algorithm is applicable for cases in which the outlier rate is over 85%. If the outlier rate of optical flows is over 45%, then discarding outliers with conventional algorithms in real-time applications is very difficult. The proposed algorithm overcomes conventional difficulties by using a three-step algorithm: 1) construct a two-dimensional histogram with two axes having the lengths and directions of the optical flows; 2) sort the number of optical flows in each bin of the two-dimensional histogram in descending order, and remove bins having a lower number of optical flows than the given threshold: 3) increase the resolution of the two-dimensional histogram if the number of optical flows grouped in a specific bin is over 20%, and decrease the resolution if the number of optical flows is less than 10%. This process is repeated until the number of optical flows falls into a range of 10%-20%. The proposed algorithm works well on different kinds of images having many outliers. Experimental results are reported.


Feature Point Optical Flow Epipolar Geometry Outlier Rate Random Sample Consensus 
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

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jaechoon Chon
    • 1
  • Hyongsuk Kim
    • 2
  1. 1.Center for Spatial Information Science at the University of TokyoTokyo
  2. 2.Chonbuk National UniversityKorea

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