Zhengyou Zhang: Software IMAGE-MATCHING


* Description of the software
* References of the work
* Example with a real data set
* Report which describes the algorithm
* How to get the software compiled for SUN Sparc or Linux
* WWW Interface for computing the epipolar geometry with your set of images
(developed by Stephane Laveau)


  • Input: two (uncalibrated or calibrated) images with PGM or INRIMAGE format
  • Output: fundamental matrix, or motion if images are calibrated, and point correspondences

    ``IMAGE-MATCHING'' is a software which implements a robust technique for binocular image matching by exploiting the only available geometric constraint, namely, the epipolar constraint. It thus computes also the epipolar geometry, in terms of the fundamental matrix, between two images.

    The images are uncalibrated, namely the motion between them and the camera parameters are not known. Thus, the images can be taken by different cameras or a single camera at different time instants. If we make an exhaustive search for the epipolar geometry, the complexity is prohibitively high. The idea underlying our approach is to use classical techniques (correlation and relaxation methods in our particular implementation) to find an initial set of matches, and then use a robust technique---the Least Median of Squares (LMedS)---to discard false matches in this set. The epipolar geometry can then be accurately estimated using a meaningful image criterion. More matches are eventually found, as in stereo matching, by using the recovered epipolar geometry. A large number of experiments have been carried out, and very good results have been obtained.

    The images can be calibrated (i.e. with known intrinsic parameters). Then instead of the fundamental matrix, the motion and structure is computed. An example with a real data set is here.

    Regarding the relaxation technique, we define a new measure of matching support, which allows a higher tolerance to deformation with respect to rigid transformations in the image plane and a smaller contribution for distant matches than for nearby ones. A new strategy for updating matches is developed, which only selects those matches having both high matching support and low matching ambiguity. The update strategy is different from the classical ``winner-take-all'', which is easily stuck at a local minimum, and also from ``looser-take-nothing'', which is usually very slow. The proposed algorithm has been widely tested and works remarkably well even for scenes with many repetitive patterns.


    Click here to have a copy of the paper (huge!) appeared in Artificial Intelligence Journal, Vol.78, pages 87-119, October 1995. Also Research Report No.2273, INRIA Sophia-Antipolis, 1994.

    image-matching has an X/Motif interface and is user-friendly. But it can also be used in batch mode.
    Click here to have a copy of executable ``image-matching'' (1175K bytes) compiled for SUN Sparc.
    Click here to have a copy of executable ``image-matching'' (174K bytes) compiled for Linux. The statically linked version is available here (1000K bytes).
    Click here to have a short description of its use. Type ``image-matching -help'' for options.
    Before downloading the software, you may want to try our WWW Interface for computing the epipolar geometry with your set of images (developed by Stephane Laveau).

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