The projective correspondence between planar point/line sets is determined using conic invariants. In each set six (4+2) randomly chosen points/lines define two conics to which two absolute projective invariants can be associated. Similar invariant values yield six-tuples matched between the sets. The order of features on a conic is not important and thus the probability of finding a match is maximized. Feature correspondence is recovered with a dynamic programming type analysis of the contingency table derived from the ensemble of matched six-tuples. The algorithm is very sensitive to feature accuracy, raising questions about the straightforward use of conic invariants in recognition of arbitrary objects. The procedures employed, however-affine invariant conic fitting to noisy data,-probabilistic two-stage matching and are of general interest.
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