Point pattern matching using irreducible matrix and relative invariant

Point pattern matching is an important problem in the fields of computer vision and pattern recognition. In this paper, new algorithms based on irredcible matrix and relative invariant for matching two sets of points with the same cardinality are proposed Thei r fundamental idea is transforming the two dimensional point sets with n points into the vectors in n-dimensional space. Considering these vectors as one dimensional point patterns, these new algorithms aim at reducing the point matching problem to that of sorting vectors in n-dimensional space as long as the sensor noise does not alter the order of the elements in the vectors. Theoretical analysis and simulation results show that the new algorithms are effective.