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Random Sample Consensus (RANSAC) algorithm is an iterative method to estimate the parameters of the model in a data set that includes outliers. It’s an uncertain algorithm with a certain probability to get a reasonable result. To increase the probability when the proportion of outliers in the sample data set is unchanged, the number of iterations needs to be increased. In application, too many iterations will have a great negative impact on the speed of algorithm execution. This paper presents a deterministic algorithm in 2D feature point matching, which solves the defect that the number of iterations of RANSAC algorithm is uncontrollable. Firstly, the similarity of feature point descriptors is used to calculate the unidirectional optimal matching (UNOM) point pair of the registered and input images. Secondly, the bidirectional optimal matching (BIOM) is calculated based on the two UNOM sets. Of course the BIOM set includes outliers. Then, calculate adjacency matrix and connected subgraph between BIOM point pairs. Eliminat the unsuitable point pairs in the connected subgraph and the remaining point pairs are considered to be correct data. Finally, use these point pairs to calculate the homography matrix with the least square method and make global accurate matching. Results of experiment show that the algorithm in this paper can get better matching results in a significantly shorter time, since it can directly calculate suitable point pairs without iterative selection.
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