Abstract：Concerning the problem that matching time and accuracy requirements can not be met the simultaneously in image matching technology, a method based on feature points matching was proposed. Landmark matching was achieved successfully by using Random Forest (RF), and matching problem was translated into simple classifying problem to reduce the complication of computation for real-time image matching. Landmark image was represented by Features from Accelerated Segment Test (FAST) feature points, the scale and affine invariability of FAST feature points were improved by Gaussian pyramid structure and affine augmented strategy, and the matching rate was raised. Comparing with Scale-Invariant Feature Transform (SIFT) algorithm and Speed Up Robust Feature (SURF) algorithm, the experimental results show that the matching rate of the proposed algrorithm reached about 90%, keeping the matching rate approximately with SIFT and SURF in cases of scale change, occlusion or rotation, and its running time was an order of magnitude than other two algorithms. This method matches landmarks efficiently and its running time meets the real-time requirements.
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2016, Vol. 36 
