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This paper proposes a novel matching method for realtime finding the correspondences among different images containing the same object. The method utilizes an efficient Kernel Projection scheme to descript the image patch around a detected feature point. In order to achieve invariance and tolerance to geometric distortions, it combines a training stage based on generated synthetic views of the object. The two reliable and efficient methods cooperate together, resulting the core part of our novel multiple view kernel projection method (MVKP). Finally, considering the properties and distribution of the described feature vectors, we search for the best correspondence between two sets of features using a fast filtering vector approximation (FFVA) algorithm, which can be viewed as a fast lower-bound rejection scheme. Extensive experimental results on both synthetic and real data have demonstrated the effectiveness of the proposed approach.