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SUMMARYThis paper proposes a high-precision, high-speed keypoint matching method using two-stage randomized trees (RTs). The keypoint classification uses conventional RTs for high-precision, real-time keypoint matching. However, the wide variety of view transformations for templates expressed by RTs make it diffidult to achieve high-precision classification for all transformations with a single RTs. To solve this problem, the proposed method classifies the template view transformations in the first stage and then, in the second stage, classifies the keypoints using the RTs that corresponds to each of the view transformations classified in the first stage. Testing demonstrated that the proposed method is 88.5% more precise than SIFT, and 63.5% more precise than using conventional RTs for images in which the viewpoint of the object is rotated by 70 degrees. We have also shown that the proposed method supports real-time keypoint matching at 12 fps.

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It is important to make its vision system more robust and accurate, to give optimal visual-feedback, which helps to control a robot. We propose a robust and accurate pattern matching method for simultaneously identifying robots and estimating their orientations that does not use color segmentation. To search for similar patterns, our approach uses continuous DP matching, which is obtained by scanning at an ellipse circumference from the center of the robot. The DP similarity value is used to identify object, and to obtain the optimal route by back tracing to estimate its orientation. We found that our system's ability to identify objects was robust to variation in light conditions. This is because it can take advantage of the changes in intensity only. [4]