A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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Recently, the 3D sensing technique using multiple cameras has been applied to various areas such as visualization, motion capturing, and so on. However, improvement of the camera model calibration is indispensible for more precise measurement. In this study, we propose a camera modeling technique for 3D sensing based on tile coding (CMAC) structure. A distance between a sensing target and the camera is used to construct the camera model considering optical projection characteristics. In our approach, the least mean square error method is successfully applied considering the simple tile structure to formulate the camera model. Then iterative calculations for solving the inverse problem of the 3-D to 2-D projection by camera are performed to attain measured 3D coordinates. Through sensing experiments of stereo vision measurement based on the proposed approach, we showed the performance of the model was drastically improved compared with the conventional modeling approach such as Open CV model or crisp partitioned model.