Transformation Model Estimation for Point Matching Via Gaussian Processes
نویسندگان
چکیده
One of main issues in point matching is the choice of the mapping function and the computation of its optimal hyperparameters. In this paper, we propose an attractive approach to determine the mapping function based on Gaussian processes (GPs) model. The mapping function is assumed to belong to a GPs model specified by a mean and a covariance function. Meanwhile, hyperparameters optimization of mapping function is replaced by adaptation of GP model. Experiments show that the algorithm has efficient mapping capability and practical implementation in both synthetic and real cases.
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