Three dimensional Bayesian state estimation using shearlet edge analysis and detection
Communications, Control and Signal Processing（2010）
摘要
In this work, we present a method of estimating the kinematic state of a three dimensional object from a set of image sequences recorded at multiple views. In our approach, three dimensional information from a Bayesian filter is merged to stabilize two dimensional recognition as well as tracking so observation collection and object state estimation are concurrent. A unique aspect of this method is that a shearlet transform is used to reliably extract image features. The method is demonstrated on both synthetic and real data for performance evaluation.
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关键词
bayes methods,edge detection,feature extraction,filtering theory,image sequences,state estimation,transforms,bayesian filter,image feature extraction,image sequence,kinematic state estimation,object state estimation,observation collection,shearlet edge analysis,shearlet transform,synthetic data,three dimensional bayesian state estimation,three dimensional object,two dimensional recognition,bayesian state estimation,shearlets,wavelets,wavelet transforms,convergence,kinematics,three dimensional,quaternions,noise,signal to noise ratio,cost function,bayesian methods,particle filters,image features
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