Abstract
Past approaches on motion estimation use iterative algorithm to produce dense motion fields, which is modeled by the energy functions. The optimization strategy such as simulated annealing or iterated conditional mode reorganize the motion fields slowly. This paper introduces adaptive block matching and multiscale smoothing as an initial motion fields for bayesian based motion estimation. The adaptive block matching is a local intensity matching procedure, which gives a unique matching results. The results are smoothed by multiscale smoothing algorithm. This algorithm is based on kalman filter, but the time domain of this filter becomes the scale domain. The result shows that this strategy can give a more global motion fields than the result of single resolution bayesian motion estimation method. This multiscale smoothing algorithm have numerous possibility to enhance the speed as well as strategy to produce better motion fields.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
