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dc.contributor.authorKim, Michael
dc.contributor.authorUeda, Jun
dc.date.accessioned2015-05-26T17:37:55Z
dc.date.available2015-05-26T17:37:55Z
dc.date.issued2015-02
dc.identifier.citationMichael Kim and Jun Ueda, “Dynamics-based motion de-blurring for a PZT-driven, compliant camera orientation mechanism,” The International Journal of Robotics Research. 2015. DOI: http://dx.doi.org/10.1177/0278364914557968en_US
dc.identifier.urihttp://hdl.handle.net/1853/53353
dc.descriptionCopyright © the Authorsen_US
dc.descriptionOriginally published by SAGE.
dc.descriptionDOI: http://dx.doi.org/10.1177/0278364914557968
dc.description.abstractThis paper proposes a method for removing motion blur from images captured by a fast-moving robot eye. Existing image techniques focused on recovering blurry images due to camera shake with long exposure time. In addition, previous studies relied solely on properties of the images or used external sensors to estimate a blur kernel, or point spread function (PSF). This paper focuses on estimating a latent image from the blur images taken by the robotic camera orientation system. A PZT-driven, compliant camera orientation system was employed to demonstrate the effectiveness of this approach. Discrete switching commands were given to the robotic system to create a rapid point-to-point motion while suppressing the vibration with a faster response. The blurry images were obtained when the robotic system created a rapid point-to-point motion, like human saccadic motion. This paper proposes a method for estimating the PSF in knowledge of system dynamics and input commands, resulting in a faster estimation. The proposed method was investigated under various motion conditions using the single-degree-of-freedom camera orientation system to verify the effectiveness and was compared with other approaches quantitatively and qualitatively. The experiment results show that overall the performance metric of the proposed method was 27.77% better than conventional methods. The computation time of the proposed method was 50 times faster than that of conventional methods.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectDe-blurringen_US
dc.subjectMotion de-bluren_US
dc.subjectDynamics baseden_US
dc.subjectRobotic visionen_US
dc.titleDynamics-based motion de-blurring for a PZT-driven, compliant camera orientation mechanismen_US
dc.typePost-printen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Mechanical Engineeringen_US
dc.identifier.doi10.1177/0278364914557968
dc.embargo.termsnullen_US


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