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This study aims to investigate and analyze various image stabilization methods used in surgical robotics. An in-vitro phantom experiment was conducted using a master slave tele-manipulated system. An articulable image probe on a tool (eye-on-tool) was installed on an open-source surgical robot, RAVEN. The 2D non-stereo camera image was used to validate the image stabilization methods, which were evaluated individually for each processing step: preprocessing, motion estimation, and motion compensation. During the preprocessing procedure, the performance of three different filters was tested for effective noise suppression. Various algorithms were compared to estimate the global motion vectors (GMVs) in the motion estimation step. Finally, three filters were analyzed to estimate the compensation motion vector (CMV) during the motion estimation procedure.
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Authors and Affiliations
Department of Computer Science and Engineering, Korea University of Technology and Education, Cheonan, South Korea
Junho Ko & Yoon Sang Kim
Engineering & Mathematics Division, Mechanical Engineering, University of Washington, Bothell, WA, USA
Woon Jong Yoon
- Junho Ko
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- Woon Jong Yoon
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- Yoon Sang Kim
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Correspondence toYoon Sang Kim.
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Ko, J., Yoon, W.J. & Kim, Y.S. A study on surgical robot image stabilization.Multimed Tools Appl77, 9871–9883 (2018). https://doi.org/10.1007/s11042-017-5330-5
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