• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.153-155
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• 2022

Recently, we are using robots and drones to collect images. However, as the robot or drone is shaken by external influences, pre-processing technology to register images is required. Therefore, in this paper, we use autonomous robots, drones dataset and improve the quality of shaken image data through the Deblur deep learning model. We confirmed through the experimental results that the shaken images were registered and evaluated the model.

• Korean Journal of Radiology
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• v.2 no.3
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• pp.171-174
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• 2001

Shaken baby syndrome (SBS) is a form of child abuse that can cause significant head injuries, of which subdural hematoma (SDH) is the most common manifestation. We report the MRI findings of chronic SDH in three cases of SBS, involving two-, three- and eight-month-old babies. The SDH signal was mostly low on T1-weighted images and high on T2-weighted images, suggesting chronic SDH. In chronic SDH, a focal high signal on T1-weighted images was also noted, suggesting rebleeding. Contrast-enhanced MRI revealed diffuse dural enhancement.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.216-218
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• 2009

This paper proposes an image processing algorithm to stabilize shaken scenes of UAV(Unmanned Aerial Vehicle) caused by vehicle self-vibration and aerodynamic disturbance. The proposed method stabilizes images by compensating estimated shake motion which is evaluated from global motion. The global motion between two continuous images modeled by 6 parameter warping model is estimated by non-linear square method based on Gauss-Newton algorithm with excluding outlier region. The shake motion is evaluated by subtracting the global motion from aerial vehicle motion obtained by averaging global motion. Experimental results show that the proposed method stabilize shaken scenes effectively.

• Transactions of the Society of Information Storage Systems
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• v.7 no.2
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• pp.70-74
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• 2011

The mobile camera module is a device to be inserted in the digital device for camera feature. The mobile camera module is being shaken by vibrations such as handshake during the exposure time. The clarity is compromised by these vibrations, thus the vibration is considered as an external disturbance. Moreover the use of mobile camera module has been being expanded for automotive vibration should be considered. These external disturbances can cause image blurring, thus optical image stabilization should be applied for image compensation. The compensator is fulfilled mechanically by movable lens group or image sensor that adjusts the optical path to the camera movement. Open loop control is useful for well-defined systems like compliant mechanism. Notch filter and lead compensator are designed and applied to improve the stability and bandwidth. The final level of image compensating is confirmed by image processing with MATLAB and CODE V to verify the better performance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.10-18
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• 2010

This paper proposes an image stabilization algorithm for close watching UAV(Unmanned Aerial Vehicle) using motion separation and stabilization mode. The motion of UAV is composed of its actual navigating motion and unwanted vibrating motion so that image sequences obtained from UAV are shaken randomly. In order to stabilize these images we separate the vibrating motion component from UAV motion and remove the effect caused by it from image sequences. In the proposed algorithm the motion and global intensity change of two consecutive images are modeled with 6 motion parameters and 2 intensity change parameters respectively. These modeled parameters are estimated by non-linear least square method based on Gauss-Newton algorithm. The vibrating motion component is separated from the estimated motion using IIR filtering and the geometric deformation caused by it is removed from image sequences. In order to apply the proposed method to real aerial image sequences with many abrupt changes of camera view, we proposed a stabilizing method using two different modes named as stabilizing and non-stabilizing mode. Experimental results show that the accuracy of motion estimation is 99% and the efficiency of removing the vibrating motion component is 90%. We apply the proposed method to real aerial image sequences and verified its stabilizing performance.