Bioinspired Image Stabilization Control Using the Adaptive Gain Adjustment Scheme of Vestibulo-Ocular Reflex

Koh, Doo-Yeol; Kim, Young Kook; Kim, Kyung-Soo; Kim, Soohyun

doi:10.1109/tmech.2015.2494367

Cited by 5 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Koh et al [20] proposed an adaptive gain adjustment scheme for fine scale image stabilization, using a stabilization device that mimicked human eye and VOR principles. Experimental results suggest that the system can successfully reject up to 10 Hz vibration.…”

Section: Rendering On Hmdmentioning

confidence: 99%

Inspired by Human Eye: Vestibular Ocular Reflex Based Gimbal Camera Movement to Minimize Viewpoint Changes

et al. 2019

View full text Add to dashboard Cite

Human eyeballs move relative to the head, resulting in optimal changes in the viewpoint. We tested similar vestibular ocular reflex (VOR)-based movement on Zenmuse-X3 gimbal camera relative to pre-defined YAW movements of the DJI Matrice-100 unmanned aerial vehicle (UAV). Changes in viewpoint have various consequences for visual and graphical rendering. Therefore, this study investigated how to minimize these changes. OpenGL visualization was performed to simulate and measure viewpoint changes using the proposed VOR-based eyeball movement algorithm and compared with results of VOR based gimbal movement. The gimbal camera was setup to render images (scenes) on flat monitors. Positions of pre-fixed targets in the images were used to measure the viewpoint changes. The proposed approach could successfully control and significantly reduce the viewpoint changes and stabilize the image to improve visual tracking of targets on flat monitors. The proposed method can also be used to render real-time camera feed to a head-mounted display (HMD) in an ergonomically pleasing way.

show abstract

Section: Rendering On Hmdmentioning

confidence: 99%

Inspired by Human Eye: Vestibular Ocular Reflex Based Gimbal Camera Movement to Minimize Viewpoint Changes

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Inter-frame transformation fidelity (ITF) was proposed in Ref. [18] for surveillance applications and has been used by many authors [19][20][21][22] to compare the performances of different stabilization methods. First, the peak signal-to-noise ratio (PSNR) between adjacent frames are computed.…”

Section: Stability Measurementsmentioning

confidence: 99%

Viewing Experience Model of First-Person Videos

Ma¹,

Reibman²

2018

J. Imaging

View full text Add to dashboard Cite

First-Person Videos (FPVs) are recorded using wearable cameras to share the recorder’s First-Person Experience (FPE). Ideally, the FPE is conveyed by the viewing experience of the FPV. However, raw FPVs are usually too shaky to watch, which ruins the viewing experience. To solve this problem, we improve the viewing experience of FPVs by modeling it as two parts: video stability and First-Person Motion Information (FPMI). Existing video stabilization techniques can improve the video stability but damage the FPMI. We propose a Viewing Experience (VE) score, which measures both the stability and the FPMI of a FPV by exploring the mechanism of human perception. This enables us to further develop a system that can stabilize FPVs while preserving their FPMI so that the viewing experience of FPVs is improved. Objective tests show that our measurement is robust under different kinds of noise, and our system has competitive performance relative to current stabilization techniques. Subjective tests show that (1) both our stability and FPMI measurements can correctly compare the corresponding attributes of an FPV across different versions of the same content, and (2) our video processing system can effectively improve the viewing experience of FPVs.

show abstract