It is very important to assess buildings that have been subjected to earthquakes to determine their safety. In some regions, the emergency safety evaluation should be conducted within 24h after a huge earthquake has occurred. Some structural health monitoring systems enable rapid evaluation; however, they generally require many vibration sensors. Our research group studied a video-based micro-vibration measurement system that can evaluate the safety of buildings without vibration sensors. We propose an estimation method of the camera fluctuation for the video-based micro-vibration measurement system. The proposed method estimates the camera fluctuation as a global movement across the entire image. Thus, the method finds a group of pixels with a mode of spatial motion using the time difference of the spatial phase. Then, the time-variant signals of the mode pixels are estimated as the camera fluctuation. We found that the proposed method can estimate the camera vibration frequency under conditions where multiple objects exist within the angle of view.
Proc. SPIE. 11049, International Workshop on Advanced Image Technology (IWAIT) 2019
KEYWORDS: Signal to noise ratio, Visual process modeling, Digital image processing, Interference (communication), Data processing, Image enhancement, Human vision and color perception, Signal detection, Stochastic processes, Brain
Elucidation of information processing in our brains is progressing given the highly informationoriented world we live in. Noise is inevitably present in both man-made and natural systems. Previously, these elements were removed for signal detection and information processing. However, recent studies have reported that noise plays a major role in brain information processing. One of the salient features of the relationship between noise and the vision system is the stochastic resonance phenomenon, wherein the detection rate of a weak signal is improved by the visual addition of a blinking noise of appropriate intensity. Improved understanding of the vision system is very useful for the development of imaging technology. This strategy of improving weak signal detection can be applied to digital image processing. In this study, we propose a vision model based on the FitzHugh–Nagumo equation and confirm that the stochastic resonance in brightness perception can be described by the model.