CG images are rendered using perspective projections. However, we sometimes experience discomfort while observing these images. The reason is that the perspective projection images do not represent the visual impression of real space. Therefore, human visual characteristics can contribute to obtaining more realistic CG. Thus, a magnification rate function was proposed to represent the relationship between the subjective perception of the size and viewing distance of the object to be rendered. The image to which the magnification function was applied was closer to the impression of real space than the perspective projection image. In this study, we propose a method for generating images by applying a magnification rate function to panoramic images. This method enables the reproduction of the impression a person feels in real space as an image, even if it is a panoramic image.
Human vision has the ability to perceive moving objects sharply, which is called motion sharpening. Our research group has proposed an image generation method that predicts the visibility of moving objects based on this visual property. However, conventional methods can only predict the appearance of objects in liner motion at a constant speed. In addition, the validity of the method has been verified only for relatively slow-moving objects. In this study, we developed an image generation method that predicts the appearance of rotating figures. The validity of the proposed method was verified by including faster moving objects. This technique enables us to design the appearance of moving objects, which was not possible before, and contributes to the extension of design methods.
This study proposes a method for detecting micro-vibrations along the optical axis of a target object using video image analysis. The proposed method detects vibrations as time variances in the scaling factor of the object by checking the shift in the peaks in the spatial power spectrum of the images. We conducted an experiment wherein a checkerboard placed 2 m below the video camera was vibrated using an actuator. The results show that the proposed method could measure the vibration frequency even when the amplitude is 0.25 permille of the shooting distance.
Human vision is capable of motion sharpening, where blurred edges look sharper while moving than when stationary. This phenomenon is an optical illusion and an important function in human vision. In this study, we propose a transformation and an inverse transformation method for simulating the motion sharpening phenomenon. Initially, we developed a digital filter based on the impulse response of the human vision. Then, we generated an image using the created filter and performed a comparison experiment between the unfiltered and filtered images. As this method provides images simulating the appearance of moving objects, it is possible to design the appearance of objects when they are moving and not just when they are stationary.
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.
KEYWORDS: Signal to noise ratio, Stochastic processes, Visual process modeling, Human vision and color perception, Interference (communication), Image enhancement, Signal detection, Digital image processing, Brain, Data processing
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.
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