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Color images taken in low light scenes are deteriorated with noise and motion blur. The simultaneous reduction of noise and motion blur from the low-light color images is difficult because the imposed noise hinders accurate motion blur kernel estimation. To overcome this problem, we build a novel imaging system using a single sensor that captures red, green, blue (RGB) and near-infrared (NIR) images...
We propose a method for disparity estimation in stereo video. We address the problems associated with spatially-temporally-correlated disparity variations (STCDV). STCDV problems are caused by complex motions, e.g., yaw-rotation, pan-tilt-zoom camera movements, etc. The key novelty of this study is to introduce a spatio-temporal disparity hyperplane (STDH) model. The proposed STDH model represents...
In this paper, we propose a novel method for light field imaging. Previous systems are difficult to obtain multi-viewpoint images (sub-images) at the high-resolution. In order to overcome this problem, we propose a two-layer light field imaging system by using an organic photoelectric conversion film (OPCF). Our imaging system places the OPCF having the green spectral sensitivity onto the micro-lens...
In this paper, we propose a method to enhance the color image of a low-light scene by using a single sensor that simultaneously captures red, green, blue (RGB) and near-infrared (NIR) information. Typical image enhancement methods require two cameras to simultaneously capture color and NIR images. In such cases, meticulous calibration is required to adjust the pixel positions of the two cameras. By...
Noise and blur are annoying, common problems in low-light photography. In this paper, we propose a novel framework to reconstruct a blur and noise-free color image sequence using near-infrared (NIR) images. In extremely low light, previous works may fail in color image restoration because both heavy noise and motion blur are produced simultaneously. To overcome these essential problems, we augment...
We propose an appearance-based head pose estimation method that can be automatically adapted to individual scenes. Appearance-based estimation methods usually require a ground-truth dataset taken from a scene which is similar to test video sequences. However, it is almost impossible to acquire many manually-labeled head images for each scene. To address the problem, we introduce a new approach for...
In this work, we propose a method for tracking individuals in crowds. Our method is based on a trajectory-based clustering approach that groups trajectories of image features that belong to the same person. The key novelty of our method is to make use of a person's individuality, that is, the gait features and the temporal consistency of local appearance to track each individual in a crowd. Gait features...
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