In our previous study, we proposed a hand-waving finger vein authentication system, in which finger region extraction
from captured images was effective to verify the finger vein patterns with high accuracy. However, it is not easy to
extract the correct finger region from grayscale images that are taken with a near infra-red LED, because the background
condition of the captured image changes complicatedly according to the location of the waving hand. In order to
overcome this limitation, we propose an alternative finger region extraction method that takes color images with an RGB
camera and a white light source and identifies the finger region based on the skin color information.
In this paper, we propose a method for human detection from low-resolution camera images. The proposed method uses video images as input and uses 3D-CNN for classification, which is an extension of 2D-CNN and that can take into account temporal features such as gait motion. In our experiments, we used Caltech Pedestrian Detection Benchmark to make datasets of low-resolution still and video images and compared the performance between 2D-CNN and 3D-CNN. As a result, 3D-CNN with low-resolution video images achieved 91.8 % accuracy rate, 99.0 % precision rate, and 82.8 % recall rate, and showed higher performance than 2D-CNN with low-resolution images, and comparable performance than 2D-CNN with high-resolution images.
We propose a method for obtaining clear underwater images by tracking the motion of suspended matter from video
images captured in water and by separating the images into foreground and background. We assume that input images
are the superposition of a foreground and a background, and constructed a transition model and the observation model.
An input image is divided into patches and tracking of the foreground in each patch is performed while applying Kalman
filter to separate the input images into the foreground and the background. From the result of the experiment using
simulated images, we confirmed that the background images were successfully estimated and a region that was moving
slowly was also recognized as a part of the background.
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