In order to efficiently generate a high-quality computer-generated hologram (HQ-CGH), which requires that both a three-dimensional object image and its computer-generated hologram (CGH) are in high-definition resolution, we implement a fast CGH generation system using a scalable and flexible personal computer (PC) cluster. From experimental results obtained in generating a HQ-CGH with a CGH resolution of 1536×1536 and 2,155,898 light sources using a PC cluster comprising a server PC and nine client PCs, it is verified that the proposed system is approximately 4.7 times faster than a single PC with two high-performance GPUs.

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Hand shape is a natural and human-friendly interface for human-computer interaction. This paper proposes a realtime and 2D vision-based hand shape recognition method. The method is robust to hand pose changes because the hand pose is neutralized after recognizing a hand pose using distance transform, principal component analysis (PCA), and histogram analysis. Also, the context-based recognition method using shape decomposition can effectively recognize tiny changes of fingers. The method worked at 44.8 fps and had a recognition rate of 83% on average in the experiment with 800 images including 5 hand shapes and 16 hand poses.

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Newtonian reaction to blood influx into the head at each heartbeat causes subtle head motion at the same frequency as the heartbeats. Thus, this head motion can be used to estimate the heart rate. Several studies have shown that heart rates can be measured accurately by tracking head motion using a desktop computer with a static camera. However, implementation of vision-based head motion tracking on smartphones demonstrated limited accuracy due to the hand-shaking problem caused by the non-static camera. The hand-shaking problem could not be handled effectively with only the frontal camera images. It also required a more accurate method to measure the periodicity of noisy signals. Therefore, this study proposes an improved head-motion-based heart-rate monitoring system using smartphones. To address the hand-shaking problem, the proposed system leverages the front and rear cameras available in most smartphones and dedicates each camera to tracking facial features that correspond to head motion and background features that correspond to hand-shaking. Then, the locations of facial features are adjusted using the average point of the background features. In addition, a correlation-based signal periodicity computation method is proposed to accurately separate the true heart-rate-related component from the head motion signal. The proposed system demonstrates improved accuracy (i.e., lower mean errors in heart-rate measurement) compared to conventional head-motion-based systems, and the accuracy is sufficient for daily heart-rate monitoring.

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