Optically recorded analog holograms can reconstruct photorealistic three-dimensional (3D) images without the need for specialized eyewear. Computer-generated holograms (CGHs) are created by simulating the holographic recording process digitally rather than capturing them optically. Large-scale 3D still-image reconstruction with wide-viewing-zone can be achieved by mapping the amplitude or phase profiles of CGHs onto diffractive optical elements (DOEs), which modulate in-plane wavefront distribution using wavelength-scale surface structures. However, DOEs exhibit limited wavelength selectivity, interacting with a broad spectral range beyond their design wavelength. This results in reduced overall transmittance and typically requires three separate CGHs for full-color reconstruction. In this work, we present an "invisible holographic window," a transparent, surface-relief CGH patterned directly on glass via laser grayscale lithography. By encoding the real component of the interference pattern and reducing the phase-modulation range, the surface-relief structure transitions from deeply wrapped, jagged phase profiles to shallower and smoother sinusoidal phase profiles, which is associated with improved optical transmittance. Furthermore, a spatial-frequency-domain band-division multiplexing strategy is applied to support crosstalk-free, full-color 3D image reconstruction from a single transparent CGH, albeit with a viewing angle reduction. This platform generates photorealistic full-color 3D still-images in real space, offering new possibilities for transparent augmented-reality display interfaces such as those integrated into storefront windows, office glass partitions, and museum display cases. Our findings pave the way for advanced holographic display technologies and promise to accelerate research in the field.

We investigate whether it is possible to classify the gender of a standing person based on a video sequence containing body sway recorded by an overhead camera. Existing methods that extract a feature from the movement of a walking person for gender classification cannot detect the slight movements of a standing person. In this paper, we propose a method for extracting a feature from the body sway of a standing person. We design a spatio-temporal feature for representing body sway using the frequency analysis of time-series signals derived from the local movements of the upper body. To evaluate the accuracy of our method, we acquired video sequences of body sway from 30 females and 30 males using an overhead camera. We found that our method obtained 90.3±1.3% accuracy for the gender classification of a standing person. We compared the accuracy of our method with that of parameters based on medical data. We found that the proposed spatio-temporal feature extracted from body sway significantly improved gender classification accuracy.

We investigate the potentiality of sociable spotlights (sociable artifacts) to change the participants' roles by providing turn-yielding signals at the Transition Relevance Place (TRP) by changing the artifacts' behaviors (the effect of colors combined with the artifact behaviors as a bunch) that moot the state of the conversation.