In this paper, we present our design and implementation of a prototype system for quasi-realtime social network construction with Ambient Suite, which is a room-shaped environment with heterogeneous sensors. We also present experimental results and lessons learned from our experience.
We demonstrate a motion-resistant, three-wavelength, spatial frequency domain imaging (SFDI) system with ambient light suppression using a new 8-tap CMOS image sensor developed in our laboratory. Compared to the previous sensor (134×150), the new sensor's readout maximum frame rate has improved to 33fps from 6.28fps, and the new 700×540pixel sensor allows imaging at a higher spatial resolution over a larger field of view. Furthermore, the number of projected images needed per wavelength is reduced from three to two after applying the Hilbert transform. One image of planar illumination and one image for sinusoidal pattern projection at three wavelengths as well as one image of ambient light are captured by the 8-tap image sensor concurrently. The bias caused by ambient light is removed by subtracting the ambient light image from other images. Suppression of motion artifacts is achieved by reducing the exposure and projection time of each pattern. Sufficient signal level is maintained by repeating the exposure multiple times. In this study, LEDs with wavelengths of 554nm, 660nm, and 730nm were used to estimate oxy-/deoxyhemoglobin and melanin concentrations from in-vivo volar forearm skin.
We present a learning-based approach to estimate scene sparsity from time-of-flight measurements and address leakage artifacts to obtain improved demixing performance from matrix pencil method. Experiments are conducted in our simulator framework using the MitsubaToF renderer.
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