45,271 research outputs found
Human-centric light sensing and estimation from RGBD images: the invisible light switch
Lighting design in indoor environments is of primary importance for at least two reasons: 1) people should perceive an adequate light; 2) an effective lighting design means consistent energy saving. We present the Invisible Light Switch (ILS) to address both aspects. ILS dynamically adjusts the room illumination level to save energy while maintaining constant the light level perception of the users. So the energy saving is invisible to them. Our proposed ILS leverages a radiosity model to estimate the light level which is perceived by a person within an indoor environment, taking into account the person position and her/his viewing frustum (head pose). ILS may therefore dim those luminaires, which are not seen by the user, resulting in an effective energy saving, especially in large open offices (where light may otherwise be ON everywhere for a single person). To quantify the system performance, we have collected a new dataset where people wear luxmeter devices while working in office rooms. The luxmeters measure the amount of light (in Lux) reaching the people gaze, which we consider a proxy to their illumination level perception. Our initial results are promising: in a room with 8 LED luminaires, the energy consumption in a day may be reduced from 18585 to 6206 watts with ILS (currently needing 1560 watts for operations). While doing so, the drop in perceived lighting decreases by just 200 lux, a value considered negligible when the original illumination level is above 1200 lux, as is normally the case in offices
Human-centric light sensing and estimation from RGBD images: The invisible light switch
Lighting design in indoor environments is of primary importance for at least
two reasons: 1) people should perceive an adequate light; 2) an effective
lighting design means consistent energy saving. We present the Invisible Light
Switch (ILS) to address both aspects. ILS dynamically adjusts the room
illumination level to save energy while maintaining constant the light level
perception of the users. So the energy saving is invisible to them. Our
proposed ILS leverages a radiosity model to estimate the light level which is
perceived by a person within an indoor environment, taking into account the
person position and her/his viewing frustum (head pose). ILS may therefore dim
those luminaires, which are not seen by the user, resulting in an effective
energy saving, especially in large open offices (where light may otherwise be
ON everywhere for a single person). To quantify the system performance, we have
collected a new dataset where people wear luxmeter devices while working in
office rooms. The luxmeters measure the amount of light (in Lux) reaching the
people gaze, which we consider a proxy to their illumination level perception.
Our initial results are promising: in a room with 8 LED luminaires, the energy
consumption in a day may be reduced from 18585 to 6206 watts with ILS
(currently needing 1560 watts for operations). While doing so, the drop in
perceived lighting decreases by just 200 lux, a value considered negligible
when the original illumination level is above 1200 lux, as is normally the case
in offices
LiveCap: Real-time Human Performance Capture from Monocular Video
We present the first real-time human performance capture approach that
reconstructs dense, space-time coherent deforming geometry of entire humans in
general everyday clothing from just a single RGB video. We propose a novel
two-stage analysis-by-synthesis optimization whose formulation and
implementation are designed for high performance. In the first stage, a skinned
template model is jointly fitted to background subtracted input video, 2D and
3D skeleton joint positions found using a deep neural network, and a set of
sparse facial landmark detections. In the second stage, dense non-rigid 3D
deformations of skin and even loose apparel are captured based on a novel
real-time capable algorithm for non-rigid tracking using dense photometric and
silhouette constraints. Our novel energy formulation leverages automatically
identified material regions on the template to model the differing non-rigid
deformation behavior of skin and apparel. The two resulting non-linear
optimization problems per-frame are solved with specially-tailored
data-parallel Gauss-Newton solvers. In order to achieve real-time performance
of over 25Hz, we design a pipelined parallel architecture using the CPU and two
commodity GPUs. Our method is the first real-time monocular approach for
full-body performance capture. Our method yields comparable accuracy with
off-line performance capture techniques, while being orders of magnitude
faster
Deep and superficial amygdala nuclei projections revealed in vivo by probabilistic tractography
Copyright © 2011 Society for Neuroscience and the authors. The The Journal of Neuroscience uses a Creative Commons Attribution-NonCommercial-ShareAlike licence: http://creativecommons.org/licenses/by-nc-sa/4.0/.Despite a homogenous macroscopic appearance on magnetic resonance images, subregions of the amygdala express distinct functional profiles as well as corresponding differences in connectivity. In particular, histological analysis shows stronger connections for superficial (i.e., centromedial and cortical), compared with deep (i.e., basolateral and other), amygdala nuclei to lateral orbitofrontal cortex and stronger connections of deep compared with superficial, nuclei to polymodal areas in the temporal pole. Here, we use diffusion weighted imaging with probabilistic tractography to investigate these connections in humans. We use a data-driven approach to segment the amygdala into two subregions using k-means clustering. The identified subregions are spatially contiguous and their location corresponds to deep and superficial nuclear groups. Quantification of the connection strength between these amygdala clusters and individual target regions corresponds to qualitative histological findings in non-human primates, indicating such findings can be extrapolated to humans. We propose that connectivity profiles provide a potentially powerful approach for in vivo amygdala parcellation and can serve as a guide in studies that exploit functional and anatomical neuroimaging.The Wellcome Trust, a Max Planck Research Award and Swiss National Science Foundation
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