767 research outputs found
Improved depth recovery in consumer depth cameras via disparity space fusion within cross-spectral stereo.
We address the issue of improving depth coverage in consumer depth cameras based on the combined use of cross-spectral stereo and near infra-red structured light sensing. Specifically we show that fusion of disparity over these modalities, within the disparity space image, prior to disparity optimization facilitates the recovery of scene depth information in regions where structured light sensing fails. We show that this joint approach, leveraging disparity information from both structured light and cross-spectral sensing, facilitates the joint recovery of global scene depth comprising both texture-less object depth, where conventional stereo otherwise fails, and highly reflective object depth, where structured light (and similar) active sensing commonly fails. The proposed solution is illustrated using dense gradient feature matching and shown to outperform prior approaches that use late-stage fused cross-spectral stereo depth as a facet of improved sensing for consumer depth cameras
Evidence for local lattice distortions in giant magnetocapacitive CdCr2S4
Raman scattering experiments on CdCr2S4 single crystals show pronounced
anomalies in intensity and frequency of optical phonon modes with an onset
temperature T*=130 K that coincides with the regime of giant magnetocapacitive
effects. A loss of inversion symmetry and Cr off-centering are deduced from the
observation of longitudinal optical and formerly infrared active modes for
T<T_c=84 K. The intensity anomalies are attributed to the enhanced electronic
polarizability of displacements that modulate the Cr-S distance and respective
hybridization. Photo doping leads to an annihilation of the symmetry reduction.
Our scenario of multiferroic effects is based on the near degeneracy of polar
and nonpolar modes and the additional low energy scale due to hybridization.Comment: 4 pages, 6 figure
Improved Depth Recovery In Consumer Depth Cameras via Disparity Space Fusion within Cross-spectral Stereo
We address the issue of improving depth coverage in consumer depth cameras based on the combined use of cross-spectral stereo and near infra-red structured light sensing. Specifically we show that fusion of disparity over these modalities, within the disparity space image, prior to disparity optimization facilitates the recovery of scene depth information in regions where structured light sensing fails. We show that this joint approach, leveraging disparity information from both structured light and cross-spectral sensing, facilitates the joint recovery of global scene depth comprising both texture-less object depth, where conventional stereo otherwise fails, and highly reflective object depth, where structured light (and similar) active sensing commonly fails. The proposed solution is illustrated using dense gradient feature matching and shown to outperform prior approaches that use late-stage fused cross-spectral stereo depth as a facet of improved sensing for consumer depth cameras
Gauge Invariant Factorisation and Canonical Quantisation of Topologically Massive Gauge Theories in Any Dimension
Abelian topologically massive gauge theories (TMGT) provide a topological
mechanism to generate mass for a bosonic p-tensor field in any spacetime
dimension. These theories include the 2+1 dimensional Maxwell-Chern-Simons and
3+1 dimensional Cremmer-Scherk actions as particular cases. Within the
Hamiltonian formulation, the embedded topological field theory (TFT) sector
related to the topological mass term is not manifest in the original phase
space. However through an appropriate canonical transformation, a gauge
invariant factorisation of phase space into two orthogonal sectors is feasible.
The first of these sectors includes canonically conjugate gauge invariant
variables with free massive excitations. The second sector, which decouples
from the total Hamiltonian, is equivalent to the phase space description of the
associated non dynamical pure TFT. Within canonical quantisation, a likewise
factorisation of quantum states thus arises for the full spectrum of TMGT in
any dimension. This new factorisation scheme also enables a definition of the
usual projection from TMGT onto topological quantum field theories in a most
natural and transparent way. None of these results rely on any gauge fixing
procedure whatsoever.Comment: 1+25 pages, no figure
Random interactions and spin-glass thermodynamic transition in the hole-doped Haldane system YCaBaNiO
Magnetization, DC and AC bulk susceptibility of the =1 Haldane chain
system doped with electronic holes, YCaBaNiO
(0x0.20), have been measured and analyzed. The most striking
results are (i) a sub-Curie power law behavior of the linear susceptibility,
, for temperature lower than the Haldane gap
of the undoped compound (x=0) (ii) the existence of a spin-glass thermodynamic
transition at = 2-3 K. These findings are consistent with (i) random
couplings within the chains between the spin degrees of freedom induced by hole
doping, (ii) the existence of ferromagnetic bonds that induce magnetic
frustration when interchain interactions come into play at low temperature.Comment: 4 pages, 4 figures, to appear in Phys. Rev.
Exploiting the BrĂžnsted acidity of phosphinecarboxamides for the synthesis of new phosphides and phosphines
International audienc
Real-time Classification of Vehicle Types within Infra-red Imagery
Real-time classification of vehicles into sub-category types poses a significant challenge within infra-red imagery due to the high levels of intra-class variation in thermal vehicle signatures caused by aspects of design, current operating duration and ambient thermal conditions. Despite these challenges, infra-red sensing offers significant generalized target object detection advantages in terms of all-weather operation and invariance to visual camouflage techniques. This work investigates the accuracy of a number of real-time object classification approaches for this task within the wider context of an existing initial object detection and tracking framework. Specifically we evaluate the use of traditional feature-driven bag of visual words and histogram of oriented gradient classification approaches against modern convolutional neural network architectures. Furthermore, we use classical photogrammetry, within the context of current target detection and classification techniques, as a means of approximating 3D target position within the scene based on this vehicle type classification. Based on photogrammetric estimation of target position, we then illustrate the use of regular Kalman filter based tracking operating on actual 3D vehicle trajectories. Results are presented using a conventional thermal-band infra-red (IR) sensor arrangement where targets are tracked over a range of evaluation scenarios
- âŠ