5,649 research outputs found
Chirality transfer and stereo-selectivity of imprinted cholesteric networks
Imprinting of cholesteric textures in a polymer network is a method of
preserving a macroscopically chiral phase in a system with no molecular
chirality. By modifying the elastics properties of the network, the resulting
stored helical twist can be manipulated within a wide range since the
imprinting efficiency depends on the balance between the elastics constants and
twisting power at network formation. One spectacular property of phase
chirality imprinting is the created ability of the network to adsorb
preferentially one stereo-component from a racemic mixture. In this paper we
explore this property of chirality transfer from a macroscopic to the molecular
scale. In particular, we focus on the competition between the phase chirality
and the local nematic order. We demonstrate that it is possible to control the
subsequent release of chiral solvent component from the imprinting network and
the reversibility of the stereo-selective swelling by racemic solvents
Robust Temporally Coherent Laplacian Protrusion Segmentation of 3D Articulated Bodies
In motion analysis and understanding it is important to be able to fit a
suitable model or structure to the temporal series of observed data, in order
to describe motion patterns in a compact way, and to discriminate between them.
In an unsupervised context, i.e., no prior model of the moving object(s) is
available, such a structure has to be learned from the data in a bottom-up
fashion. In recent times, volumetric approaches in which the motion is captured
from a number of cameras and a voxel-set representation of the body is built
from the camera views, have gained ground due to attractive features such as
inherent view-invariance and robustness to occlusions. Automatic, unsupervised
segmentation of moving bodies along entire sequences, in a temporally-coherent
and robust way, has the potential to provide a means of constructing a
bottom-up model of the moving body, and track motion cues that may be later
exploited for motion classification. Spectral methods such as locally linear
embedding (LLE) can be useful in this context, as they preserve "protrusions",
i.e., high-curvature regions of the 3D volume, of articulated shapes, while
improving their separation in a lower dimensional space, making them in this
way easier to cluster. In this paper we therefore propose a spectral approach
to unsupervised and temporally-coherent body-protrusion segmentation along time
sequences. Volumetric shapes are clustered in an embedding space, clusters are
propagated in time to ensure coherence, and merged or split to accommodate
changes in the body's topology. Experiments on both synthetic and real
sequences of dense voxel-set data are shown. This supports the ability of the
proposed method to cluster body-parts consistently over time in a totally
unsupervised fashion, its robustness to sampling density and shape quality, and
its potential for bottom-up model constructionComment: 31 pages, 26 figure
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