33,562 research outputs found
On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities
Current object recognition methods fail on object sets that include both
diffuse, reflective and transparent materials, although they are very common in
domestic scenarios. We show that a combination of cues from multiple sensor
modalities, including specular reflectance and unavailable depth information,
allows us to capture a larger subset of household objects by extending a state
of the art object recognition method. This leads to a significant increase in
robustness of recognition over a larger set of commonly used objects.Comment: 12 page
Deep learning in computational microscopy
We propose to use deep convolutional neural networks (DCNNs) to perform 2D and 3D computational imaging. Specifically, we investigate three different applications. We first try to solve the 3D inverse scattering problem based on learning a huge number of training target and speckle pairs. We also demonstrate a new DCNN architecture to perform Fourier ptychographic Microscopy (FPM) reconstruction, which achieves high-resolution phase recovery with considerably less data than standard FPM. Finally, we employ DCNN models that can predict focused 2D fluorescent microscopic images from blurred images captured at overfocused or underfocused planes.Published versio
Shape: A 3D Modeling Tool for Astrophysics
We present a flexible interactive 3D morpho-kinematical modeling application
for astrophysics. Compared to other systems, our application reduces the
restrictions on the physical assumptions, data type and amount that is required
for a reconstruction of an object's morphology. It is one of the first publicly
available tools to apply interactive graphics to astrophysical modeling. The
tool allows astrophysicists to provide a-priori knowledge about the object by
interactively defining 3D structural elements. By direct comparison of model
prediction with observational data, model parameters can then be automatically
optimized to fit the observation. The tool has already been successfully used
in a number of astrophysical research projects.Comment: 13 pages, 11 figures, accepted for publication in the "IEEE
Transactions on Visualization and Computer Graphics
3D differential phase contrast microscopy
We demonstrate 3D phase and absorption recovery from partially coherent intensity images captured with a programmable LED array source. Images are captured through-focus with four different illumination patterns. Using first Born and weak object approximations (WOA), a linear 3D differential phase contrast (DPC) model is derived. The partially coherent transfer functions relate the sample's complex refractive index distribution to intensity measurements at varying defocus. Volumetric reconstruction is achieved by a global FFT-based method, without an intermediate 2D phase retrieval step. Because the illumination is spatially partially coherent, the transverse resolution of the reconstructed field achieves twice the NA of coherent systems and improved axial resolution
3D Imaging of a Phase Object from a Single Sample Orientation Using an Optical Laser
Ankylography is a new 3D imaging technique, which, under certain
circumstances, enables reconstruction of a 3D object from a single sample
orientation. Here, we provide a matrix rank analysis to explain the principle
of ankylography. We then present an ankylography experiment on a microscale
phase object using an optical laser. Coherent diffraction patterns are acquired
from the phase object using a planar CCD detector and are projected onto a
spherical shell. The 3D structure of the object is directly reconstructed from
the spherical diffraction pattern. This work may potentially open the door to a
new method for 3D imaging of phase objects in the visible light region.
Finally, the extension of ankylography to more complicated and larger objects
is suggested.Comment: 22 pages 5 figure
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