11,908 research outputs found
Separating Reflection and Transmission Images in the Wild
The reflections caused by common semi-reflectors, such as glass windows, can
impact the performance of computer vision algorithms. State-of-the-art methods
can remove reflections on synthetic data and in controlled scenarios. However,
they are based on strong assumptions and do not generalize well to real-world
images. Contrary to a common misconception, real-world images are challenging
even when polarization information is used. We present a deep learning approach
to separate the reflected and the transmitted components of the recorded
irradiance, which explicitly uses the polarization properties of light. To
train it, we introduce an accurate synthetic data generation pipeline, which
simulates realistic reflections, including those generated by curved and
non-ideal surfaces, non-static scenes, and high-dynamic-range scenes.Comment: accepted at ECCV 201
Learning to Extract Motion from Videos in Convolutional Neural Networks
This paper shows how to extract dense optical flow from videos with a
convolutional neural network (CNN). The proposed model constitutes a potential
building block for deeper architectures to allow using motion without resorting
to an external algorithm, \eg for recognition in videos. We derive our network
architecture from signal processing principles to provide desired invariances
to image contrast, phase and texture. We constrain weights within the network
to enforce strict rotation invariance and substantially reduce the number of
parameters to learn. We demonstrate end-to-end training on only 8 sequences of
the Middlebury dataset, orders of magnitude less than competing CNN-based
motion estimation methods, and obtain comparable performance to classical
methods on the Middlebury benchmark. Importantly, our method outputs a
distributed representation of motion that allows representing multiple,
transparent motions, and dynamic textures. Our contributions on network design
and rotation invariance offer insights nonspecific to motion estimation
Growth and characterization of binary and pseudo-binary 3-5 compounds exhibiting non-linear optical behavior. Undergraduate research opportunities in microgravity science and technology
In line with the specified objectives, a Bridgman-type growth configuration in which unavoidable end effects - conventionally leading to growth interface relocation - are compensated by commensurate input-power changes is developed; the growth rate on a microscale is predictable and unaffected by changes in heat transfer conditions. To permit quantitative characterization of the growth furnace cavity (hot-zone), a 3-D thermal field mapping technique, based on the thermal image, is being tested for temperatures up to 1100 C. Computational NIR absorption analysis was modified to now permit characterization of semi-insulating single crystals. Work on growth and characterization of bismuth-silicate was initiated. Growth of BSO (B12SiO20) for seed material by the Czochralski technique is currently in progress. Undergraduate research currently in progress includes: ground based measurements of the wetting behavior (contact angles) of semiconductor melts on substrates consisting of potential confinement materials for solidification experiments in a reduced gravity environment. Hardware modifications required for execution of the wetting experiments in a KC-135 facility are developed
- …