8,787 research outputs found
Fast Multi-frame Stereo Scene Flow with Motion Segmentation
We propose a new multi-frame method for efficiently computing scene flow
(dense depth and optical flow) and camera ego-motion for a dynamic scene
observed from a moving stereo camera rig. Our technique also segments out
moving objects from the rigid scene. In our method, we first estimate the
disparity map and the 6-DOF camera motion using stereo matching and visual
odometry. We then identify regions inconsistent with the estimated camera
motion and compute per-pixel optical flow only at these regions. This flow
proposal is fused with the camera motion-based flow proposal using fusion moves
to obtain the final optical flow and motion segmentation. This unified
framework benefits all four tasks - stereo, optical flow, visual odometry and
motion segmentation leading to overall higher accuracy and efficiency. Our
method is currently ranked third on the KITTI 2015 scene flow benchmark.
Furthermore, our CPU implementation runs in 2-3 seconds per frame which is 1-3
orders of magnitude faster than the top six methods. We also report a thorough
evaluation on challenging Sintel sequences with fast camera and object motion,
where our method consistently outperforms OSF [Menze and Geiger, 2015], which
is currently ranked second on the KITTI benchmark.Comment: 15 pages. To appear at IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2017). Our results were submitted to KITTI 2015 Stereo
Scene Flow Benchmark in November 201
SlicerAstro: a 3-D interactive visual analytics tool for HI data
SKA precursors are capable of detecting hundreds of galaxies in HI in a
single 12 hours pointing. In deeper surveys one will probe more easily faint HI
structures, typically located in the vicinity of galaxies, such as tails,
filaments, and extraplanar gas. The importance of interactive visualization has
proven to be fundamental for the exploration of such data as it helps users to
receive immediate feedback when manipulating the data. We have developed
SlicerAstro, a 3-D interactive viewer with new analysis capabilities, based on
traditional 2-D input/output hardware. These capabilities enhance the data
inspection, allowing faster analysis of complex sources than with traditional
tools. SlicerAstro is an open-source extension of 3DSlicer, a multi-platform
open source software package for visualization and medical image processing.
We demonstrate the capabilities of the current stable binary release of
SlicerAstro, which offers the following features: i) handling of FITS files and
astronomical coordinate systems; ii) coupled 2-D/3-D visualization; iii)
interactive filtering; iv) interactive 3-D masking; v) and interactive 3-D
modeling. In addition, SlicerAstro has been designed with a strong, stable and
modular C++ core, and its classes are also accessible via Python scripting,
allowing great flexibility for user-customized visualization and analysis
tasks.Comment: 18 pages, 11 figures, Accepted by Astronomy and Computing.
SlicerAstro link: https://github.com/Punzo/SlicerAstro/wiki#get-slicerastr
HiTRACE: High-throughput robust analysis for capillary electrophoresis
Motivation: Capillary electrophoresis (CE) of nucleic acids is a workhorse
technology underlying high-throughput genome analysis and large-scale chemical
mapping for nucleic acid structural inference. Despite the wide availability of
CE-based instruments, there remain challenges in leveraging their full power
for quantitative analysis of RNA and DNA structure, thermodynamics, and
kinetics. In particular, the slow rate and poor automation of available
analysis tools have bottlenecked a new generation of studies involving hundreds
of CE profiles per experiment.
Results: We propose a computational method called high-throughput robust
analysis for capillary electrophoresis (HiTRACE) to automate the key tasks in
large-scale nucleic acid CE analysis, including the profile alignment that has
heretofore been a rate-limiting step in the highest throughput experiments. We
illustrate the application of HiTRACE on thirteen data sets representing 4
different RNAs, three chemical modification strategies, and up to 480 single
mutant variants; the largest data sets each include 87,360 bands. By applying a
series of robust dynamic programming algorithms, HiTRACE outperforms prior
tools in terms of alignment and fitting quality, as assessed by measures
including the correlation between quantified band intensities between replicate
data sets. Furthermore, while the smallest of these data sets required 7 to 10
hours of manual intervention using prior approaches, HiTRACE quantitation of
even the largest data sets herein was achieved in 3 to 12 minutes. The HiTRACE
method therefore resolves a critical barrier to the efficient and accurate
analysis of nucleic acid structure in experiments involving tens of thousands
of electrophoretic bands.Comment: Revised to include Supplement. Availability: HiTRACE is freely
available for download at http://hitrace.stanford.ed
CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images
With the powerfulness of convolution neural networks (CNN), CNN based face
reconstruction has recently shown promising performance in reconstructing
detailed face shape from 2D face images. The success of CNN-based methods
relies on a large number of labeled data. The state-of-the-art synthesizes such
data using a coarse morphable face model, which however has difficulty to
generate detailed photo-realistic images of faces (with wrinkles). This paper
presents a novel face data generation method. Specifically, we render a large
number of photo-realistic face images with different attributes based on
inverse rendering. Furthermore, we construct a fine-detailed face image dataset
by transferring different scales of details from one image to another. We also
construct a large number of video-type adjacent frame pairs by simulating the
distribution of real video data. With these nicely constructed datasets, we
propose a coarse-to-fine learning framework consisting of three convolutional
networks. The networks are trained for real-time detailed 3D face
reconstruction from monocular video as well as from a single image. Extensive
experimental results demonstrate that our framework can produce high-quality
reconstruction but with much less computation time compared to the
state-of-the-art. Moreover, our method is robust to pose, expression and
lighting due to the diversity of data.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence, 201
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