105,478 research outputs found
The Multiphase Halo of NGC 891: WIYN H-alpha and BVI Imaging
We present new, deep optical images (BVI+H-alpha) of the interstellar medium
(ISM) far above the plane of NGC 891. These sub-arcsecond images give a direct
visual view of two physically distinct ``phases'' of the thick interstellar
disk of this galaxy. A dense phase of the thick disk ISM is observed in our BVI
images as highly-structured dust-bearing clouds viewed against the stellar
light of the galaxy. These structures are traceable to heights |z|=2 kpc from
the midplane. Very few highly-structured dust features are present at |z|>2
kpc. The more prominent dust structures have gas masses in excess of 10^5 solar
masses, each having visual extinctions well in excess of unity. A warm ionized
phase of the high-z ISM is observed through its well-studied H-alpha emission.
Our images of the well-studied diffuse ionized medium, to date the
highest-resolution observations of this phase of the ISM in NGC 891, show it is
relatively smoothly distributed with some filamentary structure superposed on
this smooth background. There is little correspondence between the H-alpha
emitting material and the absorbing dust structures. These two phases of the
multiphase high-z ISM are physically distinct. The H-alpha emission is being
heavily extincted in many places by the dense dust-bearing medium. Our H-alpha
observations show evidence for several discrete H II regions at large distances
from the midplane (to |z|=2 kpc). The presence of these H II regions in the
thick disk of NGC 891 suggests that on-going star formation may be present in
some of the dense, high-z clouds visible in our images. (Abstract Abridged)Comment: Accepted for publication in The Astronomical Journal. 38 pages,
including 6 embedded tables and 14 embedded figures. Interested readers are
strongly encouraged to download a version with high-resolution figures from
http://fuse.pha.jhu.edu/~howk/Papers/papers.htm
Blending Learning and Inference in Structured Prediction
In this paper we derive an efficient algorithm to learn the parameters of
structured predictors in general graphical models. This algorithm blends the
learning and inference tasks, which results in a significant speedup over
traditional approaches, such as conditional random fields and structured
support vector machines. For this purpose we utilize the structures of the
predictors to describe a low dimensional structured prediction task which
encourages local consistencies within the different structures while learning
the parameters of the model. Convexity of the learning task provides the means
to enforce the consistencies between the different parts. The
inference-learning blending algorithm that we propose is guaranteed to converge
to the optimum of the low dimensional primal and dual programs. Unlike many of
the existing approaches, the inference-learning blending allows us to learn
efficiently high-order graphical models, over regions of any size, and very
large number of parameters. We demonstrate the effectiveness of our approach,
while presenting state-of-the-art results in stereo estimation, semantic
segmentation, shape reconstruction, and indoor scene understanding
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
Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching
Leveraging on the recent developments in convolutional neural networks
(CNNs), matching dense correspondence from a stereo pair has been cast as a
learning problem, with performance exceeding traditional approaches. However,
it remains challenging to generate high-quality disparities for the inherently
ill-posed regions. To tackle this problem, we propose a novel cascade CNN
architecture composing of two stages. The first stage advances the recently
proposed DispNet by equipping it with extra up-convolution modules, leading to
disparity images with more details. The second stage explicitly rectifies the
disparity initialized by the first stage; it couples with the first-stage and
generates residual signals across multiple scales. The summation of the outputs
from the two stages gives the final disparity. As opposed to directly learning
the disparity at the second stage, we show that residual learning provides more
effective refinement. Moreover, it also benefits the training of the overall
cascade network. Experimentation shows that our cascade residual learning
scheme provides state-of-the-art performance for matching stereo
correspondence. By the time of the submission of this paper, our method ranks
first in the KITTI 2015 stereo benchmark, surpassing the prior works by a
noteworthy margin.Comment: Accepted at ICCVW 2017. The first two authors contributed equally to
this pape
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
Exploring Context with Deep Structured models for Semantic Segmentation
State-of-the-art semantic image segmentation methods are mostly based on
training deep convolutional neural networks (CNNs). In this work, we proffer to
improve semantic segmentation with the use of contextual information. In
particular, we explore `patch-patch' context and `patch-background' context in
deep CNNs. We formulate deep structured models by combining CNNs and
Conditional Random Fields (CRFs) for learning the patch-patch context between
image regions. Specifically, we formulate CNN-based pairwise potential
functions to capture semantic correlations between neighboring patches.
Efficient piecewise training of the proposed deep structured model is then
applied in order to avoid repeated expensive CRF inference during the course of
back propagation. For capturing the patch-background context, we show that a
network design with traditional multi-scale image inputs and sliding pyramid
pooling is very effective for improving performance. We perform comprehensive
evaluation of the proposed method. We achieve new state-of-the-art performance
on a number of challenging semantic segmentation datasets including ,
-, , -, -,
-, and datasets. Particularly, we report an
intersection-over-union score of on the - dataset.Comment: 16 pages. Accepted to IEEE T. Pattern Analysis & Machine
Intelligence, 2017. Extended version of arXiv:1504.0101
- …