4,518 research outputs found
Visual Comfort Assessment for Stereoscopic Image Retargeting
In recent years, visual comfort assessment (VCA) for 3D/stereoscopic content
has aroused extensive attention. However, much less work has been done on the
perceptual evaluation of stereoscopic image retargeting. In this paper, we
first build a Stereoscopic Image Retargeting Database (SIRD), which contains
source images and retargeted images produced by four typical stereoscopic
retargeting methods. Then, the subjective experiment is conducted to assess
four aspects of visual distortion, i.e. visual comfort, image quality, depth
quality and the overall quality. Furthermore, we propose a Visual Comfort
Assessment metric for Stereoscopic Image Retargeting (VCA-SIR). Based on the
characteristics of stereoscopic retargeted images, the proposed model
introduces novel features like disparity range, boundary disparity as well as
disparity intensity distribution into the assessment model. Experimental
results demonstrate that VCA-SIR can achieve high consistency with subjective
perception
Constraints on large-extra-dimensions model through 125 GeV Higgs pair production at the LHC
Based on the analysis of 5 fb^-1 of data at the LHC, the ATLAS and CMS
collaborations have presented evidence for a Higgs boson with a mass in the 125
GeV range. We consider the 125 GeV neutral Higgs pair production process in the
context of large-extra-dimensions (LED) model including the Kaluza-Klein
(KK)excited gravitons at the LHC. We consider the standard model(SM) Higgs pair
production in gluon-gluon fusion channel and pure LED effects through graviton
exchange as well as their interferences. It is shown that such interferences
should be included; the LED model raises the transverse momentum (Pt)and
invariant mass (M_HH) distributions at high scales of Pt and M_HH of the Higgs
pair production. By using the Higgs pair production we could set the discovery
limit on the cutoff scale M_S up to 6 TeV for delta = 2 and 4.5 TeV for delta =
6.Comment: 6 figure
Methological quality of systematic reviews and meta-analyses on acupuncture for stroke: a review of review
Objective:
To assess the methodological quality of systematic reviews and meta-analyses regarding acupuncture intervention for stroke and the primary studies within them.
Methods:
Two researchers searched PubMed, Cumulative index to Nursing and Allied Health Literature, Embase, ISI Web of Knowledge, Cochrane, Allied and Complementary Medicine, Ovid Medline, Chinese Biomedical Literature Database, China National Knowledge Infrastructure, Wanfang and Traditional Chinese Medical Database to identify systematic reviews and meta-analyses about acupuncture for stroke published from the inception to December 2016. Review characteristics and the criteria for assessing the primary studies within reviews were extracted. The methodological quality of the reviews was assessed using adapted Oxman and Guyatt Scale. The methodological quality of primary studies was also assessed.
Results:
Thirty-two eligible reviews were identified, 15 in English and 17 in Chinese. The English reviews were scored higher than the Chinese reviews (P=0.025), especially in criteria for avoiding bias and the scope of search. All reviews used the quality criteria to evaluate the methodological quality of primary studies, but some criteria were not comprehensive. The primary studies, in particular the Chinese reviews, had problems with randomization, allocation concealment, blinding, dropouts and withdrawals, intent-to-treat analysis and adverse events.
Conclusions:
Important methodological flaws were found in Chinese systematic reviews and primary studies. It was necessary to improve the methodological quality and reporting quality of both the systematic reviews published in China and primary studies on acupuncture for stroke
DPF: Learning Dense Prediction Fields with Weak Supervision
Nowadays, many visual scene understanding problems are addressed by dense
prediction networks. But pixel-wise dense annotations are very expensive (e.g.,
for scene parsing) or impossible (e.g., for intrinsic image decomposition),
motivating us to leverage cheap point-level weak supervision. However, existing
pointly-supervised methods still use the same architecture designed for full
supervision. In stark contrast to them, we propose a new paradigm that makes
predictions for point coordinate queries, as inspired by the recent success of
implicit representations, like distance or radiance fields. As such, the method
is named as dense prediction fields (DPFs). DPFs generate expressive
intermediate features for continuous sub-pixel locations, thus allowing outputs
of an arbitrary resolution. DPFs are naturally compatible with point-level
supervision. We showcase the effectiveness of DPFs using two substantially
different tasks: high-level semantic parsing and low-level intrinsic image
decomposition. In these two cases, supervision comes in the form of
single-point semantic category and two-point relative reflectance,
respectively. As benchmarked by three large-scale public datasets
PASCALContext, ADE20K and IIW, DPFs set new state-of-the-art performance on all
of them with significant margins.
Code can be accessed at https://github.com/cxx226/DPF
NeRRF: 3D Reconstruction and View Synthesis for Transparent and Specular Objects with Neural Refractive-Reflective Fields
Neural radiance fields (NeRF) have revolutionized the field of image-based
view synthesis. However, NeRF uses straight rays and fails to deal with
complicated light path changes caused by refraction and reflection. This
prevents NeRF from successfully synthesizing transparent or specular objects,
which are ubiquitous in real-world robotics and A/VR applications. In this
paper, we introduce the refractive-reflective field. Taking the object
silhouette as input, we first utilize marching tetrahedra with a progressive
encoding to reconstruct the geometry of non-Lambertian objects and then model
refraction and reflection effects of the object in a unified framework using
Fresnel terms. Meanwhile, to achieve efficient and effective anti-aliasing, we
propose a virtual cone supersampling technique. We benchmark our method on
different shapes, backgrounds and Fresnel terms on both real-world and
synthetic datasets. We also qualitatively and quantitatively benchmark the
rendering results of various editing applications, including material editing,
object replacement/insertion, and environment illumination estimation. Codes
and data are publicly available at https://github.com/dawning77/NeRRF
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