12,175 research outputs found
Joint Learning of Intrinsic Images and Semantic Segmentation
Semantic segmentation of outdoor scenes is problematic when there are
variations in imaging conditions. It is known that albedo (reflectance) is
invariant to all kinds of illumination effects. Thus, using reflectance images
for semantic segmentation task can be favorable. Additionally, not only
segmentation may benefit from reflectance, but also segmentation may be useful
for reflectance computation. Therefore, in this paper, the tasks of semantic
segmentation and intrinsic image decomposition are considered as a combined
process by exploring their mutual relationship in a joint fashion. To that end,
we propose a supervised end-to-end CNN architecture to jointly learn intrinsic
image decomposition and semantic segmentation. We analyze the gains of
addressing those two problems jointly. Moreover, new cascade CNN architectures
for intrinsic-for-segmentation and segmentation-for-intrinsic are proposed as
single tasks. Furthermore, a dataset of 35K synthetic images of natural
environments is created with corresponding albedo and shading (intrinsics), as
well as semantic labels (segmentation) assigned to each object/scene. The
experiments show that joint learning of intrinsic image decomposition and
semantic segmentation is beneficial for both tasks for natural scenes. Dataset
and models are available at: https://ivi.fnwi.uva.nl/cv/intrinsegComment: ECCV 201
A Special Homotopy Continuation Method For A Class of Polynomial Systems
A special homotopy continuation method, as a combination of the polyhedral
homotopy and the linear product homotopy, is proposed for computing all the
isolated solutions to a special class of polynomial systems. The root number
bound of this method is between the total degree bound and the mixed volume
bound and can be easily computed. The new algorithm has been implemented as a
program called LPH using C++. Our experiments show its efficiency compared to
the polyhedral or other homotopies on such systems. As an application, the
algorithm can be used to find witness points on each connected component of a
real variety
Statistical Signatures of Photon Localization
The realization that electron localization in disordered systems (Anderson
localization) is ultimately a wave phenomenon has led to the suggestion that
photons could be similarly localized by disorder. This conjecture attracted
wide interest because the differences between photons and electrons - in their
interactions, spin statistics, and methods of injection and detection - may
open a new realm of optical and microwave phenomena, and allow a detailed study
of the Anderson localization transition undisturbed by the Coulomb interaction.
To date, claims of three-dimensional photon localization have been based on
observations of the exponential decay of the electromagnetic wave as it
propagates through the disordered medium. But these reports have come under
close scrutiny because of the possibility that the decay observed may be due to
residual absorption, and because absorption itself may suppress localization.
Here we show that the extent of photon localization can be determined by a
different approach - measurement of the relative size of fluctuations of
certain transmission quantities. The variance of relative fluctuations
accurately reflects the extent of localization, even in the presence of
absorption. Using this approach, we demonstrate photon localization in both
weakly and strongly scattering quasi-one-dimensional dielectric samples and in
periodic metallic wire meshes containing metallic scatterers, while ruling it
out in three-dimensional mixtures of aluminum spheres.Comment: 5 pages, including 4 figure
A mixed reality telepresence system for collaborative space operation
This paper presents a Mixed Reality system that results from the integration of a telepresence system and an application to improve collaborative space exploration. The system combines free viewpoint video with immersive projection technology to support non-verbal communication, including eye gaze, inter-personal distance and facial expression. Importantly, these can be interpreted together as people move around the simulation, maintaining natural social distance. The application is a simulation of Mars, within which the collaborators must come to agreement over, for example, where the Rover should land and go.
The first contribution is the creation of a Mixed Reality system supporting contextualization of non-verbal communication. Tw technological contributions are prototyping a technique to subtract a person from a background that may contain physical objects and/or moving images, and a light weight texturing method for multi-view rendering which provides balance in terms of visual and temporal quality. A practical contribution is the demonstration of pragmatic approaches to sharing space between display systems of distinct levels of immersion. A research tool contribution is a system that allows comparison of conventional authored and video based reconstructed avatars, within an environment that encourages exploration and social interaction. Aspects of system quality, including the communication of facial expression and end-to-end latency are reported
Collaborative virtual reality platform for visualizing space data and mission planning
This paper presents the system architecture of a collaborative virtual environment in which distributed multidisciplinary teams involved in space exploration activities come together and explore areas of scientific interest of a planet for future missions. The aim is to reduce the current challenges of distributed scientific and engineering meetings that prevent the exploitation of
their collaborative potential, as, at present, expertise, tools and datasets are fragmented. This paper investigates the functional characteristics of a software framework that addresses these challenges following the design science research methodology in the context of the space industry and research.
An implementation of the proposed architecture and a validation process with end users, based on the execution of different use cases, are described. These use cases cover relevant aspects of real science analysis and operation, including planetary data visualization, as the system aims at being used in future European missions. This validation suggests that the system has the
potential to enhance the way space scientists will conduct space science research in the future
Reduction of the size of datasets by using evolutionary feature selection: the case of noise in a modern city
Smart city initiatives have emerged to mitigate the negative effects of a very fast growth of urban areas. Most of the population in our cities are exposed to high levels of noise that generate discomfort and different health problems. These issues may be mitigated by applying different smart cities solutions, some of them require high accurate noise information to provide the best quality of serve possible. In this study, we have designed a machine learning approach based on genetic algorithms to analyze noise data captured in the university campus. This method reduces the amount of data required to classify the noise by addressing a feature selection optimization problem. The experimental results have shown that our approach improved the accuracy in 20% (achieving an accuracy of 87% with a reduction of up to 85% on the original dataset).Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech.
This research has been partially funded by the Spanish MINECO and FEDER projects TIN2016-81766-REDT (http://cirti.es), and TIN2017-88213-R (http://6city.lcc.uma.es)
Internalizing Symptoms Mediate the Relation Between Acute Pain and Autism in Adults.
Research on pain in autism spectrum disorder (ASD) is in its infancy, with almost nothing known about how individual differences may predicting pain response in ASD. In the present study, 45 adults (28 male, age 22-48Â years) with diagnoses of autism and intellectual delay were observed during vaccination or dental cleaning and their pain behaviours coded and measures of autism symptom severity, anxiety, depression and obsessivity taken. Our findings showed that greater autism severity predicted greater pain response which was partially mediated by anxiety and depression. These data suggest that mental health symptoms are important when considering pain response in autism. Mood must therefore be considered in future research on pain in ASD as well as clinical pain management
Randomized Reference Classifier with Gaussian Distribution and Soft Confusion Matrix Applied to the Improving Weak Classifiers
In this paper, an issue of building the RRC model using probability
distributions other than beta distribution is addressed. More precisely, in
this paper, we propose to build the RRR model using the truncated normal
distribution. Heuristic procedures for expected value and the variance of the
truncated-normal distribution are also proposed. The proposed approach is
tested using SCM-based model for testing the consequences of applying the
truncated normal distribution in the RRC model. The experimental evaluation is
performed using four different base classifiers and seven quality measures. The
results showed that the proposed approach is comparable to the RRC model built
using beta distribution. What is more, for some base classifiers, the
truncated-normal-based SCM algorithm turned out to be better at discovering
objects coming from minority classes.Comment: arXiv admin note: text overlap with arXiv:1901.0882
Dense Motion Estimation for Smoke
Motion estimation for highly dynamic phenomena such as smoke is an open
challenge for Computer Vision. Traditional dense motion estimation algorithms
have difficulties with non-rigid and large motions, both of which are
frequently observed in smoke motion. We propose an algorithm for dense motion
estimation of smoke. Our algorithm is robust, fast, and has better performance
over different types of smoke compared to other dense motion estimation
algorithms, including state of the art and neural network approaches. The key
to our contribution is to use skeletal flow, without explicit point matching,
to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this
paper we describe our algorithm in greater detail, and provide experimental
evidence to support our claims.Comment: ACCV201
Ageing, sex and cardioprotection
Translation of cardioprotective interventions aimed at reducing myocardial injury during ischaemia-reperfusion from experimental studies to clinical practice is an important yet unmet need in cardiovascular medicine. One particular challenge facing translation is the existence of demographic and clinical factors that influence the pathophysiology of ischaemia-reperfusion injury of the heart and the effects of treatments aimed at preventing it. Among these factors, age and sex are prominent and have a recognised role in the susceptibility and outcome of ischaemic heart disease. Remarkably, some of the most powerful cardioprotective strategies proven to be effective in young animals become ineffective during ageing. This article reviews the mechanisms and implications of the modulatory effects of ageing and sex on myocardial ischaemia-reperfusion injury and their potential effects on cardioprotective interventions
- …