1,138 research outputs found
The Application of Preconditioned Alternating Direction Method of Multipliers in Depth from Focal Stack
Post capture refocusing effect in smartphone cameras is achievable by using
focal stacks. However, the accuracy of this effect is totally dependent on the
combination of the depth layers in the stack. The accuracy of the extended
depth of field effect in this application can be improved significantly by
computing an accurate depth map which has been an open issue for decades. To
tackle this issue, in this paper, a framework is proposed based on
Preconditioned Alternating Direction Method of Multipliers (PADMM) for depth
from the focal stack and synthetic defocus application. In addition to its
ability to provide high structural accuracy and occlusion handling, the
optimization function of the proposed method can, in fact, converge faster and
better than state of the art methods. The evaluation has been done on 21 sets
of focal stacks and the optimization function has been compared against 5 other
methods. Preliminary results indicate that the proposed method has a better
performance in terms of structural accuracy and optimization in comparison to
the current state of the art methods.Comment: 15 pages, 8 figure
Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets
A data augmentation methodology is presented and applied to generate a large
dataset of off-axis iris regions and train a low-complexity deep neural
network. Although of low complexity the resulting network achieves a high level
of accuracy in iris region segmentation for challenging off-axis eye-patches.
Interestingly, this network is also shown to achieve high levels of performance
for regular, frontal, segmentation of iris regions, comparing favorably with
state-of-the-art techniques of significantly higher complexity. Due to its
lower complexity, this network is well suited for deployment in embedded
applications such as augmented and mixed reality headsets
Smart Augmentation - Learning an Optimal Data Augmentation Strategy
A recurring problem faced when training neural networks is that there is
typically not enough data to maximize the generalization capability of deep
neural networks(DNN). There are many techniques to address this, including data
augmentation, dropout, and transfer learning. In this paper, we introduce an
additional method which we call Smart Augmentation and we show how to use it to
increase the accuracy and reduce overfitting on a target network. Smart
Augmentation works by creating a network that learns how to generate augmented
data during the training process of a target network in a way that reduces that
networks loss. This allows us to learn augmentations that minimize the error of
that network.
Smart Augmentation has shown the potential to increase accuracy by
demonstrably significant measures on all datasets tested. In addition, it has
shown potential to achieve similar or improved performance levels with
significantly smaller network sizes in a number of tested cases
Assessing societal vulnerability of U.S. Pacific Northwest communities to storm induced coastal change
Progressive increases in storm intensities and extreme wave heights have been documented along the U.S. West Coast. Paired with global sea level rise and the potential for an increase in El Niño occurrences, these trends have substantial implications for the vulnerability of coastal communities to natural coastal hazards. Community vulnerability to hazards is characterized by the exposure, sensitivity, and adaptive capacity of human-environmental systems that influence potential impacts. To demonstrate how societal vulnerability to coastal hazards varies with both physical and social factors, we compared community exposure and sensitivity to storm-induced coastal change scenarios in Tillamook (Oregon) and Pacific (Washington) Counties. While both are backed by low-lying coastal dunes, communities in these two counties have experienced different shoreline change histories and have chosen to use the adjacent land in different ways. Therefore, community vulnerability varies significantly between the two counties. Identifying the reasons for this variability can help land-use managers make decisions to increase community resilience and reduce vulnerability in spite of a changing climate. (PDF contains 4 pages
Analysis of interaction and co-editing patterns amongst OpenStreetMap contributors
OpenStreetMap (OSM) is a very well known and popular Volunteered Geographic Information (VGI) project on the Internet. In January 2013 OSM gained its one millionth registered member. Several studies have shown that only a small percentage of these registered members carry out the large majority of the mapping and map editing work. In this article we discuss results from a social-network based analysis of seven major cities in OSM in an effort to understand if there is quantitative evidence of interaction and collaboration between OSM members in these areas. Are OSM contributors working on their own to build OSM databases in these cities or is there evidence of collaboration between OSM contributors? We find that in many cases high frequent contributors (“senior mappers”) perform very large amounts of mapping work on their own but do interact (edit/update) contributions from lower frequency contributors
Integrating Volunteered Geographic Information into Pervasive Health Computing Applications
In this paper we describe the potential for using
Volunteered Geographic Information (VGI) in pervasive health
computing. We use the OpenStreetMap project as a case-study of
a successful VGI project and investigate how it can be expanded
and used as a source of spatial information for pervasive
computing technologies particularly in the area of access to
information on healthcare services
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