436,390 research outputs found
Mixed Hierarchy Network for Image Restoration
Image restoration is a long-standing low-level vision problem, e.g.,
deblurring and deraining. In the process of image restoration, it is necessary
to consider not only the spatial details and contextual information of
restoration to ensure the quality, but also the system complexity. Although
many methods have been able to guarantee the quality of image restoration, the
system complexity of the state-of-the-art (SOTA) methods is increasing as well.
Motivated by this, we present a mixed hierarchy network that can balance these
competing goals. Our main proposal is a mixed hierarchy architecture, that
progressively recovers contextual information and spatial details from degraded
images while we design intra-blocks to reduce system complexity. Specifically,
our model first learns the contextual information using encoder-decoder
architectures, and then combines them with high-resolution branches that
preserve spatial detail. In order to reduce the system complexity of this
architecture for convenient analysis and comparison, we replace or remove the
nonlinear activation function with multiplication and use a simple network
structure. In addition, we replace spatial convolution with global
self-attention for the middle block of encoder-decoder. The resulting tightly
interlinked hierarchy architecture, named as MHNet, delivers strong performance
gains on several image restoration tasks, including image deraining, and
deblurring
Image patch analysis and clustering of sunspots: a dimensionality reduction approach
Sunspots, as seen in white light or continuum images, are associated with
regions of high magnetic activity on the Sun, visible on magnetogram images.
Their complexity is correlated with explosive solar activity and so classifying
these active regions is useful for predicting future solar activity. Current
classification of sunspot groups is visually based and suffers from bias.
Supervised learning methods can reduce human bias but fail to optimally
capitalize on the information present in sunspot images. This paper uses two
image modalities (continuum and magnetogram) to characterize the spatial and
modal interactions of sunspot and magnetic active region images and presents a
new approach to cluster the images. Specifically, in the framework of image
patch analysis, we estimate the number of intrinsic parameters required to
describe the spatial and modal dependencies, the correlation between the two
modalities and the corresponding spatial patterns, and examine the phenomena at
different scales within the images. To do this, we use linear and nonlinear
intrinsic dimension estimators, canonical correlation analysis, and
multiresolution analysis of intrinsic dimension.Comment: 5 pages, 7 figures, accepted to ICIP 201
Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification
Despite the steady progress in video analysis led by the adoption of
convolutional neural networks (CNNs), the relative improvement has been less
drastic as that in 2D static image classification. Three main challenges exist
including spatial (image) feature representation, temporal information
representation, and model/computation complexity. It was recently shown by
Carreira and Zisserman that 3D CNNs, inflated from 2D networks and pretrained
on ImageNet, could be a promising way for spatial and temporal representation
learning. However, as for model/computation complexity, 3D CNNs are much more
expensive than 2D CNNs and prone to overfit. We seek a balance between speed
and accuracy by building an effective and efficient video classification system
through systematic exploration of critical network design choices. In
particular, we show that it is possible to replace many of the 3D convolutions
by low-cost 2D convolutions. Rather surprisingly, best result (in both speed
and accuracy) is achieved when replacing the 3D convolutions at the bottom of
the network, suggesting that temporal representation learning on high-level
semantic features is more useful. Our conclusion generalizes to datasets with
very different properties. When combined with several other cost-effective
designs including separable spatial/temporal convolution and feature gating,
our system results in an effective video classification system that that
produces very competitive results on several action classification benchmarks
(Kinetics, Something-something, UCF101 and HMDB), as well as two action
detection (localization) benchmarks (JHMDB and UCF101-24).Comment: ECCV 2018 camera read
An Experimental Examination of Spatial DecisionSupport System Effectiveness: The Roles of Task Complexity and Technology
Alaboratory experiment was used to investigate the effects on decision maker performance of using geographic information system (GIS) technology as a spatial decision support system (SDSS). The research examined two independent variables: task complexity (i.e., low, medium, and high complexity, and SDSS use (i.e., no SDSS versus SDSS support). Professionals who are experienced decision makers completed a site location task that required decisions to be made based upon spatially-referenced information. The results confirm the hypotheses and show that SDSS use and task complexity both have an important impact on decision quality and solution time. The study builds upon and extends image theory as a basis for explaining efficiency differences resulting from differing graphical displays of spatial informatio
Subwavelength edge detection through trapped resonances in waveguides
Lenses that can collect the perfect image of an object must restore
propagative and evanescent waves. However, for efficient information transfer,
e.g., in compressed sensing, it is often desirable to detect only the fast
spatial variations of the wave field (carried by evanescent waves), as the one
created by edges or small details. Image processing edge detection algorithms
perform such operation but they add time and complexity to the imaging process.
Here, we present a new subwavelength approach that generates an image of only
those components of the acoustic field that are equal to or smaller than the
operating wavelength. The proposed technique converts evanescent waves into
propagative waves exciting trapped resonances in a waveguide, and it uses
periodicity to attenuate the propagative components. This approach achieves
resolutions about an order of magnitude smaller than the operating wavelength
and makes it possible to visualize independently edges aligned along different
directions
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