1,483 research outputs found
Thermographic Laplacian-pyramid filtering to enhance delamination detection in concrete structure
Despite decades of efforts using thermography to detect delamination in
concrete decks, challenges still exist in removing environmental noise from
thermal images. The performance of conventional temperature-contrast approaches
can be significantly limited by environment-induced non-uniform temperature
distribution across imaging spaces. Time-series based methodologies were found
robust to spatial temperature non-uniformity but require the extended period to
collect data. A new empirical image filtering method is introduced in this
paper to enhance the delamination detection using blob detection method that
originated from computer vision. The proposed method employs a Laplacian of
Gaussian filter to achieve multi-scale detection of abnormal thermal patterns
by delaminated areas. Results were compared with the state-of-the-art methods
and benchmarked with time-series methods in the case of handling the
non-uniform heat distribution issue. To further evaluate the performance of the
method numerical simulations using transient heat transfer models were used to
generate the 'theoretical' noise-free thermal images for comparison.
Significant performance improvement was found compared to the conventional
methods in both indoor and outdoor tests. This methodology proved to be capable
to detect multi-size delamination using a single thermal image. It is robust to
the non-uniform temperature distribution. The limitations were discussed to
refine the applicability of the proposed procedure
Uncovering hidden nodes in complex networks in the presence of noise
We thank Dr. W.-X. Wang for discussions. This work was supported by AFOSR under Grant No. FA9550-10-1-0083 and by NSF under Grant. No. CDI-1026710, and by Basic Science Research Program of the Ministry of Education, Science and Technology under Grant No. NRF-2013R1A1A2010067, and by NSF under Grant No DMS-1100309 and by Heart Association under Grant No 11BGIA7440101.Peer reviewedPublisher PD
Learning Parallax Transformer Network for Stereo Image JPEG Artifacts Removal
Under stereo settings, the performance of image JPEG artifacts removal can be
further improved by exploiting the additional information provided by a second
view. However, incorporating this information for stereo image JPEG artifacts
removal is a huge challenge, since the existing compression artifacts make
pixel-level view alignment difficult. In this paper, we propose a novel
parallax transformer network (PTNet) to integrate the information from stereo
image pairs for stereo image JPEG artifacts removal. Specifically, a
well-designed symmetric bi-directional parallax transformer module is proposed
to match features with similar textures between different views instead of
pixel-level view alignment. Due to the issues of occlusions and boundaries, a
confidence-based cross-view fusion module is proposed to achieve better feature
fusion for both views, where the cross-view features are weighted with
confidence maps. Especially, we adopt a coarse-to-fine design for the
cross-view interaction, leading to better performance. Comprehensive
experimental results demonstrate that our PTNet can effectively remove
compression artifacts and achieves superior performance than other testing
state-of-the-art methods.Comment: 11 pages, 12 figures, ACM MM202
Data based reconstruction of complex geospatial networks, nodal positioning, and detection of hidden node
Funding This work was supported by ARO under grant no. W911NF-14-1-0504.Peer reviewedPublisher PD
Context-Aware Iteration Policy Network for Efficient Optical Flow Estimation
Existing recurrent optical flow estimation networks are computationally
expensive since they use a fixed large number of iterations to update the flow
field for each sample. An efficient network should skip iterations when the
flow improvement is limited. In this paper, we develop a Context-Aware
Iteration Policy Network for efficient optical flow estimation, which
determines the optimal number of iterations per sample. The policy network
achieves this by learning contextual information to realize whether flow
improvement is bottlenecked or minimal. On the one hand, we use iteration
embedding and historical hidden cell, which include previous iterations
information, to convey how flow has changed from previous iterations. On the
other hand, we use the incremental loss to make the policy network implicitly
perceive the magnitude of optical flow improvement in the subsequent iteration.
Furthermore, the computational complexity in our dynamic network is
controllable, allowing us to satisfy various resource preferences with a single
trained model. Our policy network can be easily integrated into
state-of-the-art optical flow networks. Extensive experiments show that our
method maintains performance while reducing FLOPs by about 40%/20% for the
Sintel/KITTI datasets.Comment: 2024, Association for the Advancement of Artificial Intelligenc
Uncertainty-Guided Spatial Pruning Architecture for Efficient Frame Interpolation
The video frame interpolation (VFI) model applies the convolution operation
to all locations, leading to redundant computations in regions with easy
motion. We can use dynamic spatial pruning method to skip redundant
computation, but this method cannot properly identify easy regions in VFI tasks
without supervision. In this paper, we develop an Uncertainty-Guided Spatial
Pruning (UGSP) architecture to skip redundant computation for efficient frame
interpolation dynamically. Specifically, pixels with low uncertainty indicate
easy regions, where the calculation can be reduced without bringing undesirable
visual results. Therefore, we utilize uncertainty-generated mask labels to
guide our UGSP in properly locating the easy region. Furthermore, we propose a
self-contrast training strategy that leverages an auxiliary non-pruning branch
to improve the performance of our UGSP. Extensive experiments show that UGSP
maintains performance but reduces FLOPs by 34%/52%/30% compared to baseline
without pruning on Vimeo90K/UCF101/MiddleBury datasets. In addition, our method
achieves state-of-the-art performance with lower FLOPs on multiple benchmarks.Comment: ACM Multimedia 202
Correlation Analysis for Protein Evolutionary Family Based on Amino Acid Position Mutations and Application in PDZ Domain
BACKGROUND: It has been widely recognized that the mutations at specific directions are caused by the functional constraints in protein family and the directional mutations at certain positions control the evolutionary direction of the protein family. The mutations at different positions, even distantly separated, are mutually coupled and form an evolutionary network. Finding the controlling mutative positions and the mutative network among residues are firstly important for protein rational design and enzyme engineering. METHODOLOGY: A computational approach, namely amino acid position conservation-mutation correlation analysis (CMCA), is developed to predict mutually mutative positions and find the evolutionary network in protein family. The amino acid position mutative function, which is the foundational equation of CMCA measuring the mutation of a residue at a position, is derived from the MSA (multiple structure alignment) database of protein evolutionary family. Then the position conservation correlation matrix and position mutation correlation matrix is constructed from the amino acid position mutative equation. Unlike traditional SCA (statistical coupling analysis) approach, which is based on the statistical analysis of position conservations, the CMCA focuses on the correlation analysis of position mutations. CONCLUSIONS: As an example the CMCA approach is used to study the PDZ domain of protein family, and the results well illustrate the distantly allosteric mechanism in PDZ protein family, and find the functional mutative network among residues. We expect that the CMCA approach may find applications in protein engineering study, and suggest new strategy to improve bioactivities and physicochemical properties of enzymes
FaFCNN: A General Disease Classification Framework Based on Feature Fusion Neural Networks
There are two fundamental problems in applying deep learning/machine learning
methods to disease classification tasks, one is the insufficient number and
poor quality of training samples; another one is how to effectively fuse
multiple source features and thus train robust classification models. To
address these problems, inspired by the process of human learning knowledge, we
propose the Feature-aware Fusion Correlation Neural Network (FaFCNN), which
introduces a feature-aware interaction module and a feature alignment module
based on domain adversarial learning. This is a general framework for disease
classification, and FaFCNN improves the way existing methods obtain sample
correlation features. The experimental results show that training using
augmented features obtained by pre-training gradient boosting decision tree
yields more performance gains than random-forest based methods. On the
low-quality dataset with a large amount of missing data in our setup, FaFCNN
obtains a consistently optimal performance compared to competitive baselines.
In addition, extensive experiments demonstrate the robustness of the proposed
method and the effectiveness of each component of the model\footnote{Accepted
in IEEE SMC2023}
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