19,598 research outputs found
Two-Stream Action Recognition-Oriented Video Super-Resolution
We study the video super-resolution (SR) problem for facilitating video
analytics tasks, e.g. action recognition, instead of for visual quality. The
popular action recognition methods based on convolutional networks, exemplified
by two-stream networks, are not directly applicable on video of low spatial
resolution. This can be remedied by performing video SR prior to recognition,
which motivates us to improve the SR procedure for recognition accuracy.
Tailored for two-stream action recognition networks, we propose two video SR
methods for the spatial and temporal streams respectively. On the one hand, we
observe that regions with action are more important to recognition, and we
propose an optical-flow guided weighted mean-squared-error loss for our
spatial-oriented SR (SoSR) network to emphasize the reconstruction of moving
objects. On the other hand, we observe that existing video SR methods incur
temporal discontinuity between frames, which also worsens the recognition
accuracy, and we propose a siamese network for our temporal-oriented SR (ToSR)
training that emphasizes the temporal continuity between consecutive frames. We
perform experiments using two state-of-the-art action recognition networks and
two well-known datasets--UCF101 and HMDB51. Results demonstrate the
effectiveness of our proposed SoSR and ToSR in improving recognition accuracy.Comment: Accepted to ICCV 2019. Code:
https://github.com/AlanZhang1995/TwoStreamS
Complementarity of PALM and SOFI for super-resolution live cell imaging of focal adhesions
Live cell imaging of focal adhesions requires a sufficiently high temporal
resolution, which remains a challenging task for super-resolution microscopy.
We have addressed this important issue by combining photo-activated
localization microscopy (PALM) with super-resolution optical fluctuation
imaging (SOFI). Using simulations and fixed cell focal adhesion images, we
investigated the complementarity between PALM and SOFI in terms of spatial and
temporal resolution. This PALM-SOFI framework was used to image focal adhesions
in living cells, while obtaining a temporal resolution below 10 s. We
visualized the dynamics of focal adhesions, and revealed local mean velocities
around 190 nm per minute. The complementarity of PALM and SOFI was assessed in
detail with a methodology that integrates a quantitative resolution and
signal-to-noise metric. This PALM and SOFI concept provides an enlarged
quantitative imaging framework, allowing unprecedented functional exploration
of focal adhesions through the estimation of molecular parameters such as the
fluorophore density and the photo-activation and photo-switching rates
Recommended from our members
Improving Precipitation Estimation Using Convolutional Neural Network
Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. We introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation-related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation-related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation-related parameterization schemes using a data-driven approach
- …