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We introduce a model-based reconstruction
framework with deep learned (DL) and smoothness regularization
on manifolds (STORM) priors to recover free
breathing and ungated (FBU) cardiac MRI from highly undersampled
measurements. The DL priors enable us to exploit
the local correlations, while the STORM prior enables
us to make use of the extensive non-local similarities that are
subject dependent. We introduce a novel model-based formulation
that allows the seamless integration of deep learning

This paper focuses on face spoofing detection using video. The purpose is to find out the best scheme for this task in the end-to-end learning manner. We investigate 4 different types of structure to fully exploit the raw data in its spatial-temporal domain, which are the pure CNN, CNN with 3D convolu-tion, CNN+LSTM and CNN+Conv-LSTM. Moreover, anoth-er stream built on optical flow is also used, and with a proper fusion method, it can improve the accuracy. In experiments, we compare schemes on the raw data in single stream and fusion methods with optical flow in two streams.

Fast Vehicle Detection with Lateral Convolutional Neural Network

In this paper, we present an approach for detecting faults within seismic volumes using a saliency detection framework that employs a 3D-FFT local spectra and multi-dimensional plane projections. The projection scheme divides a 3D-FFT local spectrum into three distinct components, each depicting variations along different dimensions of the data. To detect seismic structures oriented at different angles and to capture directional features within 3D volume, we modify the center-surround model to incorporate directional comparisons around each voxel.

Over the past few years, fast and robust trackers based on Kernelized Correlation Filters have shown top notch performance on the Visual Object Tracking challenge. However there is still scope for obtaining higher performance through the use of reasonable approximations that can easily be shown to work through empirical methods. We study some variants derived from the Discriminative Scale Space Tracker and show significant improvement in tracking performance.

Interior point methods have been known for decades to be useful for the resolution of small to medium size constrained optimization problems. These approaches have the benefit of ensuring feasibility of the iterates through a logarithmic barrier. We propose to incorporate a proximal forward-backward step in the resolution of the barrier subproblem to account for non-necessarily differentiable terms arising in the objective function.