Recent improvements in magnetic resonance image (MRI) reconstruction from partial data have been reported using spatial context modelling with Markov random field (MRF) priors. However, these algorithms have been developed only for magnitude images from single-coil measurements. In practice, most of the MRI images today are acquired using multi-coil data. In this paper, we extend our recent approach for MRI reconstruction with MRF priors to deal with multi-coil data i.e., to be applicable in parallel MRI (pMRI) settings. Instead of reconstructing images from different coils independently and subsequently combining them into the final image, we recover MRI image by processing jointly the undersampled measurements from all coils together with their estimated sensitivity maps. The proposed method incorporates a Bayesian formulation of the spatial context into the reconstruction problem. To solve the resulting problem, we derive an efficient algorithm based on the alternating direction method of multipliers (ADMM). Experimental results demonstrate the effectiveness of the proposed approach in comparison to some well-adopted methods for accelerated pMRI reconstruction from undersampled data.
With rapid increase of number of vehicles on roads it is necessary to maintain close monitoring of traffic. For this
purpose many surveillance cameras are placed along roads and on crossroads, creating a huge communication load
between the cameras and the monitoring center. Therefore, the data needs to be processed on site and transferred to the
monitoring centers in form of metadata or as a set of selected images. For this purpose it is necessary to detect events of
interest already on the camera side, which implies using smart cameras as visual sensors. In this paper we propose a
method for tracking of vehicles and analysis of vehicle trajectories to detect different traffic events. Kalman filtering is
used for tracking, combining foreground and optical flow measurements. Obtained vehicle trajectories are used to detect
different traffic events. Every new trajectory is compared with collection of normal routes and clustered accordingly. If
the observed trajectory differs from all normal routes more than a predefined threshold, it is marked as abnormal and the
alarm is raised. The system was developed and tested on Texas Instruments OMAP platform. Testing was done on four
different locations, two locations in the city and two locations on the open road.
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