Global motion is estimated either in the pixel domain or in block based domain. Until now, all the approaches regarding the latter are based on fixed sized blocks while the recent compression methods tend to use variable block sizes during motion estimation. In this paper we present a new procedure for global motion estimation based on a variable block size motion vector field. A block matching algorithm which is able to adapt the block size according to the motion complexity within the frame is used. The resulting motion vectors are employed for global motion estimation. Furthermore, binary foreground-background masks are created based on the frame-by-frame motion compensated differences by exploiting spatial conditions through anisotropic diffusion filtering. For global motion estimation the performance evaluation in terms of background PSNR shows an enhancement of more than 2.5 dB in the well-known ldquoStefanrdquo sequence, compared to the conventional case of fixed block size, at a reasonable implementation complexity

Several algorithms for global motion estimation in video sequences using pixel-or block-based approaches have been published. Most known pixel-based methods lack in performance while when using block-based algorithms working on motion vectors, robustness to outliers and accuracy is missing. In this paper we present the fundamentals of a significantly improved, robust block-based method for global motion estimation in compressed domain following the generic Helmholtz principle. To this aim, we use motion vector fields as provided by MPEG data streams. Background PSNR values for four motion compensated test sequences show that our new method delivers results comparable to more complex algorithms.

No abstract

A flexible, scaleable and cost-effective medical telemetry system is described for monitoring sleep-related disorders in the home environment. The system was designed and built for real-time data acquisition and processing, allowing for additional use in intensive care unit scenarios where rapid medical response is required in case of emergency. It comprises a wearable body area network of Zigbee-compatible wireless sensors worn by the subject, a central database repository residing in the medical centre and thin client workstations located at the subject's home and in the clinician's office. The system supports heterogeneous setup configurations, involving a variety of data acquisition sensors to suit several medical applications. All telemetry data is securely transferred and stored in the central database under the clinicians' ownership and control.

No abstract