Efficient probability density function estimation is of primary interest in statistics. A popular approach for achieving this is the use of finite Gaussian mixture models. Based on...
We present an unsupervised approach for learning a generative layered representation of a scene from a video for motion segmentation. The learnt model is a composition of layers, ...
M. Pawan Kumar, Philip H. S. Torr, Andrew Zisserma...
In this work, we propose a new paradigm called power emulation, which exploits hardware acceleration to drastically speedup power estimation. Power emulation is based on the obser...
Density estimation for observational data plays an integral role in a broad spectrum of applications, e.g. statistical data analysis and information-theoretic image registration. ...
In many modern video encoders, motion estimation is usually formulated as a Lagrangian cost function that balances motion prediction accuracy and the rate to transmit motion vecto...