The likelihood models used in probabilistic visual tracking applications are often complex non-linear and/or nonGaussian functions, leading to analytically intractable inference. ...
Stochastic gradient descent (SGD) uses approximate gradients estimated from subsets of the training data and updates the parameters in an online fashion. This learning framework i...
In this paper we are interested in the joint reconstruction of geometry and photometry of scenes with multiple moving objects from a collection of motion-blurred images. We make s...
Although the Neighborhood Pattern Sensitive Fault (NPSF) model is recognized as a high quality fault model for memory arrays, the excessive test application time cost associated wi...
Modern use of FPGAs as hardware accelerators involves the partial reconfiguration of hardware resources as the application executes. In this paper, we present a polynomial time al...