Feature selection, as a preprocessing step to machine learning, has been effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improvin...
Filter cache(FC) is effective in achieving energy saving at the expense of some performance degradation. The energy savings, here, comes from repeated execution of tiny loops from...
The likelihood models used in probabilistic visual tracking applications are often complex non-linear and/or nonGaussian functions, leading to analytically intractable inference. ...
In recent years particle filters have become a tremendously popular tool to perform tracking for non-linear and/or non-Gaussian models. This is due to their simplicity, generality...
Particle Filter methods are one of the dominant tracking paradigms due to its ability to handle non-gaussian processes, multimodality and temporal consistency. Traditionally, the e...