Victor Stamatescu

We don’t have enough information about this author to calculate their statistics. If you think this is an error let us know.
Learn More
In this paper we investigate the benefit of augmenting data with synthetically created samples when training a machine learning classifier. Two approaches for creating additional training samples are data warping, which generates additional samples through transformations applied in the data-space, and synthetic over-sampling, which creates additional(More)
A critical issue for adaptive visual tracking is that of model drift, which occurs when the state space of the object of interest is polluted by observations that should have been attributed to background clutter. One approach to mitigating model drift in adaptive feature-learning visual tracking systems is to introduce prior information about the object of(More)
  • 1