In this work, boosting the efficiency of Mean-Shift Tracking using random sampling is proposed. We obtained the surprising result that mean-shift tracking requires only very few samples. Our experiments demonstrate that robust tracking can be achieved with as few as even 5 random samples from the image of the object. As the computational complexity is considerably reduced and becomes independen...

We propose a novel Mean-Shift method for data clustering, called Robust (RMS). A new update equation point iterates is proposed, mixing the ones of standard (MS) and Blurring (BMS). Despite its simplicity, proposed has not been studied so far. RMS can be set up in both kernel-based nearest-neighbor (NN)-based fashion. Since rule closer to BMS, convergence conjectured based on Chen’s BMS theorem...

The objective of this research is detection of outliers in multivariate data employing various distance measure, particularly using robust regression diagnosis technique. Several classical outlier identification methods are based on the sample mean and covariance matrix in general. But they do not always yield better result, as they themselves are affected by the outliers. Sometimes one outlier...