The original mean shift algorithm [1] on Euclidean spaces (MS) was extended in [2] to operate on general Riemannian manifolds. This extension is extrinsic (Ext-MS) since the mode seeking is performed on the tangent spaces [3], where the underlying curvature is not fully considered (tangent spaces are only valid in a small neighborhood). In [3] was proposed an intrinsic mean shift designed to op...

Mean shift is an iterative mode-seeking algorithm widely used in pattern recognition and computer vision. However, its convergence is sometimes too slow to be practical. In this paper, we improve the convergence speed of mean shift by dynamically updating the sample set during the iterations, and the resultant procedure is called dynamic mean shift (DMS). When the data is locally Gaussian, it c...

In many geodetic applications a large number of observations are being measured to estimate the unknown parameters. The unbiasedness property of the estimated parameters is only ensured if there is no bias (e.g. systematic effect) or falsifying observations, which are also known as outliers. One of the most important steps towards obtaining a coherent analysis for the parameter estimation is th...

Object tracking algorithms, when it comes to implementing it on hardware ASIC, it becomes difficult task, due to certain limitations in hardware. This paper shows how mean-shift algorithm is implemented in HDL along with the description of ports and interfaces. Keywords— Object tracking, complexity in hardware ASIC, Mean Shift algorithm, Histogram, Bhattacharya coefficient

Mean-Shift tracking is a popular algorithm for object tracking since it is easy to implement and it is fast and robust. In this paper, we address the problem of scale adaptation of the Hellinger distance based Mean-Shift tracker. We start from a theoretical derivation of scale estimation in the Mean-Shift framework. To make the scale estimation robust and suitable for tracking, we introduce reg...

To settle out the problem that search of speaker change point (SCP) is blind and exhaustive, mean shift is proposed to seek SCP by estimating the kernel density of speech stream in this paper. It contains three steps: seeking peak points using mean shift firstly, using maximum likelihood ratio (MLR) to compute the MLR value of the peak points secondly, and seeking SCPs from MLR value using the ...

In the Data Streaming world, screening for outliers is an often overlooked aspect of the data preparation phase, which is needed to rationalize inferences drawn from the analysis of data. In this paper, we examine the effects of three outlier screens: A Trimming Window, The Box-Plot Screen and the Mahalanobis Screen on the market performance profile of firms traded on the NASDAQ and NYSE. From ...

In many real-world applications, data come with corruptions, large errors or outliers. One popular approach is to use -norm function. However, the robustness of -norm function is not well understood so far. In this paper, we present a new outlier regularization framework to understand and analyze the robustness of -norm function. There are two main features for the proposed outlier regularizati...