A real time, kernel based, model-free tracking algorithm is proposed, which employs a weighted von Mises mixture as the target’s appearance model. The mixture weights, which are provided by a spatial kernel, along with the hue values are used in order to estimate the parameters of the weighted von Mises mixture model. The von Mises distribution is suitable for circular data and it is employed i...

A novel robust active appearance model (AAM) matching algorithm is presented. The method consists of two main stages. First, initial residuals are clustered by a non parametric mean shift mode detection step. Second, modes without gross outliers are selected using an objective function. Robustness of the matching procedure is demonstrated on a variety of examples with different noise conditions...

PURPOSE To determine the accuracy of smart devices (iPods) to measure intermittent noise and integrate a noise dose in the workplace. MATERIALS AND METHODS In experiment 1, four iPods were each paired with a Larson Davis Spark dosimeter and exposed to randomly fluctuating pink noise in a reverberant sound chamber. Descriptive statistics and the mean difference between the iPod and its paired ...

In this paper, we consider the fundamental problem of frequency estimation of multiple sinusoidal signals with stationary errors. We propose genetic algorithm and outlier-insensitive criterion function based technique for the frequency estimation problem. In the simulation studies and real life data analysis, it is observed that the proposed genetic algorithm based robust frequency estimators a...

In this paper, we propose an unsupervised segmentation algorithm for color images based on Gaussian mixture models (GMMs). The number of mixture components is determined automatically by adaptive mean shift, in which local clusters are estimated by repeatedly searching for higher density points in feature vector space. For the estimation of parameters of GMMs, themean field annealing expectatio...

This paper presents a special form of color correlogram as representation for object tracking and carries out a motion observability analysis to obtain the optimal simplified correlogram in a kernel based tracking framework. Compared with the color histogram, where the position information of each pixel is ignored, a simplified color correlogram (SCC) representation encodes the spatial informat...

The Mean Shift (MS) algorithm allows to identify clusters that are catchment areas of modes of a probability density function (pdf). We propose to use a multiscale and hierarchical implementation of the algorithm to process grid data of population and identify automatically urban centers and their dependant sub-centers through scales. The multiscale structure is obtained by increasing iterative...

Mean-Shift tracking gained a lot of popularity in computer vision community. This is due to its simplicity and robustness. However, the original formulation does not estimate the orientation of the tracked object. In this paper, we extend the original mean-shift tracker for orientation estimation. We use the gradient field as an orientation signature and introduce an efficient representation of...

In this paper we show the equivalence of three techniques used in image processing: local-mode finding, robust-estimation and mean-shift analysis. The computational common element in all these image operators is the spatial-tonal normalized convolution, an image operator that generalizes the bilateral filter.

In this article we model the log of the U.S. and the U.K. real oil prices in terms of fractionally integrated processes with a mean shift. We use different versions of the tests of Robinson (1994), which have standard null and local limit distributions. The results indicate that if we model the series without a mean shift, they are both nonstationary I(1). However, allowing for a mean shift dur...