introduction: in modern societies, shift work is an essential component of daily life. since the beginning of research in shift work, researchers have been engaged in the identification of factors predicting adjustment and adaptation to shift work. the concept of adaptation and tolerance refers mainly to the biological response in terms of circadian rhythms and performance efficiency as well as...

In this paper, the fuzzy coding histogram representation for an image region is proposed for visual tracking. At the initial frame, we use the fuzzy clustering technology on all pixels within the initial detection box to get the cluster prototypes, which are used to construct the fuzzy codebook. During the tracking period, the candidate image region is also represented by a fuzzy coding histogr...

Edges characterize boundaries, which define the important structural properties of an image. Therefore edge detection is a problem of fundamental importance in image processing; many tasks in image processing, to be performed successfully, depend on a reliable edge detection mechanism. Edges in images are areas with strong intensity contrasts – a jump in intensity from one pixel to its neighbor...

In this paper we present a parallel dynamic mean shift algorithm based on path transmission for medical volume data segmentation. The algorithm first translates the volume data into a joint position-color feature space subdivided uniformly by bandwidths, and then clusters points in feature space in parallel by iteratively finding its peak point. Over iterations it improves the convergent rate b...

The mean shift algorithm is a nonparametric clustering technique that does not make assumptions on the number of clusters and on their shapes. It achieves this goal by performing kernel density estimation, and iteratively locating the local maxima of the kernel mixture. The set of points that converge to the same mode defines a cluster. While appealing, the performance of the mean shift algorit...

In this paper, we investigate the use of the mean shift algorithm with respect to speaker clustering. The algorithm is an elegant nonparametric technique that has become very popular in image segmentation, video tracking and other image processing and computer vision tasks. Its primary aim is to detect the modes of the underlying density and consequently merge those observations being attracted...

In this paper we develop and validate a procedure for testing against a shift in mean in the observa-tions and hidden state sequence of state space models with Gaussian noise. State space models are popular for modelling stochastic networks as they allow to take into account that observations of the true state of a sys-tem may be corrupted by measurement noise (usually, a Gaussian noise process...

A scale and orientation adaptive mean shift tracking (SOAMST) algorithm is proposed in this paper to address the problem of how to estimate the scale and orientation changes of the target under the mean shift tracking framework. In the original mean shift tracking algorithm, the position of the target can be well estimated, while the scale and orientation changes can not be adaptively estimated...

Mean shift is a popular robust framework for statistical data analysis using kernel density estimation, originally proposed by Fukunaga and Hostetler in 70’s. Recently, due to the work by Cheng and Comaniciu, this method has been re-discovered and successfully applied to a wide range of vision applications. This article provides a comprehensive overview of the basic theory and applications of m...