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...

In this paper we introduce a natural image prior that directly represents a Gaussiansmoothed version of the natural image distribution. We include our prior in a formulation of image restoration as a Bayes estimator that also allows us to solve noise-blind image restoration problems. We show that the gradient of our prior corresponds to the mean-shift vector on the natural image distribution. I...

The goal of this project was to develop a fast video image segmentation routine which could be used as a preprocessing step for motion tracking. We chose mean shift [1] as the primary algorithm. Our implementation includes several enhancements including dynamically adjusting the kernel bandwidth based on the overall level of image noise, and keeping a cache of past moves to avoid repeated compu...

Generation of a reference standard from multiple manually annotated datasets is a non-trivial problem. This paper discusses the weighted averaging of 3D open curves, which we used to generate a reference standard for vessel tracking data. We show how weighted averaging can be implemented by applying the Mean Shift algorithm to paths, and discuss the details of our implementation. Our approach c...