Multidimensional data streams are a major paradigm in data science. This work focuses on possibilistic clustering algorithms as means to perform clustering of multidimensional streaming data. The proposed approach exploits fuzzy outlier analysis to provide good learning and tracking abilities in both concept shift and concept drift.

Facial landmark detection is an important but challenging task for real-world computer vision applications. This paper proposes an accurate and robust approach for facial landmark detection by combining data-driven and modeldriven methods. Firstly, a fully convolutional network (FCN) is trained to generate response maps of all facial landmark points. Such a data-driven method can make full use ...

A number of printing errors have appeared in [1] some of which were typos in the original manuscript, and others were introduced during the typesetting. Certain equations appeared only in half, while others were repeated twice. The authors had not had a chance to correct the galley proofs due to their absence from their contact addresses, and unfortunately, these errors went unnoticed until rec...

The mean-shift algorithm is an efficient technique for tracking 2D blobs through an image. Although the scale of the mean-shift kernel is a crucial parameter, there is presently no clean mechanism for choosing this scale or updating it while tracking blobs that are changing in size. In this paper, we adapt Lindeberg’s theory of feature scale selection based on local maxima of differential scale...

Mean-shift analysis is a general nonparametric clustering technique based on density estimation for the analysis of complex feature spaces. The algorithm consists of a simple iterative procedure that shifts each of the feature points to the nearest stationary point along the gradient directions of the estimated density function. It has been successfully applied to many applications such as segm...

In most low-level computer vision problems, very little information (if any) is known about the true underlying probability density function, such as its shape, number of mixture components, etc.. Due to this lack of knowledge, parametric approaches are less relevant, rather one has to rely on non-parametric methods. In this note we consider the construction and convergence proof of the non-par...

An object tracking algorithm using the Mean Shift framework is presented which is largely invariant to both partial and full occlusions, complex backgrounds and change in scale. Multiple features are used to gain a descriptive representation of the target object. Image moments are used to determine the scale of the target object. A kalman filter is used to successfully track the target object t...

The mean shift algorithm is a powerful clustering technique, which is based on an iterative scheme to detect modes in a probability density function. It has been utilized for image segmentation by seeking the modes in a feature space composed of spatial and color information. Although the modes of the feature space can be efficiently calculated in that scheme, different optimization techniques ...

In this paper, we present an improved mean shift for robust object tracking in complex environment. Traditional RGB color model used in mean shift tracker is sensitive to interference from similar background. In order to solve this problem, a new saliency-color target model is proposed through using the state-of-the-art target representation and updated background-weighed method. In addition, t...