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...

GrabCut is perhaps the most powerful semi-automatic algorithm for matting presented to date. In its existing form, it is not suitable for video object segmentation. This paper considers major extensions that make it suitable for this purpose. A method for initialising matting without user intervention is presented, followed by a more robust data model using a Mean Shift algorithm to control mod...

In this work, we present a fingertip tracking framework which allows observation of finger movements in task space. By applying a multi-scale edge extraction technique, an edge map is generated in which low contrast edges are preserved while noise is suppressed. Based on circular image features, determined from the map using Hough transform, the fingertips are accurately tracked by combining a ...

In this paper, we propose a maximum a posteriori formulation to the multiple target tracking problem. We adopt a graph representation for storing the detected regions as well as their association over time. The multiple target tracking problem is formulated as a multiple paths search in the graph. Due to the noisy foreground segmentation, an object may be represented by several foreground regio...

This paper proposes a multiple hypothesis tracking for multiple object tracking with moving camera. The proposed model makes use of the stability of sparse optical flow along with the invariant colour property under size and pose variation, by merging the colour property of objects into optical flow tracking. To evaluate the algorithm five different videos are selected from broadcast horse race...

This paper uses a rich set of student transcript data to estimate the economic cost incurred by a university when it does not adopt a `mean-shift grading policy' to fight grade inflation. We show that even in the face of moral hazard constraints a university can enhance its profitability by fighting grade inflation with a distributionshifting policy.