The tracking accuracy of mean shift algorithm employing the Bhattacharyya coefficient as similarity metric is low. The reason is analyzed on the basis of effect identity of the histogram bins in the target model and the candidate. A novel weighting method which is derived from the enhanced histogram intersection is proposed. Several improvements are considered in the implementation of the algor...

This paper deals with non-rigid object localization in an image, from object colors. Our method allows detection in an image of all the objects which correspond to a color model, without a priori information about their number. Our approach consists in creating a binary image, which represents the repartition of the most probable pixels to be part of the object. Considering this image as a clus...

Outlier detection concerns discovering some unusual data whose behavior is exceptional compared to other data. In contrast to non-spatial outliers which only consider non-spatial attributes, spatial outliers are defined to be those sites which are very different from its neighbors defined in terms of spatial attributes, i.e., locations. In this paper, we propose a local trimmed mean approach to...

To propose a gesture model updating and results forecasting algorithm based on Mean Shift, and to solve the problem of target model changing and influenced tracking results in gesture target tracking process. Firstly, the background difference and skin color detection methods are used to detect and get gesture model, and the Mean Shift algorithm is used to track gesture and update the gesture m...