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Dominant motion detection is essential for automated video analysis. Traditional methods usually set an empirical threshold to detect pan/tilt/zoom. In this paper, we propose a novel approach of automatic detecting video dominate motion which is parameter-free. Based on the motion trajectories of feature points, the distribution of motion is estimated by kernel density estimation. A Kullback-Leibler...
Prediction of two dimensional hand trajectories from cortical surface recordings entails finding a functional mapping from spectral modulations in multidimensional channels to instantaneous hand positions. Such studies thus far have been conducted through linear adaptive filters, even though, the functional mapping from the cortical activity to behavior might be nonlinear. Herein, we employ a nonlinear...
The task of clustering multivariate trajectory data of varying length exists in various domains. Model-based methods are capable of handling varying length trajectories without changing the length or structure. Hidden Markov models (HMMs) are widely used for trajectory data modeling. However, HMMs are not suitable for trajectories of long duration. In this paper, we propose a similarity based representation...
A graph cuts method has recently attracted a lot of attention for image segmentation, as it can minimize an energy function composed of data term estimated in feature space and smoothness term estimated in an image domain. Although previous approaches using graph cuts have shown good performance for image segmentation, they manually obtained prior information to estimate the data term, thus automatic...
We propose a novel speaker recognition method that is used to compare the trajectories of continuous phonemes. The Gaussian Mixture Model has already been developed as a speaker recognition algorithm. However, Gaussian Mixture Model assume continuous speaker recognition of using only one input sample. To apply continuous observation approach, we propose a novel speaker recognition method to compare...
We present a novel method for incorporating prior knowledge about invariances in object recognition for discriminant analysis. In contrast to conventional isotropic regularization approaches, our approach shows how to incorporate known transformation invariances in the geometry of the problem to better regularize discriminant analysis. In particular, we show how to incorporate group invariance and...
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