Fast natural feature tracking is essential to make markerless augmented reality applications practical on low performance mobile devices. To speed up the natural feature tracking process which includes computationally expensive procedures, we propose a novel fast tracking method using optical flow aimed for mobile augmented reality applications. Experimental results showed that the proposed met...

We investigate and extend the cross entropy (CE) approach for parameter estimation for ODE and DDE models introduced in [27]. Software is developed to allow models to be easily specified for use with an existing package for working with DDEs. Our software implements CE and is used to explore potential improvements to the method. A 3-stage optimization procedure is presented. First, an inexpensi...

This paper presents a comparison between methods that estimate motion of a camera from a sequence of video images. We have implemented two methods– a homography based method that assumes planar environments, and shape-from-motion, a general method that can deal with a fully three dimensional world. Both methods have been formulated in an iterative, online form to produce estimates of camera mot...

We study Kolmogorov-Smirnov goodness of fit tests for evaluating distributional hypotheses where unknown parameters need to be fitted. Following work of Pollard (1979), our approach uses a Cramérvon Mises minimum distance estimator for parameter estimation. The asymptotic null distribution of the resulting test statistic is represented by invariance principle arguments as a functional of a Brow...

Most recent approaches to monocular 3D pose estimation rely on Deep Learning. They either train a Convolutional Neural Network to directly regress from image to 3D pose, which ignores the dependencies between human joints, or model these dependencies via a max-margin structured learning framework, which involves a high computational cost at inference time. In this paper, we introduce a Deep Lea...

The motion estimator is a key component of any MPEG-2 video coder. It is common practice to use a method based on block-matching, which generates vectors designed to yield good predictions of images to be coded, without regard for whether the vectors correspond to actual motion. This Report describes a method of deriving motion vectors for an MPEG-2 coder from a motion estimator designed to pro...

Many parameter estimation problems involve with a parameter-dependent PDEs with multiple right hand sides. The computational cost and memory requirements of such problems increases linearly with the number of right hand sides. For many applications this is the main bottleneck of the computation. In this paper we show that problems with multiple right hand sides can be reformulated as stochastic...

In this paper, we focus on parameters estimation of probabilistic models in discrete space. A naive calculation of the normalization constant of the probabilistic model on discrete space is often infeasible and statistical inference based on such probabilistic models has difficulty. In this paper, we propose a novel estimator for probabilistic models on discrete space, which is derived from an ...

Generally, there are two approaches for solving the problem of human pose estimation from monocular images. One is the learning-based approach, and the other is the model-based approach. The former method can estimate the poses rapidly but has the disadvantage of low estimation accuracy. While the latter method is able to accurately estimate the poses, its computational cost is high. In this pa...

The paper deals with the estimation problem for the generalized Pareto distribution based on progressive type-II censoring with random removals. The number of components removed at each failure time is assumed to follow a binomial distribution. Maximum likelihood estimators and the asymptotic variance-covariance matrix of the estimates are obtained. Finally, a numerical example is given to illu...