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In High Efficiency Video Coding (HEVC), a rate control module usually relies on a bitrate model (representing the bitrate as a function of video content statistics and control parameters of the codec) to control the encoder to achieve the target bitrate. Existing bitrate models are not accurate due to their sensitivity to dynamic change of video content. To address this limitation, this paper proposes...
Tracking hypersonic glide reentry vehicles (HGRVs) is considered in the paper. Firstly, justified by an analysis of dynamic models of HGRVs, we proposed a more accurate motion model with less computation burden. Secondly, fixed-interval Gaussian mixture approximation smoother for non-linear Markov jump systems (NLMJSs) is presented in the paper. The Gaussian mixture filter can effectively approximate...
Under the common state space model for tracking a maneuvering target, the tracker needs to adapt its state transition model timely to match the target maneuver, which is usually carried out by finding the best one from a bank of candidate Markov models or employing all of them simultaneously but assigning different probabilities. Both methods suffer from time delay for confirming the target maneuver...
Facial age estimation is an important problem in the field of computer image processing. Because of the difficulty of data collection, one of the most challenges of facial age estimation is that there are not sufficient training data. Label distribution learning is an effective method to address this problem, where its motivation is that facial aging information on adjacent ages can be introduced...
The paradigm shift in construction cost estimation is attributed to technological innovations in the spheres of building information modelling (BIM) and industrialised building systems (IBS). Majority of construction projects experience cost overruns which affect project objectives. The outcome of cost overruns invariably leads to disputes and further delays. Similarly, cost estimators resistance...
Aircraft loss-of-control (LOC) is the major contributing factor to fatal accidents and is characterised by the manoeuvring of aircraft beyond the allowable flight envelopes. This paper proposes an online learning and inference based method for aircraft flight envelope estimation in order to prevent aircraft LOC. The lift and drag coefficients of the aircraft are identified online using an extended...
This paper presents a novel angular velocity estimation strategy of a Reaction Sphere (RS) for satellite attitude control based on a Linear Parameter-Varying (LPV) Kalman Filter. The reaction sphere is a permanent magnet synchronous spherical actuator whose rotor is magnetically levitated and can be accelerated about any desired axis. The spherical actuator is composed of an 8-pole permanent magnet...
Estimation of periodic quantities such as angles or phase values is a common problem. However, standard approaches, for example the Kalman filter and extensions thereof, have difficulties when estimating periodic quantities. To address this problem, circular filtering algorithms have been proposed but they are limited to just a single angle. In order to deal with multiple, possibly correlated angles,...
In this paper, a novel image moment-based model for extended object shape estimation and tracking is presented. A method to represent and estimate an elliptical shape using its image moments is first developed. The model of representing the shape of an object falls under the category of random hypersurface model (RHM) for extended object tracking. The moments are estimated using an unscented Kalman...
Different from representation learning models using deep learning to project original feature space into lower density ones, we propose a feature space learning (FSL) model based on a semi-supervised clustering framework. There are three main contributions in our approach: (1) Inspired by Zipf's law and word bursts, the feature space learning processes not only select trusted unlabeled samples and...
Visual attention is a dynamic search process of acquiring information. However, most previous studies have focused on the prediction of static attended locations. Without considering the temporal relationship of fixations, these models usually cannot explain the dynamic saccadic behavior well. In this paper, an iterative representation learning framework is proposed to predict the saccadic scanpath...
Efficient processing of spectral unmixing is a challenging problem in high-resolution satellite data analysis. The decomposition of a pixel into a linear combination of pure spectra into their corresponding proportions is often very time-consuming. In this paper, a fast unmixing algorithm is proposed based on classifying pixels into a full unmixing group for subset selection requiring intensive computational...
We present a method for developing executable algorithms for quantitative cyber-risk assessment. Exploiting techniques from security risk modeling and actuarial approaches, the method pragmatically combines use of available empirical data and expert judgments. The input to the algorithms are indicators providing information about the target of analysis, such as suspicious events observed in the network...
This paper presents a framework for saliency estimation and fixation prediction in videos. The proposed framework is based on a hierarchical feature representation obtained by stacking convolutional layers of independent subspace analysis (ISA) filters. The feature learning is thus unsupervised and independent of the task. To compute the saliency, we then employ a multiresolution saliency architecture...
Global motion estimation (GME) algorithms are typically employed on aerial videos captured by on-board UAV cameras to compensate for the artificial motion induced in these video frames due to camera motion. However, existing methods for GME have high computational complexity and are therefore not suitable for on-board processing in UAVs with limited computing capabilities. In this paper, we propose...
An online self-estimation algorithm is developed to jointly estimate the states describing the traffic flow dynamics in a signalized junction under different traffic conditions, together with model parameters and their uncertainties. The proposed novel methodology is based on the Expectation-Maximization algorithm and Robbins-Monro stochastic approximation. The algorithm is validated by simulating...
This paper presents a novel two-stage regularized moving-horizon algorithm for PieceWise Affine (PWA) regression. At the first stage, the training samples are processed iteratively, and a Mixed-Integer Quadratic-Programming (MIQP) problem is solved to find the sequence of active modes and the model parameters which best match the training data, within a relatively short time window in the past. According...
Acquisition of parameters for the Bidirectional Scattering Surface Reflectance Distribution Function (BSSRDF) has significant meanings in the study of computer graphics and vision research field. In this paper, we present an inverse rendering approach combined with a newly developed BSSRDF model, directional dipole model, for parameter estimation. To validate our algorithm, we estimate parameters...
Many noninvasive continuous blood pressure measurements using photoplethysmography (PPG) are still inadequate in terms of accuracy and stability, which hinders the practical application of this method. This paper proposes a model based on ensemble method for BP estimation using PPG. A number of blood pressure calculation base-models is built on the same training data. These base-models are used to...
We consider the problem of time-series prediction with missing observations. We consider the autoregressive model (AR model) and cast the problem as a regression problem. On the basic of sampling methods and the online gradient descent (OGD), we propose efficient any-time methods to solve this problem. We show that our algorithm can learn the underlying model efficiently, meanwhile, is robust to the...
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