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In this paper we apply state-estimation techniques to a model which describes the time-evolution of observed traffic patterns. We develop a switched linear state-space formulation of a macroscopic traffic flow model and then use Sequential Monte Carlo filtering and regime-based Kaiman Filter (RKF) to reconstruct the underlying traffic patterns, where observations are provided by a microscopic traffic...
In likelihood ratio based decision methods, often a variable number of input evidences is used. A decision based on many such inputs can result in nearly the same likelihood ratio as one based on few inputs. We consider methods for distinguishing between such situations. One of these is to provide confidence intervals together with the decisions and another is to combine the inputs using weights....
The Horn-Schunck (HS) optical flow method is widely employed to initialize many motion estimation algorithms. In this work, a variational Bayesian approach of the HS method is presented where the motion vectors are considered to be spatially varying Student's t-distributed unobserved random variables and the only observation available is the temporal image difference. The proposed model takes into...
This paper addresses issues of online learning and occlusion handling in video object tracking. Although manifold tracking is promising, large pose changes and long-term partial occlusions of video objects remain challenging. We propose a novel manifold tracking scheme that tackles such problems, with the following main novelties: (a) Online estimation of object appearances on Grassmann manifolds;...
In this paper, we propose a novel method for feature selection and model detection using Student's t-distributions based on the variational Bayesian (VB) approach. First, our method is based on the Student's t-mixture model which has heavier tails than the Gaussian distribution and is therefore less sensitive to small numbers of data points and consequent precision-estimates of the components number...
We propose a nonparametric Bayesian, linear Poisson gamma model for count data and use it for dictionary learning. A key property of this model is that it captures the parts-based representation similar to nonnegative matrix factorization. We present an auxiliary variable Gibbs sampler, which turns the intractable inference into a tractable one. Combining this inference procedure with the slice sampler...
Matting and super-resolution of frames from an image sequence have been studied independently in the literature. We propose a unified formulation to solve both inverse problems by assimilating matting within the super-resolution model. We adopt a multi-frame approach which uses data from adjacent frames to increase the resolution of the matte as well as foreground.
In this paper, a unified view of the problem of class-selection with Bayesian classifiers is presented. Selecting a subset of classes instead of singleton allows 1) to reduce the error rate and 2) to propose a reduced set to another classifier or an expert. This second step provides additional information, and therefore increases the quality of the result. The proposed framework, based on the evaluation...
Feature redundancy and loss of local feature are central problems for image classification. Feature selection decreases the feature redundancy by choosing a subset of features and eliminating those with low prediction. The local feature representation is able to highlight objects in an image, thus, overcoming the drawbacks of global features. This paper presents a new method, called the local kernel...
This paper proposed an unsupervised learning method to learn speech features based on Dynamic Bayesian Networks (DBNs) that accounts for the spatiotemporal dependences in speech signal. Although deep networks have been successfully applied to unsupervised learning features, the structures of the deep networks are often fixed before learning and they fail to capture temporal representation. In this...
We propose a nonparametric Bayesian approach to time series alignment. Time series alignment is a technique often required when we analyze a set of time series in which there exists a typical structural pattern common to all the time series. Such a set of time series is typically obtained by repeated measurements of a biological, chemical or physical process. In time series alignment, we are required...
Human faces undergo considerable amount of variations across ages. This paper proposes an age-invariant face verification method by using a Local Classifier Ensemble Model (LCEM). First, reference points are located based on an extended Active Shape Model and faces are aligned afterwards. Second, a face is grouped into several non-overlapping patches and each group is further divided into several...
One of most challenging and important tasks for electricity grid operators and utility companies is to predict and estimate the precise energy consumption and generation of individual households which have their own decentralized production system. This is a under-determined source separation problem since only the difference between energy production and consumption in the micro-generation system...
Bayesian network structure learning is a well-known NP-complete problem, whose solution is of importance in machine learning. Two algorithms are proposed, both of which assess dependency between variables using the chi-squared test of independence between pairs of variables and the log-likelihood evaluation criterion for the network. The first determines the effect of adding a potential edge (in both...
Head pose estimation is critical in many applications such as face recognition and human-computer interaction. Various classifiers such as LDA, SVM, or nearest neighbor are widely used for this purpose; however, the recognition rates are limited due to the limited discriminative power of these classifiers for discretized pose estimation. In this paper, we propose a head pose estimation method using...
The practice always makes us face the challenge of processing pattern recognition data flows with time-varying target concept, i.e., changing statistical relationship between class memberships and observable characteristics of entities to be perceived by the recognition system. In this paper, a mathematical and algorithmic framework is proposed for handling the concept drift in pattern recognition...
Many scalable video compression techniques utilize a mixed-resolution scheme, which down-samples some frames at the encoder to be reduced-resolution frames while keeping resolutions of other frames unchanged as full resolutions, in order to achieve higher compression gain. Image enlargement technique is required at the decoder to recover the original full-resolution frames for this mixed-resolution...
People counting has attracted much attention in video surveillance. This paper proposes an online adaptive learning people counting system across multiple cameras with partial overlapping Fields Of Views (FOVs). The main novelty of this system is that: 1) we propose an online adaptive learning scheme to detect and count people in order to make the system adaptive to various scenes. The system can...
The purpose of this paper is to develop an approach to learn dynamic Bayesian network (DBN) discriminatively for human activity recognition. DBN is a generative model widely used for modeling temporal events in human activity recognition. The parameters of the DBN models are usually learned through maximizing likelihood or expected likelihood. However, activity is often recognized through identifying...
This paper describes an efficient image matting method by combining color and depth information. First, the depth image is segmented by variational level set. Then morphological operators, dilation and erosion, are used to form trimap of ROI (Region of Interest). Finally, with preprocessed depth image, color image and trimap as inputs, an RGB-D Bayesian matting method is proposed to estimate the alpha...
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