Image mosaicing is an effective means of constructing a single panoramic image from a series of snapshots taken in different viewing angles. However, in the case of congested traffic scenes with a cluttered environmen...
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ISBN:
(纸本)9784907764302
Image mosaicing is an effective means of constructing a single panoramic image from a series of snapshots taken in different viewing angles. However, in the case of congested traffic scenes with a cluttered environment including vehicles or pedestrians, there are severe difficulties in aligning a pair of snapshots. In such cases, some objects would be taken only in one of the image pair, thereby resulting in failure in stitching the pair of images. This paper deals with three types of techniques for performing an image mosaicing: homography estimation for determining geometrical relationships between the image pair, expectation-maximization algorithm for removing inconsistent overlapping region, and Dijkstrapsilas algorithm to find the boundary for stitching the images together. Experimental results indicate that the proposed technique is effective to synthesize a panoramic image from a series of narrow-field-of-view snapshots.
Modern classification applications necessitate supplementing the few available labeled examples with unlabeled examples to improve classification performance. We present a new tractable algorithm for exploiting unlabe...
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ISBN:
(纸本)0262122413
Modern classification applications necessitate supplementing the few available labeled examples with unlabeled examples to improve classification performance. We present a new tractable algorithm for exploiting unlabeled examples in discriminative classification. This is achieved essentially by expanding the input vectors into longer feature vectors via both labeled and unlabeled examples. The resulting classification method can be interpreted as a discriminative kernel density estimate and is readily trained via the EM algorithm, which in this case is both discriminative and achieves the optimal solution. We provide, in addition, a purely discriminative formulation of the estimation problem by appealing to the maximum entropy framework. We demonstrate that the proposed approach requires very few labeled examples for high classification accuracy.
This paper addresses the problem of unsupervised soft bit error rate (BER) estimation for any communications system, where no prior knowledge either about transmitted information bits, or the transceiver scheme is ava...
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ISBN:
(纸本)9781467318808
This paper addresses the problem of unsupervised soft bit error rate (BER) estimation for any communications system, where no prior knowledge either about transmitted information bits, or the transceiver scheme is available. We show that the problem of BER estimation is equivalent to estimating the conditional probability density functions (pdf)s of soft receiver outputs. Assuming that the receiver has no analytical model of soft observations, we propose a non parametric Kernel-based pdf estimation technique, with Maximum Likelihood based smoothing parameter computation. We then introduce an iterative Stochastic expectation Maximization algorithm for the estimation of both a priori and a posteriori probabilities of transmitted information bits, and the classification of soft observations according to transmitted bit values. These inputs serve in the iterative Kernel-based estimation procedure of conditional pdfs. We analyze the performance of the proposed unsupervised BER estimator in the framework of a multiuser code division multiple access (CDMA) system with single user detection, and show that attractive performance are achieved compared with conventional Monte Carlo-aided techniques.
A hidden Markov model is proposed for the analysis of time-series of daily log-returns of the last 4 years of Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash. These log-returns are assumed to have a multivariate...
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A hidden Markov model is proposed for the analysis of time-series of daily log-returns of the last 4 years of Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash. These log-returns are assumed to have a multivariate Gaussian distribution conditionally on a latent Markov process having a finite number of regimes or states. The hidden regimes represent different market phases identified through distinct vectors of expected values and variance-covariance matrices of the log-returns, so that they also differ in terms of volatility. Maximum-likelihood estimation of the model parameters is carried out by the expectation-maximisation algorithm, and regimes are singularly predicted for every time occasion according to the maximum-a-posteriori rule. Results show three positive and three negative phases of the market. In the most recent period, an increasing tendency towards positive regimes is also predicted. A rather heterogeneous correlation structure is estimated, and evidence of structural medium term trend in the correlation of Bitcoin with the other cryptocurrencies is detected.
While much of the literature on cross-section dependence has focused on estimation of the regression coefficients in the underlying model, estimation and inferences on the magnitude and strength of spillovers and inte...
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While much of the literature on cross-section dependence has focused on estimation of the regression coefficients in the underlying model, estimation and inferences on the magnitude and strength of spillovers and interactions has been largely ignored. At the same time, such inferences are important in many applications, not least because they have structural interpretations and provide useful inferences and structural explanation for the strength of any interactions. In this paper we propose GMM methods designed to uncover underlying (hidden) interactions in social networks and committees. Special attention is paid to the interval censored regression model. Small sample performance is examined through a Monte Carlo study. Our methods are applied to a study of committee decision making within the Bank of England's Monetary Policy Committee.
The objective of this paper is to develop a robust maximum likelihood estimates (MLE) for the stochastic state space model via the expectation maximization (EM) algorithm to cope with observation outliers. Two types o...
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ISBN:
(纸本)9781612844879
The objective of this paper is to develop a robust maximum likelihood estimates (MLE) for the stochastic state space model via the expectation maximization (EM) algorithm to cope with observation outliers. Two types of outliers and their influence have been studied in this sequel namely the additive (AO) and innovative outliers (IO). Due to the sensitivity of the MLE to AO and IO we propose two techniques for robustifying the MLE: the weighted maximum likelihood estimate (WMLE) and the trimmed maximum likelihood estimate (TMLE). The WMLE is easy to implement, however it is still sensitive to IO. On the other hand, the TMLE is a combinatorial optimization problem and hard to implement but it is efficient to all types of outliers presented here. A Monte Carlo simulation result shows the efficiency of the TMLE and WMLE based on the EM algorithm.
We propose a way to evaluate various sound localization systems for moving sounds under the same conditions. To construct a database for moving sounds, we developed a moving sound creation tool using the API library d...
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ISBN:
(纸本)9781424420575
We propose a way to evaluate various sound localization systems for moving sounds under the same conditions. To construct a database for moving sounds, we developed a moving sound creation tool using the API library developed by the ARINIS Company. We developed a two-channel-based sound source localization system integrated with a cross-power spectrum phase (CSP) analysis and EM algorithm. The CSP of sound signals obtained with only two microphones is used to localize the sound source without having to use prior information such as impulse response data. The EM algorithm helps the system cope with several moving sound sources and reduce localization error. We evaluated our sound localization method using artificial moving sounds and confirmed that it can well localize moving sounds slower than 1.125 rad/sec. Finally, we solve the problem of distinguishing whether sounds are coming from the front or back by rotating a robot's head equipped with only two microphones. Our system was applied to a humanoid robot called SIG2, and we confirmed its ability to localize sounds over the entire azimuth range.
In this paper, we propose an extension of a mixture periodic ARCH model (MPARCH) to a mixture periodic GARCH model (MPGARCH), and provide some probabilistic properties of this class of models. An estimation method bas...
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ISBN:
(纸本)9781467358125
In this paper, we propose an extension of a mixture periodic ARCH model (MPARCH) to a mixture periodic GARCH model (MPGARCH), and provide some probabilistic properties of this class of models. An estimation method based on the expectation-Maximization (EM) algorithm is proposed. Finally, it is applied to model the spot rates of the Algerian Dinar against the U.S.-Dollar and Euro. The empirical analysis demonstrates that the proposed mixture model yields the best performance among the competing models.
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