In this paper, we present a statistical method to extract images of passenger cars from highway traffic scenes. The expectation-maximization (EM) algorithm is used to classify the vehicles in the images as either bein...
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In this paper, we present a statistical method to extract images of passenger cars from highway traffic scenes. The expectation-maximization (EM) algorithm is used to classify the vehicles in the images as either being passenger cars or some other bigger vehicles, cars versus non-cars. The vehicle classification algorithm uses training sets of 100-frame video sequences. The car group is comprised of passenger cars and light trucks. The non-car group is comprised of heavy single trucks as well as 3-axle and more combination trucks. We use the properties of their dimensional distribution and the probability of their appearance to identify the vehicle group. We present a validation of our algorithm using real-world traffic scenes.
We present a novel region-based image fusion method using a rigorous application of estimation theory. This method takes advantage of the similar intensity or texture in a region for fusion. A statistical image format...
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We present a novel region-based image fusion method using a rigorous application of estimation theory. This method takes advantage of the similar intensity or texture in a region for fusion. A statistical image formation model using Gaussian mixture distortion is built for each region and the EM (expectation-maximization) algorithm is used in conjunction with the model to develop the region-level EM fusion algorithm to produce the fused image. Since in most applications of image fusion, objects carry the information of interest and regions can be used to represent objects, the region-based fusion approaches could be more meaningful than pixel-based methods. Our experiments demonstrate the efficiency of the proposed region-base fusion method and the advantages in dealing with region interface artifacts for concealed weapon detection and night vision applications.
This paper addresses the problem of multi-pitch estimation, which consists in estimating the fundamental frequencies of multiple harmonic sources, with possibly overlapping partials, from their mixture. The proposed a...
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This paper addresses the problem of multi-pitch estimation, which consists in estimating the fundamental frequencies of multiple harmonic sources, with possibly overlapping partials, from their mixture. The proposed approach is based on the expectation-maximization algorithm, which aims at maximizing the likelihood of the observed spectrum, by performing successive single-pitch and spectral envelope estimations. This algorithm is illustrated in the context of musical chord identification.
This paper investigates the application of the expectation-maximization algorithm for systems with multiple transmit and/or receive antennas in presence of fast fading channels.
This paper investigates the application of the expectation-maximization algorithm for systems with multiple transmit and/or receive antennas in presence of fast fading channels.
This paper surveys a short study about using and applying symbolic processing to direct execution of the expectation-maximization algorithm. Formulas are derived in the manner defined by the expectation-maximization a...
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This paper surveys a short study about using and applying symbolic processing to direct execution of the expectation-maximization algorithm. Formulas are derived in the manner defined by the expectation-maximization algorithm and how there are applied. Symbolic processing uses the set of equations on the same way that describe expectationmaximization algorithm without any adaptation and loops of computation as usually done. The methodology of processing is described step-by-step for direct execution of expectation-maximization algorithm. Some of the advantages of symbolic processing are described in regard to numerical processing. Finally, numerical data are applied on the complete model and results are displayed.
In this paper an expectation-maximization (EM) approach assisted by Dempster-Shafer evidence theory (DST) for image segmentation is presented. The images obtained by a synthetic aperture sonar (SAS) are segmented into...
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ISBN:
(纸本)9781467315906
In this paper an expectation-maximization (EM) approach assisted by Dempster-Shafer evidence theory (DST) for image segmentation is presented. The images obtained by a synthetic aperture sonar (SAS) are segmented into highlight, background and shadow regions for the purpose of shape feature extraction, which requires highly correct and precise segmentation results. The EM method of Sanjay-Gopal et al. is improved by using the gamma mixture model. Moreover, an intermediate step (I-step) based on DST is introduced between the E- and M-steps of the EM to consider the spatial dependency among pixels. Two combination rules of DST are adopted and compared, i.e. the Dempster rule and the cautious rule. Finally, numerical tests are carried out on both synthetic images and SAS images. The results are compared to those methods from the literature. Our approach provides segmentations with less false alarms and better shape preservation.
A maximum likelihood (ML) estimate of the magnitude of a complex-valued image from measurements of its Fourier transform in a limited region offset from the origin is derived under the assumption of independent, unifo...
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A maximum likelihood (ML) estimate of the magnitude of a complex-valued image from measurements of its Fourier transform in a limited region offset from the origin is derived under the assumption of independent, uniformly distributed image phase samples. The expectation-maximization (EM) algorithm is employed to solve the resulting nonlinear maximum likelihood equation, and it yields a computationally efficient iterative estimator. Reconstructions from this algorithm contain significantly more energy than conventional reconstructions, but with only slightly improved reconstruction quality.< >
Gaussian mixture models are a very useful tool for modeling data distribution. While estimating parameters using the expectation-maximization algorithm, this approach does not scale well with big datasets, especially ...
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ISBN:
(纸本)9781665480468
Gaussian mixture models are a very useful tool for modeling data distribution. While estimating parameters using the expectation-maximization algorithm, this approach does not scale well with big datasets, especially if it is necessary to prepare many models for the proper selection of metaparameters. In this article we present an approximation of the expectation-maximization algorithm obtained by merging crucial subsets of the dataset, that differ slightly in their effect on the expectation-maximization loss function, into information granules. Furthermore, application examples comparing new method with the classical approach are shown.
This paper places turbo synchronization into the sum-product (SP) and the expectation-maximization (EM) algorithm framework. In particular, we show that the combination of these algorithms enables to design low-comple...
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This paper places turbo synchronization into the sum-product (SP) and the expectation-maximization (EM) algorithm framework. In particular, we show that the combination of these algorithms enables to design low-complexity and very powerful synchronisers. The proposed synchronizer is compared to a previously-proposed EM framework. As the derivation suggests, our new iterative scheme clearly outperforms the classical use of the EM algorithm.
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