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International Conference on Signal Processing and Communication Systems, ICSPCS

作者： Takeaki Iiyoshi Wataru Mitsuhashi Information and Communication Engineering University of Electro-Communications Tokyo Japan

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 graph cuts for seamless stitching and background estimation. Experimental results indicate that the proposed technique is effective to synthesize a panoramic image from a series of narrow-field-of-view snapshots.

关键词： Pixel Minimization methods Nonlinear distortion Automotive engineering Layout Vehicles expectation-maximization algorithms Robustness Computer vision Iterative algorithms

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DWT-CEM: an algorithm for scale-temporal clustering in fMRI

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BIOLOGICAL CYBERNETICS 2007年第1期97卷 33-45页

作者： Sato, Joao Ricardo Fujita, Andre Amaro, Edson, Jr. Miranda, Janaina Mourao Morettin, Pedro Alberto Brammer, Michal John Univ Sao Paulo Inst Math & Stat BR-05508090 Sao Paulo Brazil Univ Sao Paulo Dept Radiobiochem LIM44 NIF BR-05403001 Sao Paulo Brazil Kings Coll London Inst Psychiat Brain Image Anal Unit London SE5 8AF England

The number of studies using functional magnetic resonance imaging (fMRI) has grown very rapidly since the first description of the technique in the early 1990s. Most published studies have utilized data analysis methods based on voxel-wise application of general linear models (GLM). On the other hand, temporal clustering analysis (TCA) focuses on the identification of relationships between cortical areas by measuring temporal common properties. In its most general form, TCA is sensitive to the low signal-to-noise ratio of BOLD and is dependent on subjective choices of filtering parameters. In this paper, we introduce a method for wavelet-based clustering of time-series data and show that it may be useful in data sets with low signal-to-noise ratios, allowing the automatic selection of the optimum number of clusters. We also provide examples of the technique applied to simulated and real fMRI datasets.

关键词： CLUSTER analysis (Statistics) MAGNETIC resonance imaging SIGNAL-to-noise ratio expectation-maximization algorithms GAUSSIAN distribution WAVELETS (Mathematics) LINEAR models (Statistics)

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Estimation of propagation channel parameters using multi-dimensional hybrid-space SAGE algorithm

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IET MICROWAVES ANTENNAS & PROPAGATION 2007年第6期1卷 1212-1222页

作者： Tan, C. M. Beach, M. A. BT Grp Plc Chief Architect Off Kuala Lumpur 50470 Malaysia Univ Bristol Ctr Commun Res Bristol BS8 1UB Avon England

To-date, most multipath propagation parameters are estimated in the element-space domain due to implementation simplicity. Although estimation in the beam-space domain can reduce both the dimensionality of the data size and numerical complexity, it is only beneficial if applied to arrays with a large number of uniformly spaced elements. A new implementation of the space-alternating generalised expectation-maximisation (SAGE) algorithm is presented in joint element-space and beam-space, namely the 'hybrid-space' (HS), such that the underlying features of these dual domains can be simultaneously exploited. The resultant algorithm, namely HS-SAGE algorithm, retains the advantages of the individual domains as the user can specify which parameter should be evaluated in each domain, thus optimising the overall performance. Here, the efficient execution flow of the HS-SAGE algorithm is presented, where the estimation quality is demonstrated through results obtained from both synthetic and real-measurement environments employing circular arrays. It is shown that the proposed algorithm can achieve significant timesaving when compared with the classical implementation approach, whilst maintaining accuracy.

关键词： RADIO wave propagation expectation-maximization algorithms MULTIDIMENSIONAL databases ESTIMATION theory ESTIMATION bias CONSTANTS of integration RADIO waves algorithms COMPUTATIONAL complexity

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Penalized probabilistic clustering

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NEURAL COMPUTATION 2007年第6期19卷 1528-1567页

作者： Lu, Zhengdong Leen, Todd K. Oregan Hlth & Sci Inst Dept Comp Sci & Engn OGI Sch Sci & Engn Beaverton OR 97006 USA

While clustering is usually an unsupervised operation, there are circumstances in which we believe (with varying degrees of certainty) that items A and B should be assigned to the same cluster, while items A and C should not. We would like such pairwise relations to influence cluster assignments of out-of-sample data in a manner consistent with the prior knowledge expressed in the training set. Our starting point is probabilistic clustering based on gaussian mixture models (GMM) of the data distribution. We express clustering preferences in a prior distribution over assignments of data points to clusters. This prior penalizes cluster assignments according to the degree with which they violate the preferences. The model parameters are fit with the expectation-maximization (EM) algorithm. Our model provides a flexible framework that encompasses several other semisupervised clustering models as its special cases. Experiments on artificial and real-world problems show that our model can consistently improve clustering results when pairwise relations are incorporated. The experiments also demonstrate the superiority of our model to other semisupervised clustering methods on handling noisy pairwise relations.

关键词： expectation-maximization algorithms algorithms PROBABILISTIC number theory MATHEMATICAL models DATA analysis

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State-space models: From the EM algorithm to a gradient approach

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NEURAL COMPUTATION 2007年第4期19卷 1097-1111页

作者： Olsson, Rasmus Kongsgaard Petersen, Kaare Brandt Lehn-Schioler, Tue Tech Univ Denmark DK-2800 Lyngby Denmark

Slow convergence is observed in the EM algorithm for linear state-space models. We propose to circumvent the problem by applying any off-the-shelf quasi-Newton-type optimizer, which operates on the gradient of the log-likelihood function. Such an algorithm is a practical alternative due to the fact that the exact gradient of the log-likelihood function can be computed by recycling components of the expectation-maximization (EM) algorithm. We demonstrate the efficiency of the proposed method in three relevant instances of the linear state-space model. In high signal-to-noise ratios, where EM is particularly prone to converge slowly, we show that gradient-based learning results in a sizable reduction of computation time.

关键词： expectation-maximization algorithms CONVERGENCE STATE-space methods algorithms CONJUGATE gradient methods ALGEBRA

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Learning the lie groups of visual invariance

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NEURAL COMPUTATION 2007年第10期19卷 2665-2693页

作者： Miao, Xu Rao, Rajesh P. N. Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA

A fundamental problem in biological and machine vision is visual invariance: How are objects perceived to be the same despite transformations such as translations, rotations, and scaling? In this letter, we describe a new, unsupervised approach to learning invariances based on Lie group theory. Unlike traditional approaches that sacrifice information about transformations to achieve invariance, the Lie group approach explicitly models the effects of transformations in images. As a result, estimates of transformations are available for other purposes, such as pose estimation and visuomotor control. Previous approaches based on first-order Taylor series expansions of images can be regarded as special cases of the Lie group approach, which utilizes a matrix-exponential-based generative model of images and can handle arbitrarily large transformations. We present an unsupervised expectation-maximization algorithm for learning Lie transformation operators directly from image data containing examples of transformations. Our experimental results show that the Lie operators learned by the algorithm from an artificial data set containing six types of affine transformations closely match the analytically predicted affine operators. We then demonstrate that the algorithm can also recover novel transformation operators from natural image sequences. We conclude by showing that the learned operators can be used to both generate and estimate transformations in images, thereby providing a basis for achieving visual invariance.

关键词： COMPUTER vision PATTERN recognition systems ARTIFICIAL intelligence IMAGE processing expectation-maximization algorithms LIE algebras LIE groups

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On the strong non-rigidity of certain tight Euclidean designs

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EUROPEAN JOURNAL OF COMBINATORICS 2007年第6期28卷 1662-1680页

作者： Bannai, Eiichi Bannai, Etsuko Suprijanto, Djoko Kyushu Univ Grad Sch Math Higashi Ku Fukuoka 8128581 Japan Bandung Inst Technol Dept Math Bandung 40133 Indonesia

We study the non-rigidity of Euclidean t-designs, namely we study when Euclidean designs (in particular certain tight Euclidean designs) can be deformed keeping the property of being Euclidean t-designs. We show that certain tight Euclidean t-designs are non-rigid, and in fact satisfy a stronger form of non-rigidity which we call strong non-rigidity. This shows that there are plenty of non-isomorphic tight Euclidean t-designs for certain parameters, which seems to have been unnoticed before. We also include the complete classification of tight Euclidean 2-designs. (C) 2007 Published by Elsevier Ltd

关键词： EUCLIDEAN algorithm algorithms NUMBER theory expectation-maximization algorithms

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Robust Statistics Based expectation-maximization Algorithm for Channel Tracking in OFDM Systems

Robust Statistics Based Expectation-Maximization Algorithm f...

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IEEE International Conference on Communications (ICC)

作者： S. Kalyani K. Giridhar Telecommunication and Computer Networks (TeNeT) Group Department of Electrical Engineering Indian Institute of Technology Madras Chennai India

Decision directed channel tracking (DDCT) at high fade rates in OFDM based systems is addressed in this paper. Existing DDCT algorithms like the expectation-maximization (EM) algorithm (Al-Naffouri et al., 2002) suffer from error propagation and exhibit poor performance when applied to large frames at high fade rates. We propose a robust EM algorithm which mitigates the effect of error propagation and is able to track the channel in the decision directed mode even over frame durations experiencing 2-3 fade cycles. This EM algorithm uses the Huber's cost function in the maximization step instead of the non-robust least squares or Kalman cost function. Further, the noise variance is estimated using the robust median absolute deviation estimator instead of the standard maximum likelihood estimator. The proposed robust EM based DDCT scheme has a better error rate and MSE performance when compared to Kalman filter based pilot assisted channel tracking scheme with a 6.25% pilot overhead, even at a normalized Doppler of 0.04.

关键词： Statistics expectation-maximization algorithms OFDM Noise robustness Maximum likelihood estimation Working environment noise Cost function Channel estimation Parameter estimation Communications Society

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Level Set Method for Positron Emission Tomography

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INTERNATIONAL JOURNAL OF BIOMEDICAL IMAGING 2007年第unknown期2007卷 1-15页

作者： Chan, Tony F. Li, Hongwei Lysaker, Marius Tai, Xue-Cheng Univ Calif Los Angeles Dept Math 405 Hilgard Ave Los Angeles CA 90095 USA Univ Bergen Ctr Integrated Petr Res N-5007 Bergen Norway Simula Res Lab AS Dept Sci Comp N-1325 Lysaker Norway Henan Univ Dept Math & Syst Sci Kaifeng 475001 Peoples R China Univ Bergen Dept Math N-5009 Bergen Norway

In positron emission tomography (PET), a radioactive compound is injected into the body to promote a tissue-dependent emission rate. expectation maximization (EM) reconstruction algorithms are iterative techniques which estimate the concentration coefficients that provide the best fitted solution, for example, a maximum likelihood estimate. In this paper, we combine the EM algorithm with a level set approach. The level set method is used to capture the coarse scale information and the discontinuities of the concentration coefficients. An intrinsic advantage of the level set formulation is that anatomical information can be efficiently incorporated and used in an easy and natural way. We utilize a multiple level set formulation to represent the geometry of the objects in the scene. The proposed algorithm can be applied to any PET configuration, without major modifications. Copyright (C) 2007 Tony F. Chan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

关键词： EMISSION tomography LEVEL set methods expectation-maximization algorithms MEDICINE -- Mathematics DIAGNOSTIC imaging BIOMEDICAL engineering -- Research

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Robust Face Matching Under Large Occlusions

Robust Face Matching Under Large Occlusions

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International Conference on Image Analysis and Processing

作者： Georgy Gimel'farb Patrice Delmas John Morris Al Shorin Department of Computer Science University of Auckland Auckland New Zealand

Outliers due to occlusions and contrast and offset signal deviations notably hinder recognition and retrieval of facial images. We propose a new maximum likelihood matching score with "soft masking" of outliers which is robust in these conditions. Differences between two images are modelled by unknown contrast and offset deviations from an unknown template and by independent pixel-wise errors. The error distribution is a mixture of a zero-centred Gaussian noise with an unknown variance and uniformly distributed outliers. The matching score combines the maximum likelihood estimates of model parameters and the soft masks being produced by a simple iterative expectation-maximisation algorithm. Experiments with facial images from the MIT face database show the robustness of this technique in the presence of large occlusions.

关键词： Maximum likelihood estimation Noise robustness Face recognition Image recognition Image retrieval Pixel Gaussian noise Parameter estimation expectation-maximization algorithms Image databases

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