Results 1  10
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410
CONDENSATION  conditional density propagation for visual tracking
 International Journal of Computer Vision
, 1998
"... The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses "factored sampling", previously applied to the ..."
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Cited by 1124 (12 self)
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The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses "factored sampling", previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together with visual observations, to propagate the random set over time. The result is highly robust tracking of agile motion. Notwithstanding the use of stochastic methods, the algorithm runs in near realtime. Contents 1 Tracking curves in clutter 2 2 Discretetime propagation of state density 3 3 Factored sampling 6 4 The Condensation algorithm 8 5 Stochastic dynamical models for curve motion 10 6 Observation model 13 7 Applying the Condensation algorithm to videostreams 17 8 Conclusions 26 A Nonline...
Efficient and Effective Querying by Image Content
 Journal of Intelligent Information Systems
, 1994
"... In the QBIC (Query By Image Content) project we are studying methods to query large online image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, and shape of image objects and regions. Potential applications include medical ..."
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Cited by 429 (12 self)
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In the QBIC (Query By Image Content) project we are studying methods to query large online image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, and shape of image objects and regions. Potential applications include medical ("Give me other images that contain a tumor with a texture like this one"), photojournalism ("Give me images that have blue at the top and red at the bottom"), and many others in art, fashion, cataloging, retailing, and industry. We describe a set of novel features and similarity measures allowing query by color, texture, and shape of image object. We demonstrate the effectiveness of the QBIC system with normalized precision and recall experiments on test databases containing over 1000 images and 1000 objects populated from commercially available photo clip art images, and of images of airplane silhouettes. We also consider the efficient indexing of these features, specifically addre...
Optimal motion and structure estimation
 IEEE Trans. Pattern Anal. Mach. Intell
, 1993
"... This paper studies optimal estimation for motion and structure from point correspondences. (1) A study of the characteristics of thc problem provides insight into the need for optimal estimation. (2) Methods have been developed for optimal estimation with known or unknown noise distribution. The sim ..."
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Cited by 131 (5 self)
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This paper studies optimal estimation for motion and structure from point correspondences. (1) A study of the characteristics of thc problem provides insight into the need for optimal estimation. (2) Methods have been developed for optimal estimation with known or unknown noise distribution. The simulations showed that the optimal estimations achieve remarkable improvement over the preliminary estimates given by the linear algorithm. (3) An approach to estimating errors in the optimized solution is presented. (4) The performance of the algorithm is compared with a theoretical lower bound CramCrRao bound. Simulations show that the actual errors have essentially reached the bound. (5) A batch leastsquares technique (LevenbergMarquardt) and a sequential leastsquares technique (iterated extended Kalman filtering) are analyzed and compared. The analysis and experiments show that, in general, a batch technique will perform better than a sequential technique for any nonlinear problems. Recursive batch processing technique is proposed for nonlinear problems that require recursive estimation. 1.
Recent advances in the automatic recognition of audiovisual speech
 PROC. IEEE
, 2003
"... Visual speech information from the speaker’s mouth region has been successfully shown to improve noise robustness of automatic speech recognizers, thus promising to extend their usability in the human computer interface. In this paper, we review the main components of audiovisual automatic speech r ..."
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Cited by 110 (14 self)
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Visual speech information from the speaker’s mouth region has been successfully shown to improve noise robustness of automatic speech recognizers, thus promising to extend their usability in the human computer interface. In this paper, we review the main components of audiovisual automatic speech recognition and present novel contributions in two main areas: First, the visual front end design, based on a cascade of linear image transforms of an appropriate video regionofinterest, and subsequently, audiovisual speech integration. On the latter topic, we discuss new work on feature and decision fusion combination, the modeling of audiovisual speech asynchrony, and incorporating modality reliability estimates to the bimodal recognition process. We also briefly touch upon the issue of audiovisual adaptation. We apply our algorithms to three multisubject bimodal databases, ranging from small to largevocabulary recognition tasks, recorded in both visually controlled and challenging environments. Our experiments demonstrate that the visual modality improves automatic speech recognition over all conditions and data considered, though less so for visually challenging environments and large vocabulary tasks.
Euclidean Reconstruction from Image Sequences with Varying and Unknown Focal Length and Principal Point
"... In this paper the special case of reconstruction from image sequences taken by cameras with skew equal to 0 and aspect ratio equal to 1 has been treated. These type of cameras, here called cameras with Euclidean image planes, represent rigid projections where neither the principal point nor the foca ..."
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Cited by 105 (9 self)
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In this paper the special case of reconstruction from image sequences taken by cameras with skew equal to 0 and aspect ratio equal to 1 has been treated. These type of cameras, here called cameras with Euclidean image planes, represent rigid projections where neither the principal point nor the focal length is known. It will be shown that it is possible to reconstruct an unknown object from images taken by a camera with Euclidean image plane up to similarity transformations, i.e., Euclidean transformations plus changes in the global scale. An algorithm, using bundle adjustment techniques, has been implemented. The performance of the algorithm is shown on simulated data.
Information Geometry of the EM and em Algorithms for Neural Networks
 Neural Networks
, 1995
"... In order to realize an inputoutput relation given by noisecontaminated examples, it is effective to use a stochastic model of neural networks. A model network includes hidden units whose activation values are not specified nor observed. It is useful to estimate the hidden variables from the obs ..."
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Cited by 101 (8 self)
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In order to realize an inputoutput relation given by noisecontaminated examples, it is effective to use a stochastic model of neural networks. A model network includes hidden units whose activation values are not specified nor observed. It is useful to estimate the hidden variables from the observed or specified inputoutput data based on the stochastic model. Two algorithms, the EM  and emalgorithms, have so far been proposed for this purpose. The EMalgorithm is an iterative statistical technique of using the conditional expectation, and the emalgorithm is a geometrical one given by information geometry. The emalgorithm minimizes iteratively the KullbackLeibler divergence in the manifold of neural networks. These two algorithms are equivalent in most cases. The present paper gives a unified information geometrical framework for studying stochastic models of neural networks, by forcussing on the EM and em algorithms, and proves a condition which guarantees their equ...
The Science of Breeding and its Application to the Breeder Genetic Algorithm BGA
 EVOLUTIONARY COMPUTATION
, 1994
"... The Breeder Genetic Algorithm BGA models artificial selection as performed by human breeders. The science of breeding is based on advanced statistical methods. In this paper a connection between genetic algorithm theory and the science of breeding is made. We show how the response to selection eq ..."
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Cited by 100 (23 self)
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The Breeder Genetic Algorithm BGA models artificial selection as performed by human breeders. The science of breeding is based on advanced statistical methods. In this paper a connection between genetic algorithm theory and the science of breeding is made. We show how the response to selection equation and the concept of heritability can be applied to predict the behavior of the BGA. Selection, recombination and mutation are analyzed within this framework. It is shown that recombination and mutation are complementary search operators. The theoretical results are obtained under the assumption of additive gene effects. For general fitness landscapes regression techniques for estimating the heritability are used to analyze and control the BGA. The method of decomposing the genetic variance into an additive and a nonadditive part connects the case of additive fitness functions with the general case.
Unsupervised feature selection using feature similarity
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2002
"... AbstractÐIn this article, we describe an unsupervised feature selection algorithm suitable for data sets, large in both dimension and size. The method is based on measuring similarity between features whereby redundancy therein is removed. This does not need any search and, therefore, is fast. A new ..."
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Cited by 98 (2 self)
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AbstractÐIn this article, we describe an unsupervised feature selection algorithm suitable for data sets, large in both dimension and size. The method is based on measuring similarity between features whereby redundancy therein is removed. This does not need any search and, therefore, is fast. A new feature similarity measure, called maximum information compression index, is introduced. The algorithm is generic in nature and has the capability of multiscale representation of data sets. The superiority of the algorithm, in terms of speed and performance, is established extensively over various reallife data sets of different sizes and dimensions. It is also demonstrated how redundancy and information loss in feature selection can be quantified with an entropy measure. Index TermsÐData mining, pattern recognition, dimensionality reduction, feature clustering, multiscale representation, entropy measures. 1
Convergence of a stochastic approximation version of the EM algorithm
, 1997
"... The Expectation Maximization (EM) algorithm is a powerful computational technique for locating maxima of functions... ..."
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Cited by 86 (8 self)
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The Expectation Maximization (EM) algorithm is a powerful computational technique for locating maxima of functions...
Mean and Variance of Implicitly Defined Biased Estimators (such as Penalized Maximum Likelihood): Applications to Tomography
 IEEE Tr. Im. Proc
, 1996
"... Many estimators in signal processing problems are defined implicitly as the maximum of some objective function. Examples of implicitly defined estimators include maximum likelihood, penalized likelihood, maximum a posteriori, and nonlinear leastsquares estimation. For such estimators, exact analyti ..."
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Cited by 84 (30 self)
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Many estimators in signal processing problems are defined implicitly as the maximum of some objective function. Examples of implicitly defined estimators include maximum likelihood, penalized likelihood, maximum a posteriori, and nonlinear leastsquares estimation. For such estimators, exact analytical expressions for the mean and variance are usually unavailable. Therefore investigators usually resort to numerical simulations to examine properties of the mean and variance of such estimators. This paper describes approximate expressions for the mean and variance of implicitly defined estimators of unconstrained continuous parameters. We derive the approximations using the implicit function theorem, the Taylor expansion, and the chain rule. The expressions are defined solely in terms of the partial derivatives of whatever objective function one uses for estimation. As illustrations, we demonstrate that the approximations work well in two tomographic imaging applications with Poisson sta...