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902
Matching 3D Models with Shape Distributions
"... Measuring the similarity between 3D shapes is a fundamental problem, with applications in computer vision, molecular biology, computer graphics, and a variety of other fields. A challenging aspect of this problem is to find a suitable shape signature that can be constructed and compared quickly, whi ..."
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Cited by 172 (7 self)
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Measuring the similarity between 3D shapes is a fundamental problem, with applications in computer vision, molecular biology, computer graphics, and a variety of other fields. A challenging aspect of this problem is to find a suitable shape signature that can be constructed and compared quickly, while still discriminating between similar and dissimilar shapes. In this paper, we propose and analyze a method for computing shape signatures for arbitrary (possibly degenerate) 3D polygonal models. The key idea is to represent the signature of an object as a shape distribution sampled from a shape function measuring global geometric properties of an object. The primary motivation for this approach is to reduce the shape matching problem to the comparison of probability distributions, which is a simpler problem than the comparison of 3D surfaces by traditional shape matching methods that require pose registration, feature correspondence, or model fitting. We find that the dissimilarities be...
Prior Probabilities
 IEEE Transactions on Systems Science and Cybernetics
, 1968
"... e case of location and scale parameters, rate constants, and in Bernoulli trials with unknown probability of success. In realistic problems, both the transformation group analysis and the principle of maximum entropy are needed to determine the prior. The distributions thus found are uniquely determ ..."
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Cited by 166 (3 self)
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e case of location and scale parameters, rate constants, and in Bernoulli trials with unknown probability of success. In realistic problems, both the transformation group analysis and the principle of maximum entropy are needed to determine the prior. The distributions thus found are uniquely determined by the prior information, independently of the choice of parameters. In a certain class of problems, therefore, the prior distributions may now be claimed to be fully as "objective" as the sampling distributions. I. Background of the problem Since the time of Laplace, applications of probability theory have been hampered by difficulties in the treatment of prior information. In realistic problems of decision or inference, we often have prior information which is highly relevant to the question being asked; to fail to take it into account is to commit the most obvious inconsistency of reasoning and may lead to absurd or dangerously misleading results. As an extreme examp
Speaker recognition: A tutorial
"... A tutorial on the design and development of automatic speakerrecognition systems is presented. Automatic speaker recognition is the use of a machine to recognize a person from a spoken phrase. These systems can operate in two modes: to identify a particular person or to verify a person’s claimed id ..."
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Cited by 160 (2 self)
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A tutorial on the design and development of automatic speakerrecognition systems is presented. Automatic speaker recognition is the use of a machine to recognize a person from a spoken phrase. These systems can operate in two modes: to identify a particular person or to verify a person’s claimed identity. Speech processing and the basic components of automatic speakerrecognition systems are shown and design tradeoffs are discussed. Then, a new automatic speakerrecognition system is given. This recognizer performs with 98.9 % correct identification. Last, the performances of various systems are compared.
A Maximum Entropy Model for Prepositional Phrase Attachment
 In Proceedings of the ARPA Workshop on Human Language Technology
, 1994
"... this paper methods for constructing statistical models for computing the probability of attachment decisions. These models could be then integrated into scoring the probability of an overall parse. We present our methods in the context of prepositional phrase (PP) attachment. ..."
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Cited by 128 (3 self)
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this paper methods for constructing statistical models for computing the probability of attachment decisions. These models could be then integrated into scoring the probability of an overall parse. We present our methods in the context of prepositional phrase (PP) attachment.
Informationtheoretic asymptotics of Bayes methods
 IEEE Transactions on Information Theory
, 1990
"... AbstractIn the absence of knowledge of the true density function, Bayesian models take the joint density function for a sequence of n random variables to be an average of densities with respect to a prior. We examine the relative entropy distance D,, between the true density and the Bayesian densit ..."
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Cited by 107 (10 self)
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AbstractIn the absence of knowledge of the true density function, Bayesian models take the joint density function for a sequence of n random variables to be an average of densities with respect to a prior. We examine the relative entropy distance D,, between the true density and the Bayesian density and show that the asymptotic distance is (d/2Xlogn)+ c, where d is the dimension of the parameter vector. Therefore, the relative entropy rate D,,/n converges to zero at rate (logn)/n. The constant c, which we explicitly identify, depends only on the prior density function and the Fisher information matrix evaluated at the true parameter value. Consequences are given for density estimation, universal data compression, composite hypothesis testing, and stockmarket portfolio selection. 1.
Within the Twilight Zone: A Sensitive ProfileProfile Comparison Tool Based on Information Theory
 J. Mol. Biol
, 2002
"... This paper presents a novel approach to proleprole comparison. The method compares two input proles (like those that are generated by PSIBLAST) and assigns a similarity score to assess their statistical similarity. Our proleprole comparison tool, which allows for gaps, can be used to detect weak ..."
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Cited by 99 (4 self)
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This paper presents a novel approach to proleprole comparison. The method compares two input proles (like those that are generated by PSIBLAST) and assigns a similarity score to assess their statistical similarity. Our proleprole comparison tool, which allows for gaps, can be used to detect weak similarities between protein families. It has also been optimized to produce alignments that are in very good agreement with structural alignments. Tests show that the proleprole alignments are indeed highly correlated with similarities between secondary structure elements and tertiary structure. Exhaustive evaluations show that our method is signicantly more sensitive in detecting distant homologies than the popular prolebased search programs PSIBLAST and IMPALA. The relative improvement is the same order of magnitude as the improvement of PSIBLAST relative to BLAST. Our new tool often detects similarities that fall within the twilight zone of sequence similarity
Face Recognition From LongTerm Observations
 In Proc. IEEE European Conference on Computer Vision
, 2002
"... We address the problem of face recognition from a large set of images obtained over time  a task arising in many surveillance and authentication applications. A set or a sequence of images provides information about the variability in the appearance of the face which can be used for more robust rec ..."
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Cited by 92 (2 self)
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We address the problem of face recognition from a large set of images obtained over time  a task arising in many surveillance and authentication applications. A set or a sequence of images provides information about the variability in the appearance of the face which can be used for more robust recognition. We discuss di#erent approaches to the use of this information, and show that when cast as a statistical hypothesis testing problem, the classification task leads naturally to an informationtheoretic algorithm that classifies sets of images using the relative entropy (KullbackLeibler divergence) between the estimated density of the input set and that of stored collections of images for each class. We demonstrate the performance of the proposed algorithm on two mediumsized data sets of approximately frontal face images, and describe an application of the method as part of a viewindependent recognition system.
Similaritybased models of word cooccurrence probabilities
 Machine Learning
, 1999
"... Abstract. In many applications of natural language processing (NLP) it is necessary to determine the likelihood of a given word combination. For example, a speech recognizer may need to determine which of the two word combinations “eat a peach ” and “eat a beach ” is more likely. Statistical NLP met ..."
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Cited by 90 (0 self)
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Abstract. In many applications of natural language processing (NLP) it is necessary to determine the likelihood of a given word combination. For example, a speech recognizer may need to determine which of the two word combinations “eat a peach ” and “eat a beach ” is more likely. Statistical NLP methods determine the likelihood of a word combination from its frequency in a training corpus. However, the nature of language is such that many word combinations are infrequent and do not occur in any given corpus. In this work we propose a method for estimating the probability of such previously unseen word combinations using available information on “most similar ” words. We describe probabilistic word association models based on distributional word similarity, and apply them to two tasks, language modeling and pseudoword disambiguation. In the language modeling task, a similaritybased model is used to improve probability estimates for unseen bigrams in a backoff language model. The similaritybased method yields a 20 % perplexity improvement in the prediction of unseen bigrams and statistically significant reductions in speechrecognition error. We also compare four similaritybased estimation methods against backoff and maximumlikelihood estimation methods on a pseudoword sense disambiguation task in which we controlled for both unigram and bigram frequency to avoid giving too much weight to easytodisambiguate highfrequency configurations. The similaritybased methods perform up to 40 % better on this particular task.