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9,662
Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models
, 1995
"... ..."
Contour Tracking By Stochastic Propagation of Conditional Density
, 1996
"... . In Proc. European Conf. Computer Vision, 1996, pp. 343--356, Cambridge, UK The problem of tracking curves in dense visual clutter is a challenging one. Trackers based on Kalman filters are of limited use; because they are based on Gaussian densities which are unimodal, they cannot represent s ..."
Abstract
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Cited by 661 (23 self)
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. In Proc. European Conf. Computer Vision, 1996, pp. 343--356, Cambridge, UK The problem of tracking curves in dense visual clutter is a challenging one. Trackers based on Kalman filters are of limited use; because they are based on Gaussian densities which are unimodal, they cannot represent
The Capacity of Low-Density Parity-Check Codes Under Message-Passing Decoding
, 2001
"... In this paper, we present a general method for determining the capacity of low-density parity-check (LDPC) codes under message-passing decoding when used over any binary-input memoryless channel with discrete or continuous output alphabets. Transmitting at rates below this capacity, a randomly chos ..."
Abstract
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Cited by 574 (9 self)
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In this paper, we present a general method for determining the capacity of low-density parity-check (LDPC) codes under message-passing decoding when used over any binary-input memoryless channel with discrete or continuous output alphabets. Transmitting at rates below this capacity, a randomly
Performance of optical flow techniques
- INTERNATIONAL JOURNAL OF COMPUTER VISION
, 1994
"... While different optical flow techniques continue to appear, there has been a lack of quantitative evaluation of existing methods. For a common set of real and synthetic image sequences, we report the results of a number of regularly cited optical flow techniques, including instances of differential, ..."
Abstract
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Cited by 1325 (32 self)
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While different optical flow techniques continue to appear, there has been a lack of quantitative evaluation of existing methods. For a common set of real and synthetic image sequences, we report the results of a number of regularly cited optical flow techniques, including instances of differential
The Vector Field Histogram -- Fast Obstacle Avoidance For Mobile Robots
- IEEE JOURNAL OF ROBOTICS AND AUTOMATION
, 1991
"... A new real-time obstacle avoidance method for mobile robots has been developed and implemented. This method, named the vector field histogram(VFH), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward the target. The VFH method uses a ..."
Abstract
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Cited by 484 (24 self)
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suitable sector from among all polar histogram sectors with a low polar obstacle density, and the steering of the robot is aligned with that direction. Experimental results from a mobile robot traversing densely cluttered obstacle courses in smooth and continuous motion and at an average speed of 0.6 0.7m
MAP Estimation of Continuous Density HMM: Theory and Applications
- In: Proceedings of DARPA Speech and Natural Language Workshop
, 1992
"... We discuss maximum a posteriori estimation of continuous density hidden Markovmodels(CDHMM).The classical MLE reestimation algorithms, namely the forward-backward algorithm and the segmental k-means algorithm, are expanded and reestimation formulas are given for HMM with Gaussian mixture observation ..."
Abstract
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Cited by 31 (6 self)
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We discuss maximum a posteriori estimation of continuous density hidden Markovmodels(CDHMM).The classical MLE reestimation algorithms, namely the forward-backward algorithm and the segmental k-means algorithm, are expanded and reestimation formulas are given for HMM with Gaussian mixture
Continuous Density Queries for Moving Objects
"... Monitoring dense areas, where the density of moving ob-jects is higher than the given threshold, has many applica-tions like traffic control, bandwidth management, and col-lision probability evaluation. Although many studies have been done on density queries for moving objects in highly dynamic scen ..."
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scenarios, they all focused on how to answer snap-shot density queries. In this paper, we focus on continuously monitoring dense regions for moving objects. Based on the notion of safe interval, we propose effective algorithms to evaluate and keep track of dense regions. Experimental re-sults show that our
Central Gaussian semigroups of measures with continuous density
- J. Funct. Anal
, 1999
"... This paper investigates the existence and properties of symmetric central Gaussian semigroups ( t ) t>0 which are absolutely continuous and have a continuous density x 7! t (x), t > 0, with respect to Haar measure on groups of the form R n K where K is compact connected locally connecte ..."
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Cited by 10 (8 self)
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This paper investigates the existence and properties of symmetric central Gaussian semigroups ( t ) t>0 which are absolutely continuous and have a continuous density x 7! t (x), t > 0, with respect to Haar measure on groups of the form R n K where K is compact connected locally
MAP Estimation of Continuous Density HMM: Theory and Applications
"... We discuss maximum a posteriori estimation of continuous density hidden Markov models (CDHMM). The classical MLE reestimation algorithms, namely the forward-backward algorithm and the segmental k-means algorithm, are expanded and reestimation formulas are given for HMM with Gaussian mixture observat ..."
Abstract
- Add to MetaCart
We discuss maximum a posteriori estimation of continuous density hidden Markov models (CDHMM). The classical MLE reestimation algorithms, namely the forward-backward algorithm and the segmental k-means algorithm, are expanded and reestimation formulas are given for HMM with Gaussian mixture
Parameterisation of a Stochastic Model for Human Face Identification
, 1994
"... Recent work on face identification using continuous density Hidden Markov Models (HMMs) has shown that stochastic modelling can be used successfully to encode feature information. When frontal images of faces are sampled using top-bottom scanning, there is a natural order in which the features appe ..."
Abstract
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Cited by 398 (0 self)
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Recent work on face identification using continuous density Hidden Markov Models (HMMs) has shown that stochastic modelling can be used successfully to encode feature information. When frontal images of faces are sampled using top-bottom scanning, there is a natural order in which the features
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