The Experts below are selected from a list of 147 Experts worldwide ranked by ideXlab platform
Ling Chen - One of the best experts on this subject based on the ideXlab platform.
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On Scanning Linear Barcodes From Out-of-Focus Blurred Images: A Spatial Domain Dynamic Template Matching Approach
IEEE Transactions on Image Processing, 2014Co-Authors: Ling ChenAbstract:Because of the lack of disciplined and efficient mechanisms, most modern area charge-coupled device-based barcode scanning technologies are not capable of handling out-of-focus (OOF) image blur and rely heavily on camera systems for capturing good quality, well-focused barcode images. In this paper, we present a novel linear barcode scanning system based on a dynamic template matching scheme. The proposed system works entirely in the spatial domain, and is capable of reading linear barcodes from low-resolution images containing severe OOF blur. This paper treats linear barcode scanning under the perspective of deformed binary waveform analysis and classification. A Directed Graphical Model is designed to characterize the relationship between the blurred barcode waveform and its corresponding symbol value at any specific blur level. Under this Model, linear barcode scanning is cast to find the optimal state sequence associated with the deformed barcode waveform segments. A dynamic programming-based inference algorithm is designed to retrieve the optimal state sequence, enabling real-time decoding on mobile devices of limited processing power.
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a Directed Graphical Model for linear barcode scanning from blurred images
Asian Conference on Computer Vision, 2012Co-Authors: Ling ChenAbstract:Image blur is one of the major issues deteriorating the capability of a linear barcode scanning system. In this work, linear barcode scanning is treated under the perspective of stochastic Modeling and inference. A Directed Graphical Model is proposed to characterize the relationship between barcode value and its out-of-focused waveforms, based on which highly effective inference process can be implemented, allowing decoding barcode in real-time on mobile devices, directly from blurred images. The value of the proposed Model is its potential to enlarge the operating range of current linear barcode scanning systems with no need for dedicated hardware components and making linear barcode scanning at close-up distance on fixed-focus lens a reality.
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ACCV (1) - A Directed Graphical Model for linear barcode scanning from blurred images
Computer Vision – ACCV 2012, 2012Co-Authors: Ling ChenAbstract:Image blur is one of the major issues deteriorating the capability of a linear barcode scanning system. In this work, linear barcode scanning is treated under the perspective of stochastic Modeling and inference. A Directed Graphical Model is proposed to characterize the relationship between barcode value and its out-of-focused waveforms, based on which highly effective inference process can be implemented, allowing decoding barcode in real-time on mobile devices, directly from blurred images. The value of the proposed Model is its potential to enlarge the operating range of current linear barcode scanning systems with no need for dedicated hardware components and making linear barcode scanning at close-up distance on fixed-focus lens a reality.
Slawomir Mackowiak - One of the best experts on this subject based on the ideXlab platform.
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ICCVG - Human Activity Interpretation Using Evenly Distributed Points on the Human Hull
Computer Vision and Graphics, 2014Co-Authors: Łukasz Kamiński, Krzysztof Kowalak, Pawel Gardzinski, Slawomir MackowiakAbstract:In this paper, a human activity recognition system which automatically detects human behaviors in video is presented. The solution presented in this paper uses a Directed Graphical Model with proposed by the authors Evenly Distributed Points (EDP) method. The experimental results prove efficient representation of the human activity and high score of recognition.
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Human Activity Interpretation Using Evenly Distributed Points on the Human Hull
Computer Vision and Graphics Iccvg 2014, 2014Co-Authors: Lukasz Kaminski, Krzysztof Kowalak, Pawel Gardzinski, Slawomir MackowiakAbstract:In this paper, a human activity recognition system which automatically\ndetects human behaviors in video is presented. The solution presented in\nthis paper uses a Directed Graphical Model with proposed by the authors\nEvenly Distributed Points (EDP) method. The experimental results prove\nefficient representation of the human activity and high score of\nrecognition.
Yi Wu - One of the best experts on this subject based on the ideXlab platform.
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ICCC - Robust fingerprinting-based localization using Directed Graphical Models
2017 IEEE CIC International Conference on Communications in China (ICCC), 2017Co-Authors: Yueyue Zhang, Lianfeng Shen, Yi WuAbstract:In this paper, we propose a robust fingerprinting-based localization using Directed Graphical Model. To overcome the influence caused by the jitter of received signal strength (RSS), the location of one mobile node can be estimated by fusing both the current matching result and the previous location estimation, using the Bayesian Graphical Model (BGM). Then, the localization problem is cast as a maximum a posteriori (MAP) estimator, which is also proved to coincide to maximum likelihood (ML) estimator. However, the initialized MAP estimator can be hardly solved with incomplete statistical property concerning the random vectors. To this end, we propose an adaptive smoothing algorithm (ASA) to attain the suboptimal solution of the original problem. Finally, the experimental results show that the proposed algorithm obtains a significant performance gain.
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Robust fingerprinting-based localization using Directed Graphical Models
2017 IEEE CIC International Conference on Communications in China (ICCC), 2017Co-Authors: Yueyue Zhang, Lianfeng Shen, Yi WuAbstract:In this paper, we propose a robust fingerprinting-based localization using Directed Graphical Model. To overcome the influence caused by the jitter of received signal strength (RSS), the location of one mobile node can be estimated by fusing both the current matching result and the previous location estimation, using the Bayesian Graphical Model (BGM). Then, the localization problem is cast as a maximum a posteriori (MAP) estimator, which is also proved to coincide to maximum likelihood (ML) estimator. However, the initialized MAP estimator can be hardly solved with incomplete statistical property concerning the random vectors. To this end, we propose an adaptive smoothing algorithm (ASA) to attain the suboptimal solution of the original problem. Finally, the experimental results show that the proposed algorithm obtains a significant performance gain.
Reinhold Haeb-umbach - One of the best experts on this subject based on the ideXlab platform.
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ITG Symposium on Speech Communication - Factor Graph Decoding for Speech Presence Probability Estimation
2020Co-Authors: Thomas Glarner, Mohammad Mahdi Momenzadeh, Lukas Drude, Reinhold Haeb-umbachAbstract:This paper is concerned with speech presence probability estimation employing an explicit Model of the temporal and spectral correlations of speech. An unDirected Graphical Model is introduced, based on a Factor Graph formulation. It is shown that this unDirected Model cures some of the theoretical issues of an earlier Directed Graphical Model. Furthermore, we formulate a message passing inference scheme based on an approximate graph factorization, identify this inference scheme as a particular message passing schedule based on the turbo principle and suggest further alternative schedules. The experiments show an improved performance over speech presence probability estimation based on an IID assumption, and a slightly better performance of the turbo schedule over the alternatives.
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Factor Graph Decoding for Speech Presence Probability Estimation
Speech Communication; 12. ITG Symposium, 2016Co-Authors: Thomas Glarner, Mohammad Mahdi Momenzadeh, Lukas Drude, Reinhold Haeb-umbachAbstract:This paper is concerned with speech presence probability estimation employing an explicit Model of the temporal and spectral correlations of speech. An unDirected Graphical Model is introduced, based on a Factor Graph formulation. It is shown that this unDirected Model cures some of the theoretical issues of an earlier Directed Graphical Model. Furthermore, we formulate a message passing inference scheme based on an approximate graph factorization, identify this inference scheme as a particular message passing schedule based on the turbo principle and suggest further alternative schedules. The experiments show an improved performance over speech presence probability estimation based on an IID assumption, and a slightly better performance of the turbo schedule over the alternatives.
Christopher Meek - One of the best experts on this subject based on the ideXlab platform.
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ARMA Time-Series Modeling with Graphical Models
arXiv: Applications, 2012Co-Authors: Bo Thiesson, David Maxwell Chickering, David Heckerman, Christopher MeekAbstract:We express the classic ARMA time-series Model as a Directed Graphical Model. In doing so, we find that the deterministic relationships in the Model make it effectively impossible to use the EM algorithm for learning Model parameters. To remedy this problem, we replace the deterministic relationships with Gaussian distributions having a small variance, yielding the stochastic ARMA (ARMA) Model. This modification allows us to use the EM algorithm to learn parmeters and to forecast,even in situations where some data is missing. This modification, in conjunction with the GraphicalModel approach, also allows us to include cross predictors in situations where there are multiple times series and/or additional nontemporal covariates. More surprising,experiments suggest that the move to stochastic ARMA yields improved accuracy through better smoothing. We demonstrate improvements afforded by cross prediction and better smoothing on real data.
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UAI - ARMA time-series Modeling with Graphical Models
2004Co-Authors: Bo Thiesson, David Maxwell Chickering, David Heckerman, Christopher MeekAbstract:We express the classic ARMA time-series Model as a Directed Graphical Model. In doing so, we find that the deterministic relationships in the Model make it effectively impossible to use the EM algorithm for learning Model parameters. To remedy this problem, we replace the deterministic relationships with Gaussian distributions having a small variance, yielding the stochastic ARMA (σARMA) Model. This modification allows us to use the EM algorithm to learn parameters and to forecast, even in situations where some data is missing. This modification, in conjunction with the Graphical-Model approach, also allows us to include cross predictors in situations where there are multiple time series and/or additional non-temporal covariates. More surprising, experiments suggest that the move to stochastic ARMA yields improved accuracy through better smoothing. We demonstrate improvements afforded by cross prediction and better smoothing on real data.