Serial Schedules for Belief-Propagation: Analysis of Convergence Time

Goldberger, Jacob; Kfir, Haggai

doi:10.1109/tit.2007.915702

Cited by 21 publications

(21 citation statements)

References 13 publications

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“…However, in our experience, the effects from a feature function propagate relatively slowly through the model if a parallel schedule is followed. This is in line with Goldberger and Kfir [18]. For this reason we have chosen a sequential propagation schedule which allows for faster convergence.…”

Section: Methodsmentioning

confidence: 73%

Left ventricular segmentation from MRI datasets with edge modelling conditional random fields

2013

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BackgroundThis paper considers automatic segmentation of the left cardiac ventricle in short axis magnetic resonance images. Various aspects, such as the presence of papillary muscles near the endocardium border, makes simple threshold based segmentation difficult.MethodsThe endo- and epicardium are modelled as two series of radii which are inter-related using features describing shape and motion. Image features are derived from edge information from human annotated images. The features are combined within a discriminatively trained Conditional Random Field (CRF). Loopy belief propagation is used to infer segmentations when an unsegmented video sequence is given. Powell’s method is applied to find CRF parameters by minimizing the difference between ground truth annotations and the inferred contours. We also describe how the endocardium centre points are calculated from a single human-provided centre point in the first frame, through minimization of frame alignment error.ResultsWe present and analyse the results of segmentation. The algorithm exhibits robustness against inclusion of the papillary muscles by integrating shape and motion information. Possible future improvements are identified.ConclusionsThe presented model integrates shape and motion information to segment the inner and outer contours in the presence of papillary muscles. On the Sunnybrook dataset we find an average Dice metric of 0.91±0.02 and 0.93±0.02 for the inner and outer segmentations, respectively. Particularly problematic are patients with hypertrophy where the blood pool disappears from view at end-systole.

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Section: Methodsmentioning

confidence: 73%

Left ventricular segmentation from MRI datasets with edge modelling conditional random fields

2013

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“…It has been proved that GMP with random serial schedules converges about twice as fast as the conventional GMP. The schedule analysed in [18] is randomly chosen and fixed in each realization instead of for each iteration. That is, the update schedule is the same for all iterations in [18].…”

Section: Related Workmentioning

confidence: 99%

“…The schedule analysed in [18] is randomly chosen and fixed in each realization instead of for each iteration. That is, the update schedule is the same for all iterations in [18]. As shown in our later simulations, the convergence of serial GMP heavily depends on the update order.…”

Section: Related Workmentioning

confidence: 99%

Scalable Uplink Signal Detection in C-RANs via Randomized Gaussian Message Passing

Fan

Yuan

Zhang

2017

IEEE Trans. Wireless Commun.

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Abstract-Cloud Radio Access Network (C-RAN) is a promising architecture for unprecedented capacity enhancement in nextgeneration wireless networks thanks to the centralization and virtualization of base station processing. However, centralized signal processing in C-RANs involves high computational complexity that quickly becomes unaffordable when the network grows to a huge size. Among the first, this paper endeavours to design a scalable uplink signal detection algorithm, in the sense that both the complexity per unit network area and the total computation time remain constant when the network size grows. To this end, we formulate the signal detection in C-RAN as an inference problem over a bipartite random geometric graph. By passing messages among neighboring nodes, message passing (a.k.a. belief propagation) provides an efficient way to solve the inference problem over a sparse graph. However, the traditional message-passing algorithm is not guaranteed to converge, because the corresponding bipartite random geometric graph is locally dense and contains many short loops. As a major contribution of this paper, we propose a randomized Gaussian message passing (RGMP) algorithm to improve the convergence. Instead of exchanging messages simultaneously or in a fixed order, we propose to exchange messages asynchronously in a random order. The proposed RGMP algorithm demonstrates significantly better convergence performance than conventional message passing. The randomness of the message updating schedule also simplifies the analysis, and allows the derivation of the convergence conditions for the RGMP algorithm. In addition, we generalize the RGMP algorithm to a blockwise RGMP (B-RGMP) algorithm, which allows parallel implementation. The average computation time of B-RGMP remains constant when the network size increases.

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“…This modification is known as random serial scheduling [17] or shuffled belief propagation [18]. Serial scheduling in BP decoders has been shown to converge faster than their parallel counterparts [17]- [19], without sacrificing error performance [18].…”

Section: Analysis Of Serial Schedulingmentioning

confidence: 99%

“…Serial scheduling in BP decoders has been shown to converge faster than their parallel counterparts [17]- [19], without sacrificing error performance [18]. We consider this decoder solely for the purpose of the analysis.…”

Section: Analysis Of Serial Schedulingmentioning

confidence: 99%

Ripple Design of LT Codes for BIAWGN Channels

Sørensen

Koike‐Akino

Orlik

et al. 2014

IEEE Trans. Commun.

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This paper presents a novel framework, which enables a design of rateless codes for binary input additive white Gaussian noise (BIAWGN) channels, using the ripple-based approach known from the works for the binary erasure channel (BEC). We reveal that several aspects of the analytical results from the BEC also hold in BIAWGN channels. The presented framework is applied in a code design example, which shows promising results compared to existing work. In particular it shows a great robustness towards variations in the signal-to-noise power ratio (SNR), contrary to existing codes. IEEE Transactions on CommunicationsThis work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Abstract-This paper presents a novel framework, which enables a design of rateless codes for binary input additive white Gaussian noise (BIAWGN) channels, using the ripple-based approach known from the works for the binary erasure channel (BEC). We reveal that several aspects of the analytical results from the BEC also hold in BIAWGN channels. The presented framework is applied in a code design example, which shows promising results compared to existing work. In particular it shows a great robustness towards variations in the signal-to-noise power ratio (SNR), contrary to existing codes.

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Serial Schedules for Belief-Propagation: Analysis of Convergence Time

Cited by 21 publications

References 13 publications

Left ventricular segmentation from MRI datasets with edge modelling conditional random fields

Left ventricular segmentation from MRI datasets with edge modelling conditional random fields

Scalable Uplink Signal Detection in C-RANs via Randomized Gaussian Message Passing

Ripple Design of LT Codes for BIAWGN Channels

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