• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.817-818
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• 2008

In the present paper we propose a new method of intermediate view reconstruction between stereo images using belief propagation_based stereo matching. Intermediate view reconstruction is an important step for multiview 3D display. Many previous paper about intermediate view reconstruction using depth information to synthesize interview though stereo matching were proposed. But depth information is different to estimated accurately. In the present paper, in order to obtain accurate depth information, belief propagation_based stereo matching was used.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.2
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• pp.95-101
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• 2020

In this paper, we consider a group testing (GT) framework which is to find a set of defective samples out of a large number of samples. To handle this framework, we propose a maximum product detection algorithm (MPDA) which is based on maximum a posteriori probability (MAP). The key idea of this algorithm exploits iterative detection to propagate belief to neighbor samples by exchanging marginal probabilities between samples and output results. The belief propagation algorithm as a conventional approach has been used to detect defective samples, but it has computational complexity to obtain the marginal probability in the output nodes which combine other marginal probabilities from the sample nodes. We show that the our proposed MPDA provides a benefit to reduce computational complexity up to 12% in runtime, while its performance is only slightly degraded compared to the belief propagation algorithm. And we verify the simulations to compare the difference of performance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.4
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• pp.223-228
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• 2014

It is known that a belief propagation algorithm is a fast decoding scheme for LT codes but it require a large overhead, especially for a short block length LT codes. In this paper an improved belief decoding algorithm using searching method for degree-1 packets is proposed for a small overhead. The proposed decoding scheme shows the desirable performance in terms of overhead while guaranteeing the same computational complexity with respect to the conventional BP decoding scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4405-4418
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• 2016

Automatic image completion techniques have difficulty processing images in which the target region has multiple planes or is non-facade. Here, we propose a new image completion method that uses belief propagation based on planar priorities. We first calculate planar information, which includes planar projection parameters, plane segments, and repetitive regularity extractions within the plane. Next, we convert this planar information into planar guide knowledge using the prior probabilities of patch transforms and offsets. Using the energy of the discrete Markov Random Field (MRF), we then define an objective function for image completion that uses the planar guide knowledge. Finally, in order to effectively optimize the MRF, we propose a new optimization scheme, termed Planar Priority-belief propagation that includes message-scheduling-based planar priority and dynamic label cropping. The results of experiment show that our approach exhibits advanced performance compared with existing approaches.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.162-164
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• 2010

Terrestrial trunked radio (TETRA) standard specifies shortened Reed-Muller (RM) codes as forward error correction means for control signals. In this paper, we compare decoding algorithms for RM codes in TETRA, in terms of performance and complexity trade-off. Belief propagation and majority logic decoding algorithms are selected for comparison.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.52-53
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• 2012

Belief propagation (BP) is a commonly used global energy minimization algorithm for solving stereo matching problem in 3D reconstruction. However, it requires large memory bandwidth and data size. In this paper, we propose a novel memory-efficient algorithm of BP in stereo matching on the Graphics Processing Units (GPU). The data size and transfer bandwidth are significantly reduced by storing only a part of the whole message. In order to maintain the accuracy of the matching result, the local messages are reconstructed using shared memory available in GPU. Experimental result shows that there is almost an order of reduction in the global memory consumption, and 21 to 46% saving in memory bandwidth when compared to the conventional algorithm. The implementation result on a recent GPU shows that we can obtain 22.8 times speedup in execution time compared to the execution on CPU.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.397-410
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• 2016

This paper proposes a novel cooperative localization method for distributed wireless networks in 3-dimensional (3D) global positioning system (GPS) denied environments. The proposed method, which is referred to as hybrid ellipsoidal variational algorithm (HEVA), combines the use of non-parametric belief propagation (NBP) and variational Bayes (VB) to benefit from both the use of the rich information in NBP and compact communication size of a parametric form. InHEVA, two novel filters are also employed. The first one mitigates non-line-of-sight (NLoS) time-of-arrival (ToA) messages, permitting it to work well in high noise environments with NLoS bias while the second one decreases the number of calculations. Simulation results illustrate that HEVA significantly outperforms traditional NBP methods in localization while requires only 50% of their complexity. The superiority of VB over other clustering techniques is also shown.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06b
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• pp.227-230
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• 2010

비젼 기반의 휴먼 인체 추적 문제는 추정해야 할 파라미터의 수가 많고, 인체 구성 요소들 간의 가려짐등으로 인해 발생하는 포즈 간의 모호성 등으로 야기되는 어려움을 가지고 있다. 본 논문에서는 다양한 휴먼 상체 포즈에 따른 고차원 탐색 공간을 효율적으로 계산할 수 있는 Mean-Shift Belief Propagation (MSBP) 및 역기구학(Inverse Kinematics) 제약에 기반한 방법을 제안한다. 제안된 방법의 효율성을 증명하기 위해 실험을 기존에 제안된 방법들과의 비교 실험을 수행하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.1-8
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• 2008

The belief propagation method that has been studied recently yields good performance in disparity extraction. The method in which a target function is modeled as an energy function based on Markov random field(MRF), solves the stereo matching problem by finding the disparity to minimize the energy function. MRF models provide robust and unified framework for vision problem such as stereo and image restoration. the belief propagation method produces quite correct results, but it has difficulty in real time implementation because of higher computational complexity than other stereo methods. To relieve this problem, in this paper, we propose a fast algorithm of the belief propagation method. Energy function consists of a data term and a smoothness tern. The data term usually corresponds to the difference in brightness between correspondences, and smoothness term indicates the continuity of adjacent pixels. Smoothness information is created from messages, which are assigned using four different message arrays for the pixel positions adjacent in four directions. The processing time for four message arrays dominates 80 percent of the whole program execution time. In the proposed method, we propose an algorithm that dramatically reduces the processing time require in message calculation, since the message.; are not produced in four arrays but in a single array. Tn the last step of disparity extraction process, the messages are called in the single integrated array and this algorithm requires 1/4 computational complexity of the conventional method. Our method is evaluated by comparing the disparity error rates of our method and the conventional method. Experimental results show that the proposed method remarkably reduces the execution time while it rarely increases disparity error.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.737-738
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• 2008

An implementation of robust stereo matching algorithm under variable illumination conditions is presented in this paper. By applying window-based matchingmethod to global stereo matching algorthm using belief propagation, our implemented system shows low error rate and could efficiently cope with general radiometric disimilarities between images acquired from two different point of views.