• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.201-208
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• 2015

3D image must have depth image for depth information in order for 3D realistic media broadcasting. We used generally belief propagation algorithm to solve probability model. Belief propagation algorithm is operated by message passing between nodes corresponding to each pixel. The high resolution image will be able to precisely represent but that required much computational complexity for 3D representation. We proposed fast stereo matching algorithm using belief propagation with multi-resolution based wavelet or lifting. This method can be shown efficiently computational time at much iterations for accurate disparity map.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.281-282
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• 2008

Turbo equalizers which use MAP (maximum a posteriori probability) equalizer or MMSE (minimum mean square error) equalizer have shown high performance and adoptability [1], [2]. In this paper, we show that the BP (belief propagation) algorithm can also be applied in equalizer and when it is connected with channel code, it can replace the MAP equalizer with similar complexity and performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.67-73
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• 2008

In the Markov network which models disparity map with the Markov Random Field(MRF), the belief propagation algorithm is operated by message passing between nodes corresponding to each pixels. Belief propagation algorithm required much iteration for accurate result. In this paper, we propose the stereo matching algorithm using belief propagation in multi-resolution domain. Multi-resolution method based on wavelet or lifting can reduce the search area, therefore this algorithm can generate disparity map with fast speed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.119-122
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• 2011

The Stereo vision using belief propagation algorithm that has been studied recently yields good performance in disparity extraction. In this paper, BP algorithm is proved theoretically to high precision for a stereo matching algorithm. We derive disparity map from stereo image by using Belief Propagation (BP) algorithm and area-based matching algorithm. Two algorithms are compared using stereo images provided by Middlebury web site. Disparity map error rate decreased from 52.3% to 2.3%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.597-607
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• 2016

The goal of this study was to analyze landslide susceptibility using two different models and compare the results. For this purpose, a landslide inventory map was produced from a field survey, and the inventory was divided into two groups for training and validation, respectively. Sixteen landslide conditioning factors were considered. The relationships between landslide occurrence and landslide conditioning factors were analyzed using the FR (Frequency Ratio) and EBF (Evidential Belief Function) models. The LSI (Landslide Susceptibility Index) maps that were produced were validated using the ROC (Relative Operating Characteristics) curve and the SCAI (Seed Cell Area Index). The AUC (Area under the ROC Curve) values of the FR and EBF LSI maps were 80.6% and 79.5%, with prediction accuracies of 72.7% and 71.8%, respectively. Additionally, in the low and very low susceptibility zones, the FR LSI map had higher SCAI values compared to the EBF LSI map, as high as 0.47%p. These results indicate that both models were reasonably accurate, however that the FR LSI map had a slightly higher accuracy for landslide susceptibility mapping in the study area.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.147-158
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• 2008

Fuzzy Cognitive Map (FCM) and Bayesian Belief Network (BBN) are two major frameworks for modeling, representing and reasoning about causal knowledge. Despite their extensive use in causal knowledge engineering, there is no reported work which compares their respective roles. This paper aims to fill the gap by providing a qualitative comparison of the two frameworks through a systematic analysis based on some inherent features of the frameworks. We proposed a set of comparison criteria which covers the entire process of causal knowledge engineering, including modeling, representation, and reasoning. These criteria are usability, expressiveness, reasoning capability, formality, and soundness. The results of comparison have revealed some important facts about the characteristics of FCM and BBN, which will help to determine how FCM and BBN should be used, with respect to each other, in causal knowledge engineering.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.256-261
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• 2007

본 논문은 이미지 완성(Image Completion)을 위한 근사적 에너지 최적화 알고리즘을 제안한다. 이미지 완성이란 이미지의 특정영역이 지워진 상태에서, 그 지워진 부분을 나머지 부분과 시각적으로 어울리도록 완성시키는 기법을 말한다. 본 논문에서 이미지 완성은 유사-확률적(pseudo-probabilistic) 시스템인 Markov Random Field로 모델링된다. MRF로 모델링된 이미지 완성 시스템에서 사후 확률(posterior probability)을 최대로 만드는 MAP(Maximum A Posterior) 문제는 결국 시스템의 전체 에너지를 낮추는 에너지 최적화 문제와 동일하다. 본 논문에서는 MRF의 최적화 알고리즘들 중에서 Belief Propagation 알고리즘을 이용한다. BP 알고리즘이 이미지 완성 분야에 적용될 때 다음 두 가지가 계산시간을 증가시키는 요인이 된다. 첫 번째는 완성시킬 영역이 넓어 MRF를 구성하는 정점의 수가 증가할 때이다. 두 번째는 비교할 후보 이미지 조각의 수가 증가할 때이다. 기존에 제안된 Priority-Belief Propagation 알고리즘은 우선순위가 높은 정점부터 메시지를 전파하고 불필요한 후보 이미지 조각의 수를 제거함으로써 이를 해결하였다. 하지만 우선순위를 정점에 할당하기 위한 최초 메시지 전파의 경우 Belief Propagation의 단점은 그대로 남아있다. 이를 개선하기 위해 본 논문에서는 이미지 완성을 위한 MRF 모델을 피라미드 구조와 같이 층위로 나누어 정점의 수를 줄이고, 계층적으로 메시지를 전파하여 시스템의 적합성(fitness)을 정교화 해나가는 Hierarchical Priority Belief Propagation 알고리즘을 제안한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.502-508
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• 2015

Stereo images have an advantage of calculating depth(distance) values which can not analyze from 2-D images. However, depth information obtained by stereo images has due to following reasons: it can be obtained by computation process; mismatching occurs when stereo matching is processing in occlusion which has an effect on accuracy of calculating depth information. Also, if global method is used for stereo matching, it needs a lot of computation. Therefore, this paper proposes the method obtaining disparity map which can reduce computation time and has higher accuracy than established method. Edge extraction which is image segmentation based on feature is used for improving accuracy and reducing computation time. Color K-Means method which is image segmentation based on color estimates correlation of objects in an image. And it extracts region of interest for applying Loopy Belief Propagation(LBP). For this, disparity map can be compensated by considering correlation of objects in the image. And it can reduce computation time because of calculating region of interest not all pixels. As a result, disparity map has more accurate and the proposed method reduces computation time.

• 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.

• Journal of the Semiconductor & Display Technology
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• v.9 no.2
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• pp.123-126
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• 2010

An implementation of modified stereo matching using efficient belief propagation (BP) algorithm is presented in this paper. We do recommend the use of the simple sobel, prewitt edge operator. The application of B band sobel edge operator over image demonstrates result with somewhat noisy (distinct border). When we adopt the only MRF + BP algorithm, however, borders cannot be distinguished due to that the message functions in the BP algorithm is just the mechanism which passes energy data to the only large gap of each Message functions In order to address the abovementioned disadvantageous phenomenon, we use the sobel edge operator + MRF + BP algorithm to distinguish the border that is located between the similar message data. Using edge information, the result shows that our proposed process diminishes the propagation of wrong probabilistic information. The enhanced result is due to that our proposed method effectively reduced errors incurred by ambiguous scene properties.