• Journal of Broadcast Engineering
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• v.21 no.6
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• pp.913-919
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• 2016

In this paper, we propose the visual tracking algorithm that takes advantage of multiple random walkers. We first show the tracking method based on support vector machine as [1] and suggest a method that suppresses feature vectors extracted from backgrounds while preserve features vectors from foregrounds. We also show how to discriminate between foregrounds and backgrounds. Learned by reducing influences of backgrounds, support vector machine can clearly distinguish foregrounds and backgrounds from the image whose target objects are similar to backgrounds and occluded by another object. Thus, the algorithm can track target objects well. Furthermore, we introduce a simple method improving tracking speed. Finally, experiments validate that proposed algorithm yield better performance than the state-of-the-art trackers on the widely-used benchmark dataset with high speed.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.273-274
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• 2016

본 논문에서는 다중 랜덤 워커(multiple random walkers)에 기반한 객체 추적 기법을 제안한다. 우선 서포트 벡터 머신(support vector machine)을 이용한 분류기 기반 객체 추적 기법을 소개한다. 다음으로 영상의 영역에 대한 특징 벡터 중 배경으로부터 추출된 특징 벡터를 억제하는 기법을 제안한다. 영역에서 배경 요소를 찾기 위해 다중 랜덤 워커를 이용한 전경 및 배경 추출 방법을 제시한다. 배경 요소를 억제하여 학습된 서포트 벡터 머신은 객체와 배경이 유사한 영상, 객체가 다른 물체에 의해 가려지는 영상 등에서 객체와 배경을 확실하게 구분하여 객체를 잃지 않고 추적할 수 있다. 마지막으로 실험을 통해 제안하는 기법이 기존 기법에 비해 우수한 추적 성능을 보임을 확인한다.

• Journal of Mechanical Science and Technology
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• v.17 no.4
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• pp.545-561
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• 2003

We use the first-passage-time formulation by Torquato, Kim and Cule ［J. Appl. Phys., Vol. 85, pp. 1560∼1571 (1999) ］, which makes use of the first-passage region in association with the diffusion tracer's Brownian movement, and develop a new efficient Brownian motion simulation method to compute the effective conductivity of digitized composite media. By using the new method, one can remarkably enhance the speed of the Brownian walkers sampling the medium and thus reduce the computation time. In the new method, we specifically choose the first-passage regions such that they coincide with two, four, or eight digitizing units according to the dimensionality of the composite medium and the local configurations around the Brownian walkers. We first obtain explicit solutions for the relevant first-passage-time equations in two-and three-dimensions. We then apply the new method to solve the illustrative benchmark problem of estimating the effective conductivities of the checkerboard-shaped composite media. for both periodic and random configurations. Simulation results show that the new method can reduce the computation time about by an order of magnitude.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.11a
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• pp.21-22
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• 2016

본 논문에서는 동영상에서 객체를 자동 검출하는 기법을 제안한다. 제안하는 기법은 정지 영상에서 객체를 검출하는 기법과 동영상에서 객체를 추적하는 기법을 동시에 수행하여 동영상에서 객체를 검출한다. 매 프레임 검출기는 학습된 종류의 객체들을 검출하고 추적기는 이전 프레임에서 검출되었던 객체를 추적한다. 검출기가 검출한 결과와 추적기가 추적한 결과를 매칭하고, 겹치는 결과와 그렇지 않은 결과에 대해 각각 다른 검사를 수행하여 신뢰도 있는 결과를 도출한다. 실험 결과를 통해 제안하는 기법이 기존 검출 기법에 비해 우수한 성능을 보임을 확인한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1712-1731
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• 2016

In this paper, we propose a novel saliency detection framework via multiple random walks (MRW) which simulate multiple agents on a graph simultaneously. In the MRW system, two agents, which represent the seeds of background and foreground, traverse the graph according to a transition matrix, and interact with each other to achieve a state of equilibrium. The proposed algorithm is divided into three steps. First, an initial segmentation is performed to partition an input image into homogeneous regions (i.e., superpixels) for saliency computation. Based on the regions of image, we construct a graph that the nodes correspond to the superpixels in the image, and the edges between neighboring nodes represent the similarities of the corresponding superpixels. Second, to generate the seeds of background, we first filter out one of the four boundaries that most unlikely belong to the background. The superpixels on each of the three remaining sides of the image will be labeled as the seeds of background. To generate the seeds of foreground, we utilize the center prior that foreground objects tend to appear near the image center. In last step, the seeds of foreground and background are treated as two different agents in multiple random walkers to complete the process of salient object detection. Experimental results on three benchmark databases demonstrate the proposed method performs well when it against the state-of-the-art methods in terms of accuracy and robustness.

• The Journal of Korean Physical Therapy
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• v.3 no.1
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• pp.123-149
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• 1991

This study has been attempted to be helpful for the back rehabilization of Korean workers by analyzing the general, occupational, social aspects of low back pain and to identify possible risk factors for back pain in White and Blue collar workers. The primary data were collected from 380 workers in Seoul city by means of a Questionnaire with random which was distributed from March 10 to 31, 1990. For the test of statistical significance, chi-square analysis was used to compare the back pain characteristics between above two groups. The results were as follows : 1. The incidence of low back pain in all walkers studied was $79.7\%$. The incidence of Blue collar with low back pain $(87.2\%)$ was higher than that of White collars $(75.0\%)$. 2. With regard to the relationship of back pain to the occupational characteristics, statistically significant differences were observed between workers with and without back pain concerning the job factors on work-time, job satisfaction, mental stress, chair fittness, work posture, work rotation, weight lifting, monotonous repetitive movements, vibration, and heavy noise (p<0.05). 3. With regard to the relationship of back pain to the social characteristics, there were no differences with respect to having car, personality types, drinking habits, and leisure-time activities. However, significant differences were showed between no pain and pain groups for the using bed, sleeping posture, traffic time amount, walking health state, smoking habits, and physical exercise (p<0.05). 4. The comparative analysis of back pain related to work factors showed highly significant differences with respect to mental stress, chair fittness, work posture, trunk rotation, weight lifting, monotonous repetitive work in White collar group (p<0.01) ; and job satisfaction, mental stress, trunk rotation, weight lifting, monotonus repetitive work, exposure to vibration and heavy noise in Blue collar group(p<0.01). 5. The comparative analysis of social factors in two groups showed differences with respect to the using bed, sleeping posture, walking amount, health state, physical exercise, smoking habits in White cellar group (p<0.05) ; and walking amount, traffic time amount, health state, smoking habits, physical exercise in Blue cellar group (p<0.05). 6. In regard to the general aspects of back pain between two groups, there were differences concerning etiolgy of back pain, counselling partners, treatment types, and sick-leaves(p<0.05), except pain duration, and awareness of back pain.