• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.52-54
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• 2015

In this paper we propose a new background motion compensation method which can be applicable to moving object detection with a moving monocular camera. To estimate the background motion, a series of image warpings are carried out for each pair of the corresponding patches, defined by the fixed-size lattice, based on the motion information extracted from the feature points surrounded by the patches and the estimated camera motion. Experiment results proved that the proposed has approximately 50% faster in execution time and 8dB higher in PSNR comparing to a conventional method.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.1
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• pp.47-58
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• 2019

The purpose of this paper is to examine the relationship between the nature of controlling shareholders and corporate anti-corruption practice disclosure (ACPD) as well as the mediating role of political background of the chairman or CEO of the firm on the relationship between the two. The content analysis was conducted to extract ACPD from standalone corporate social responsibility reports (CSRR) of 703 China's A-share listed companies. A dummy variable was constructed according to whether a firm disclosed ACPD or not. Logistic regression analysis was used then. Results show that the nature of controlling shareholders has a significant impact on corporate ACPD, with central enterprises disclosing the most frequently, local state-owned enterprises the second and private enterprises the least. Political background of the chairman or CEO has a negative impact on corporate ACPD of state-owned enterprises. These findings have some useful insights in understanding the rent-seeking behavior and information disclosure behavior of corporates in emerging markets. In order to curb the serious corruption problem which is commonplace in developing countries like China, the government should exert certain pressure to strengthen the supervision of information disclosure of listed firms and improve information transparency.

• Korean Journal of Remote Sensing
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• v.32 no.1
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• pp.61-65
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• 2016

Importance of remote sensing for surface is increased than past. So many countries try to many ways to retrieve surface reflectance. In this study, we study a Bidirectional Reflectance Distribution Function (BRDF) to retrieve surface reflectance. We apply BRDF using observed surface reflectance of SPOT/VEGETATION (VGT-S1) and angular data to get Bidirectional Reflectance Distribution (BRD) coefficients for calculating scattering. And then we apply BRDF in the opposite direction with BRD coefficients and angular data to retrieve Background Surface Reflectance (BSR). The range of BSR is not over $0.4{\mu}m$ (blue), $0.45{\mu}m$ (red), $0.55{\mu}m$ (NIR). And for validation we compare BSR with VGT-S1, there are bias is from 0.0116 to 0.0158 and RMSE is from 0.0459 to 0.0545. As a result, we confirm that BSR is similar to VGT-S1.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.4
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• pp.866-872
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• 2004

In fingerprint verification and identification, fingerprint and background region should be segmented. For this purpose, most systems obtain variance of brightness of X and Y direction using Sobel mask. To decide given local region is background or not, the variance is compared with a certain threshold. Although this method is simple, most fingerprint image does not separated with two region of fingerprint and background region. In this paper, we presented a new segmentation algorithm based on run-length connectivity analysis. For a given binary image after thresholding, suggested algorithm calculates RL of X and Y direction. Until the given image is segmented to two regions, small run region is successively inverted. Experimental result show that this algorithm effectively separates fingerprint region and background region.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.90-94
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• 2010

In this paper, we propose a vehicle tracking method that can be applied in the intelligent traffic information system. The proposed method mainly consists of two steps: overlap removal and background updating. In order to remove overlap, we detect the overlap based on the location of the vehicle from successive images. Background updating is to calculate a background using statistical analysis of successive images. We collected a set of test images from the traffic monitoring system and experimented. The experimental results show more than 96% of tracking accuracy.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.228-237
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• 2013

In this paper, we propose a normalized cross correlation(NCC)-based multiview background subtraction method which is robust when an object and background have similar color. When the background of the capturing environment is not artificially composed, the regions in the background images which would be occluded by an object tends to have difference colors. The colors of those regions, however, becomes similar when an object enters the capturing environment. Based on this assumption, this paper proposes a concept of GoNCC(Graph of Normalized Cross Correlation). GoNCC is the distribution of NCC between a pixel in an image and pixels related by epipolar constraints with the pixel. The proposed multiview background subtraction method is performed by comparing GoNCC of the current images with the background images. To reduce computational complexity, we perform multiview background subtraction only to the pixels undetermined by single view background subtraction. Experimental results show that the proposed method is more robust to color similarity between an object and background than a single-view background subtraction method and a previous multiview background subtraction method.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.515-524
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• 2006

Background extraction is an important technique to find the moving objects in video surveillance system. This paper proposes a new on-line background extraction method for color video using vector order statistics. In the proposed method, using the fact that background occurs more frequently than objects, the vector median of color pixels in consecutive frames Is treated as background at the position. Also, the objects of current frame are consisted of the set of pixels whose distance from background pixel is larger than threshold. In the paper, the proposed method is compared with the on-line multiple background extraction based on Gaussian mixture model(GMM) in order to evaluate the performance. As the result, its performance is similar or superior to the method based on GMM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2141-2155
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• 2020

In order to meet the requirements of background change, illumination variation, moving shadow interference and high accuracy in object detection of moving camera, and strive for real-time and high efficiency, this paper presents an object detection algorithm based on sparse approximation recursion and sparse coding migration in subspace. First, low-rank sparse decomposition is used to reduce the dimension of the data. Combining with dictionary sparse representation, the computational model is established by the recursive formula of sparse approximation with the video sequences taken as subspace sets. And the moving object is calculated by the background difference method, which effectively reduces the computational complexity and running time. According to the idea of sparse coding migration, the above operations are carried out in the down-sampling space to further reduce the requirements of computational complexity and memory storage, and this will be adapt to multi-scale target objects and overcome the impact of large anomaly areas. Finally, experiments are carried out on VDAO datasets containing 59 sets of videos. The experimental results show that the algorithm can detect moving object effectively in the moving camera with uniform speed, not only in terms of low computational complexity but also in terms of low storage requirements, so that our proposed algorithm is suitable for detection systems with high real-time requirements.

• Journal of information and communication convergence engineering
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• v.13 no.1
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• pp.42-49
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• 2015

In laser pointer interaction systems, laser spot detection is one of the most important technologies, and most of the challenges in this area are related to the varying backgrounds, and the real-time performance of the interaction system. In this paper, we present a robust dictionary construction and update algorithm based on a sparse model of background subtraction. In order to control dynamic backgrounds, first, we determine whether there is a change in the backgrounds; if this is true, the new background can be directly added to the dictionary configurations; otherwise, we run an online cumulative average on the backgrounds to update the dictionary. The proposed dictionary construction and update algorithm for laser spot detection, is robust to the varying backgrounds and noises, and can be implemented in real time. A large number of experimental results have confirmed the superior performance of the proposed method in terms of the detection error and real-time implementation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.85-96
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• 2006

This paper deal with a method for efficient placing 2 dimensional virtual labels on the view plane. The proposed method has suitable computational costs for realtime processing and it overcomes the local minima problem which is not solved in previous automatic label placement algorithms, and also it enhances readability by placing labels in less congestion area on the view plane. Background analysis must be considered for label placement. However previous works do not concern with this problem seriously. And furthermore, automatic label placement algorithm and background analysis algorithm have been studied separately in their own field. This paper proposed the background analysis method using background color and texture component to enhance readability, and it is the first research about analyzing the background of color image and applying it in automatic label placement field. This paper shown improved placement performance through combining automatic label placement algorithm and background analysis algorithm organically, and various experiments verified it.