• Transactions on Electrical and Electronic Materials
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• v.13 no.1
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• pp.10-15
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• 2012

In conventional, skin detection methods using for skin color definitions is based on prior knowledge. By experimentation, the threshold value for dividing the background from the skin region is determined subjectively. A drawback of such techniques is that their performance is dependent on a threshold value which is estimated from repeated experiments. To overcome this, the present paper introduces a skin region detection method. This method uses a histogram approximation based on the mean shift algorithm. This proposed method applies the mean shift procedure to a histogram of a skin map of the input image. It is generated by comparing with the standard skin colors in the $C_bC_r$ color space. It divides the background from the skin region by selecting the maximum value according to the brightness level. As the histogram has the form of a discontinuous function. It is accumulated according to the brightness values of the pixels. It is then, approximated by a Gaussian mixture model (GMM) using the Bezier curve technique. Thus, the proposed method detects the skin region using the mean shift procedure to determine a maximum value. Rather than using a manually selected threshold value, as in existing techniques this becomes the dividing point. Experiments confirm that the new procedure effectively detects the skin region.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.623-635
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• 2021

The purpose of this research is to examine the impact of information risk on the Cost of Equity (COE) and whether the risk of a stock price crash mediates the relation between information risk and COE. To test the dynamic nature of the proposed model, the two-step system GMM dynamic panel estimators are applied to all the non-financial firms listed on the Pakistan Stock Exchange (PSX) from 2007- 2018. The results of this study show that all three types of information risk, as well as the risk of the share price crash, increases the COE. The crash risk strengthens the impact of information risk on the COE. Moreover, these three information risks are correlated with each other and an increase in information quality reduces the effect of asymmetric information and improves the investor interpreting ability, while an increase in private information decreases the transparency. The finding is crucial for asset pricing, portfolio management, and information disclosure. This study contributes to the literature by providing novel findings on the impact of three different types of information risk, i.e. private information, quality of information, and transparency of information on the COE as well as whether crash risk mediates the relationship.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.385-393
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• 2021

This study investigates the effect of infrastructure, economic sectors and its status, foreign direct investment and private investment, as well as the role of political stability in enhancing the tourism demand in the ASEAN region. The research collected the secondary data from the World Bank database and the UNWTO website of 10 ASEAN countries over 17 years from 2000 to 2016. Applying the generalized method of moments, this research found that, "private investment", "economic sectors", "exchange rate and infrastructure measured by "using of the internet" can increase the tourism demand of a country in the ASEAN region. This research provided evidence indicating that the "foreign direct investment" and "inflation" are two detrimental factors for tourist attraction. The major finding confirmed the positive role of "political stability" in increasing tourist arrivals. First, attracting tourists to a country always poses many challenges to its government. It has been observed in the past decades that though there were many documents, which confirmed that industry can help in promoting tourism, very few studies investigated the role of both agriculture and manufacturing sectors in tourism promotion. Secondly, there are only a few studies which verifies the stability of the political system to the tourism demand in the ASEAN region and that this variable (political stability) has the strongest impact.

• Journal of Korea Trade
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• v.25 no.7
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• pp.41-55
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• 2021

Purpose - This paper empirically investigates the relationship between the Economic Complexity Index (ECI) and the level of development. Moreover, this research attempts to discover the determinants of ECI in the globalization wave. Design/methodology - Our empirical model considers the relationship between ECI and the level of development in middle- and high-income economies from 1995 to 2010 by using systemic qualitative analysis, including OLS, fixed-effects, and system GMM. Next, this research used OLS regression to find the determinants of ECI. In particular, we compared the effects of different factors on ECI in the different development stages. Findings - Our main findings can be summarized as follows: 1. If the ECI increases by 1, it could lead to an increase of about 30% in the level of development in middle- and high-income economies. 2. Human capital plays an important role in the development of and increase in ECI. 3. GVC participation and outflow FDI enhance an increase in ECI, in particular in middle-income economies. 4. The development of manufacturing industries is helpful to increase ECI; however, middle-income economies should pay more attention to their comparative advantage industries. 5. R&D has positive effects on the ECI. Originality/value - To the best of our knowledge, this is the first paper that uses systemic qualitative analysis to investigate the relationship between ECI and the level of development. The paper provides suggestions for policy makers to increase ECI under the current wave of globalization, in particular in middle-income economies.

• The Korean Journal of Financial Management
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• v.24 no.1
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• pp.109-147
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• 2007

According to the perspective of capital structure theory, we analyzed the dynamism of the capital structure determinants by using panel data of 244 KOSDAQ firms based on two-step GMM system methodology suggested by Blundell Bond(1998). This dynamic methodology had not been used to analyse capital structure determinants in Korea. In the dynamic model of capital structure, profit had negative effect on the book leverage and market leverage, which meant supporting pecking order theory. Growth opportunity (MBR) affected negatively to the market leverage. For the determinants of leverage, earnings volatility had significantly positive effect on KOSDAQ 50 firms. KOSDAQ and KOSDAQ 50 firms had the target leverage. The adjustment speed in KOSDAQ firms was 0.4958 on the book leverage, it was faster than in KOSDAQ 50 firm's 0.2863 on the book leverage and the adjustment speeds for the market leverage were 0.7651 for KOSDAQ firms and 0.5643 for KOSDAQ 50 firms. There was difference in adjustment cost between KOSDAQ firms and KOSDAQ 50 firms.

• Journal of Korea Trade
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• v.27 no.3
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• pp.161-178
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• 2023

Purpose - purpose of this paper is to analyze the effects of the investment facilitation levels of 11 RCEP countries (excluding Myanmar, Brunei, and Laos due to lack of data) on China's outward foreign direct investments(OFDI) using balanced panel data from 2010 to 2019. Design/methodology - First, four investment facilitation measurement indicators (regulatory environment, infrastructure, financial market, ease of doing business) were selected,investment facilitation scores of the 11 countries were obtained using the principal component analysis, an investment gravity model was established with nine explanatory variables (investment facilitation level, market size, population, geographic distance, degree of opening, tax level, natural resources, whether the country is an APEC member or not, and whether a valid bilateral investment treaty with China has been concluded) were used to establish an investment gravity model, and regression analyses were conducted with OLS and system GMM. Findings - The results of the regression analyses showed that investment facilitation levels had the greatest effect on China's OFDI, all four first-level indicators had positive effects on China's OFDI, and among them, the institutional environment had the greatest effect. In addition, it was shown that explanatory variables such as market size, population, geographical distance, degree of openness, natural resources, and whether or not a valid bilateral investment treaty has been concluded would have positive effects on China's OFDI, while tax levels and APEC membership would impede China's OFDI to some extent. Originality/value - Since the Regional Comprehensive Economic Partnership (RCEPT) came into effect not long ago, there are not so many studies on the effects of investment facilitation levels of RCEP member states on China's OFDI, and the investment facilitation measurement index constructed in this paper is relatively systematic and scientific because it includes all the contents of investment facilitation related to the life cycle of company's foreign direct investments.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.531-548
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• 2015

Atmospheric Motion Vector (AMV) from satellite images have shown Slow Speed Bias (SSB) in comparison with rawinsonde. The causes of SSB are originated from tracking, selection, and height assignment error, which is known to be the leading error. However, recent works have shown that height assignment error cannot be fully explained the cause of SSB. This paper attempts a new approach to examine the possibility of SSB reduction of COMS AMV by using a new target tracking algorithm. Tracking error can be caused by averaging of various wind patterns within a target and changing of cloud shape in searching process over time. To overcome this problem, Gaussian Mixture Model (GMM) has been adopted to extract the coldest cluster as target since the shape of such target is less subject to transformation. Then, an image filtering scheme is applied to weigh more on the selected coldest pixels than the other, which makes it easy to track the target. When AMV derived from our algorithm with sum of squared distance method and current COMS are compared with rawindsonde, our products show noticeable improvement over COMS products in mean wind speed by an increase of $2.7ms^{-1}$ and SSB reduction by 29%. However, the statistics regarding the bias show negative impact for mid/low level with our algorithm, and the number of vectors are reduced by 40% relative to COMS. Therefore, further study is required to improve accuracy for mid/low level winds and increase the number of AMV vectors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.4
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• pp.765-773
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• 2009

Currently Telematics traffic information services have been various because we can collect real-time traffic information through Intelligent Transport System. In this paper, we proposed and implemented a short-term traffic information prediction model for giving to guarantee the traffic information with high quality in the near future. A Short-term prediction model is for forecasting traffic flows of each segment in the near future. Our prediction model gives an average speed on the each segment from 5 minutes later to 60 minutes later. We designed a Bayesian network for each segment with some casual nodes which makes an impact to the road situation in the future and found out its joint probability density function on the supposition of GMM(Gaussian Mixture Model) using EM(Expectation Maximization) algorithm with training real-time traffic data. To validate the precision of our prediction model we had conducted various experiments with real-time traffic data and computed RMSE(Root Mean Square Error) between a real speed and its prediction speed. As the result, our model gave 4.5, 4.8, 5.2 as an average value of RMSE about 10, 30, 60 minutes later, respectively.

• Journal of Digital Convergence
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• v.10 no.8
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• pp.211-217
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• 2012

Conventional GrabCut algorithm is semi-automatic algorithm that user must be set rectangle window surrounds the object. This paper studied automatic object detection to solve these problem by detecting salient region based on Human Visual System. Saliency map is computed using Lab color space which is based on color opposing theory of 'red-green' and 'blue-yellow'. Then Saliency Points are computed from the boundaries of Low-Frequency region that are extracted from Saliency Map. Finally, Rectangle windows are obtained from coordinate value of Saliency Points and these windows are used in GrabCut algorithm to extract objects. Through various experiments, the proposed algorithm computing rectangle windows of salient region and extracting objects has been proved.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.493-497
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• 2016

Infrared (IR) imaging is capable of detecting targets that are not visible at night, thus it has been widely used for the security and defense system. However, the quality of the IR image is often degraded by low resolution and noise corruption. This paper addresses target segmentation with the IR image. Multiple regions of interest (ROI) are extracted by the multi-level segmentation and targets are segmented from the individual ROI. Each level of the multi-level segmentation is composed of a k-means clustering algorithm an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering algorithm initializes the parameters of the Gaussian mixture model (GMM) and the EM algorithm iteratively estimates those parameters. Each pixel is assigned to one of clusters during the decision. This paper proposes the selection and the merging of the extracted ROIs. ROI regions are selectively merged in order to include the overlapped ROI windows. In the experiments, the proposed method is tested on an IR image capturing two pedestrians at night. The performance is compared with conventional methods showing that the proposed method outperforms others.