• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.199-205
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• 2011

This paper presents an algorithm capable of detecting free space for the autonomous vehicle navigation. The algorithm consists of two main steps: 1) estimation of longitudinal profile of road, 2) detection of free space. The estimation of longitudinal profile of road is detection of v-line in v-disparity image which is corresponded to road slope, using v-disparity image and hough transform, Dijkstra algorithm. To detect free space, we detect u-line in u-disparity image which is a boundary line between free space and obstacle's region, using u-disparity image and dynamic programming. Free space is decided by detected v-line and u-line. The proposed algorithm is proven to be successful through experiments under various traffic scenarios.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2343-2352
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• 2015

In this paper, we propose novel motion field estimation method using U-disparity map and forward-backward error removal in vehicles environment. Generally, in an image obtained from a camera attached in a vehicle, a motion vector occurs according to the movement of the vehicle. but this motion vector is less accurate by effect of surrounding environment. In particular, it is difficult to extract an accurate motion vector because of adjacent pixels which are similar each other on the road surface. Therefore, proposed method removes road surface by using U-disparity map and performs optical flow about remaining portion. forward-backward error removal method is used to improve the accuracy of the motion vector. Finally, we predict motion of the vehicle by applying RANSAC(RANdom SAmple Consensus) from acquired motion vector and then generate motion field. Through experimental results, we show that the proposed algorithm performs better than old schemes.

• International Area Studies Review
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• v.17 no.4
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• pp.67-88
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• 2013

The purpose of this study empirically examines the pattern of regional disparities on the level of development in China's eastern, central, western and northeast regions for the period 1978-2012. To do this, it test Williamson's inverted-U hypothesis and Amos' augmented inverted-U hypothesis, focusing on polarization, polarization reversal, and spatial restructuring. Results of study are as follows: In the absolute economic disparity(AED) models of per capita income within a region, the Williamson's inverted-U hypothesis was supported in the eastern region, central region and inter-region model. The central region and the western region supports Williamson's hypothesis in the case of the relative economic disparity(RED). On the other hand, The inter-region model and the western region supports Amos' augmented inverted-U hypothesis in model of per capita income. In the urban-rural income economic disparity model, the inter-region model of AED and the central region of RED supports Amos' augmented inverted-U hypothesis. But the Williamson's inverted-U hypothesis was supported in the inter-region model and the western region in RED.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.428-435
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• 2017

In this paper, we propose a novel motion field estimation algorithm for which a U-disparity map and forward-and-backward error removal are applied in a vehicular environment. Generally, a motion exists in an image obtained by a camera attached to a vehicle by vehicle movement; however, the obtained motion vector is inaccurate because of the surrounding environmental factors such as the illumination changes and vehicles shaking. It is, therefore, difficult to extract an accurate motion vector, especially on the road surface, due to the similarity of the adjacent-pixel values; therefore, the proposed algorithm first removes the road surface region in the obtained image by using a U-disparity map, and uses then the optical flow that represents the motion vector of the object in the remaining part of the image. The algorithm also uses a forward-backward error-removal technique to improve the motion-vector accuracy and a vehicle's movement is predicted through the application of the RANSAC (RANdom SAmple Consensus) to the previously obtained motion vectors, resulting in the generation of a motion field. Through experiment results, we show that the performance of the proposed algorithm is superior to that of an existing algorithm.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.6
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• pp.86-96
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• 2010

In this paper, we propose stereo vision-based obstacle detection and vehicle verification methods using U-disparity map and bird's-eye view mapping. First, we extract a road feature using maximum frequent values in each row and column. And we extract obstacle areas on the road using the extracted road feature. To extract obstacle areas exactly we utilize U-disparity map. We can extract obstacle areas exactly on the U-disparity map using threshold value which consists of disparity value and camera parameter. But there are still multiple obstacles in the extracted obstacle areas. Thus, we perform another processing, namely segmentation. We convert the extracted obstacle areas into a bird's-eye view using camera modeling and parameters. We can segment obstacle areas on the bird's-eye view robustly because obstacles are represented on it according to ranges. Finally, we verify the obstacles whether those are vehicles or not using various vehicle features, namely road contacting, constant horizontal length, aspect ratio and texture information. We conduct experiments to prove the performance of our proposed algorithms in real traffic situations.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1141-1142
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• 2008

For intelligent vehicles, recognizing the sounding is an important task. In this paper we propose an road area detection system. This system uses u-disparity and v-disparity map. v-disparity map is used to find the road area. u-disparity is used to cluster the area that is an object. The test results and overall system is discribed in this paper.

• International Area Studies Review
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• v.13 no.2
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• pp.226-253
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• 2009

The study analyzes the trend of regional economic development disparity in China, sets up research period from 1952 to 2008, and uses the after-modified regional GDP data by the first national economic census in 2004. The results as follow. Firstly, the Coefficient of variation(CV) with after-modified GDP data lower than the pre-modified data. Secondly, generally speaking, after-reform and open period's disparity lower than pre-reform and open period. In particular, the regional development disparity increased slowly after 1990, not rapidly. Third, the new cycle of the inverse-U type is appeared from 2002. Fourth, compared with Herfindhal-Hirschman index(HHI) and Theil Entrophy index(TEI), the lower level regions more affect to reduce the disparity in 1980s, and it also affect to reduce the disparity after 2000. Fifth, the convergence hypothesis test finds that the regional economic development disparity has been converged in 1978-2008. Sixth, the inverse-U type hypothesis not has statistical significance, from 1952 to 2008, but it has statistical significance from 1991 to 2008. This result same as the CV and the convergence test.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2012.05a
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• pp.61-62
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• 2012

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.2
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• pp.107-115
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• 2002

In this paper, we describe a novel method to generate an intermediate scene using BDM (Bidirectional Disparity Morphing) from the parallel stereopair. Because an image is composed of several layers and each layer has a similar disparity, it is available to use the block based disparity estimation. In order to prevent the false correspondence, however, we closely investigate the corresponding block as we adaptively vary the block size according to the estimation error. Therefore, we can detect the occlusion because of larger estimation error of the occluded region. We define three occluding patterns, which ate derived from the peculiar property of the disparity map, in order to smooth the computed disparity map. The filtered disparity map using these patterns presents that the false disparities ate well corrected and the boundary between foreground and background becomes sharper. As a result, we can improve the quality of the intermediate scenes.

• Health Policy and Management
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• v.32 no.3
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• pp.272-281
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• 2022

With the outbreak of coronavirus disease 2019 (COVID-19) pandemic, health policymakers are adopting new policies regarding the issue of immunization disparities, especially for children in low-income communities of color who lack awareness and thereby access to vaccines. The purpose of this paper is to propose an evaluation framework using program theory-based evaluation approach and logic model to analyze and evaluate the immunization disparities in children aged 19-35 months. Data is collected from New York City department of Health and the U.S. Census Bureau for Northern Manhattan Start Right Coalition program which consists of 19,800 children, and the community-provider partnership includes 26 practices and 20 groups. Program theory is used to evaluate this community-based initiative with the logic model which is a visual depiction that illustrations the program theory to all stakeholders. The logic model highlights the resources, activities, outputs, outcomes, and impacts of the program to guide to planners and evaluators and to call attention to the inadequacies or flaws in the operational, implementation and service delivery process of the program in offering a new perspective on the program. This framework adds to the literature on evaluations of immunization disparities in determining whether evaluators can definitively attribute positive immunization outcomes in the community to the program and conclude whether it has potential in expanding or duplicating it to other similar settings, especially in other rural areas of the United States, and abroad, where routine immunization equity gaps are wide due to income, racial and ethnic diversity, and language barrier.