• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2228-2230
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• 1998

This paper presents the development of depth extraction algorithm for the stereoscopic endoscope data using a stereo matching method. generally, the purpose of existing stereo algorithms is to reconstruct stereo object surface and depth map. but the main purpose of our processing is to give exact depth feeling to doctor showing depth information in some points. for this purpose, this paper presents two stereo matching algorithms which are to measure exact depth. one is using variable window, and the other is reference points-based algorithm for a fast processing.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.397-404
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• 2011

This paper proposes an algorithm capable of extracting corresponding points between a pair of stereo images based on dynamic programming. The purpose of extracting the corresponding points is to provide the stereo disparity data to a road-slope estimation algorithm with high accuracy and in real-time. As the road-slope estimation algorithm does not require dense disparity data, the proposed stereo matching algorithm aims at extracting corresponding points accurately and quickly. In order to realize this contradictory goal, this paper exploits dynamic programming, and minimizes matching candidates using vertical components of color edges. Furthermore, the typical occlusion problem in stereo vision is solved. The proposed algorithm is proven to be effective through experiments with various images captured on the roads.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.75-85
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• 2013

To acquire depth information using stereo vision, it is required to find correspondence points between stereo image pair. Conventional stereo vision systems usually use two cameras to get disparity data. Therefore, conventional stereo matching methods cannot resolve the tradeoff problem between accuracy and precision with respect to the length of baseline. Besides, belief propagation method, which is being used recently, has a problem that matching performance is dependent on the fixed weight parameter ${\lambda}$. In this paper, we propose a modified belief propagation stereo matching technique based on multi-baseline stereo vision to solve the tradeoff problem. The proposed method calculates EMAD(extended mean of absolute differences) as local evidence. And proposed method decides weight parameter ${\lambda}$ adaptively to local texture information. The proposed method shows higher initial matching performance than conventional methods and reached optimum solution in less iteration. The matching performance is increased about 4.85 dB in PSNR.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.69-78
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• 1996

This paper presetns an iterative relaxation method for stereo matching using matching probability and compatibility coefficients between disparities. Stereo matching can be considered as the labeling problem of assigning unique matches to feature points of image an relaxation labelin gis an iterative procedure which reduces local ambiguities and achieves global consistency. the relation between disparities is determined from highly reliable matches in initial matching and quantitatively expressed in temrs of compatibility coefficient. The matching results of neighbor pixels support center pixel through compatibility coefficients and update its matching probability. The proposed adaptive method reduces the degradtons on the discontinuities of disparity areas and obtains fast convergence.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.123-128
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• 2015

In this study, experimented how to increase corresponding points which are obtained through stereo matching for dense 3D reconstruction. After extracting a snapshot image from the images acquired through stereo CCTVs, the matching points obtained using the SIFT matching and RANSAC procedure were gradually overlapped. In conclusion, it was confirmed that as images are overlapped, the number of matching points continues to grow.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.88-95
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• 2011

Stereo matching is the research area that regarding the estimation of the distance between objects and camera using different view points and it still needs lot of improvements in aspects of speed and accuracy. This paper presents a fast stereo matching algorithm based on plane-converging belief propagation that uses message passing convergence in hierarchical belief propagation. Also, stereo matching technique is developed using GPU and it is available for real-time applications. The error rate of proposed Plane-converging Belief Propagation algorithm is similar to the conventional Hierarchical Belief Propagation algorithm, while speed-up factor reaches 2.7 times.

• Journal of KIISE:Software and Applications
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• v.32 no.3
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• pp.173-180
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• 2005

This paper proposes an effective algorithm that combines both the stereo matching and the marching cube algorithm. By applying the stereo matching technique to an image obtained from various angles, 3D geometry data are acquired, and using the camera extrinsic parameter, the images are combined. After reconstructing the combined data into mesh using the image index, the normal vector equivalent to each point is obtained and the mesh smoothing is processed. This paper describes the successive processes and techniques on the 3D mesh reconstruction, and by proposing the intermediate iso- surface algorithm. Therefore it improves the 3D data instability problem caused when using the conventional marching cube algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3C
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• pp.279-287
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• 2009

In this paper, a hierarchical stereo matching with color information is proposed. To generate an initial disparity map, feature based stereo matching is carried out and to generate a final disparity map, hierarchical stereo matching is carried out. The boundary (edge) region is obtained by segmenting a given image into R, G, B and White components. From the obtained boundary, disparity is extracted. The initial disparity map is generated when the extracted disparity is spread to the surrounding regions by evaluating autocorrelation from each color region. The initial disparity map is used as an initial value for generating the final disparity map. The final disparity map is generated from each color region by changing the size of a block and the search range. 4 test images that are provided by Middlebury stereo vision are used to evaluate the performance of the proposed algorithm objectively. The experiment results show better performance compared to the Graph-cuts and Dynamic Programming methods. In the final disparity map, about 11% of the disparities for the entire image were inaccurate. It was verified that the boundary for the non-contiguous point was clear in the disparity map.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.737-738
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• 2008

An implementation of robust stereo matching algorithm under variable illumination conditions is presented in this paper. By applying window-based matchingmethod to global stereo matching algorthm using belief propagation, our implemented system shows low error rate and could efficiently cope with general radiometric disimilarities between images acquired from two different point of views.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.403-404
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• 2006

In this paper, we proposed muli-dimensional feature vector matching method combined with disparity smoothness constraint. The smoothness constraint was calculated using the difference between disparity of center pixel and those of 4-neighbor pixels. By applying proposed algorithm to IKONOS satellite stereo imagery, we obtained robust stereo matching result in urban areas.