• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.313-314
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• 2007

In this paper, we suggest robust object extraction algorithm taking advantage of efficient Belief Propagation method. It does not get a disparity information because of uniform region and occlusion region etc. on initial depth map that use forward direction disparity information although is object area. Therefore, We run parallel backward disparity information and brightness information for certain object extraction.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2941-2943
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• 1999

The application of the aerial images are to find the 3-D elevations. Image matching techniques such as Multi-resolution techniques, WCC (Weighted Cross-Correlation), NSSR (Narrow Search Sub-pixel Registration) that we know robustly apply to images which have enough features. But the method is not adaptive in images which have not enough features due to increasing of disparity errors. In this paper, we propose Disparity Interpolation that decrease disparity errors occurring in the area where images have not enough features. By using real aerial images we compare the result from existing image matching techniques to the result from proposed method.

• Annual Conference of KIPS
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• 2013.11a
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• pp.1464-1466
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• 2013

기존의 스테레오 매칭 시스템은 얻어지는 영상에 따라 disparity의 결과가 크게 차이를 나타내게 된다. 이러한 오류를 줄이고자 일정한 패턴을 주사하여 스테레오 매칭을 계산하는 방식인 액티브 스테레오 기법이 대두되고 있다. 본 논문에서는 이러한 액티브 스테레오 매칭을 사용시에 얻어질 수 있는 패턴 영상과 패턴이 없는 원영상을 서로 조합하여 스테레오 매칭을 수행함으로서 정확한 disparity를 얻고자 한다. 이러한 영상의 합성시에 두 영상의 비율에 따라서, 얻어지는 영상의 밝기 구성에 따라서 disparity결과의 차이와 이를 실제 시스템을 구성시에 필요한 점에 대해서 확인하고자 한다.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.871-875
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• 2003

To detect and avoid obstacles is one of the important tasks of mobile navigation. In a real environment, when a mobile robot encounters dynamic obstacles, it is required to simultaneously detect and avoid obstacles for its body safely. In previous vision system, mobile robot has used it as either a passive sensor or an active sensor. This paper proposes a new obstacle detection algorithm that uses a stereo camera as both a passive sensor and an active sensor. Our system estimates the distances from obstacles by both passive-correspondence and active-correspondence using laser slit. The system operates in three steps. First, a far-off obstacle is detected by the disparity from stereo correspondence. Next, a close obstacle is acquired from laser slit beam projected in the same stereo image. Finally, we implement obstacle avoidance algorithm, adopting the modified Dynamic Window Approach (DWA), by using the acquired the obstacle's distance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3208-3229
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• 2017

Rectification is an essential procedure for simplifying the disparity extraction of stereo matching algorithms by removing vertical mismatches between left and right images. To support real-time stereo matching, studies have introduced several look-up table (LUT)- and computational logic (CL)-based rectification approaches. However, to support high-resolution images, the LUT-based approach requires considerable memory resources, and the CL-based approach requires numerous hardware resources for its circuit implementation. Thus, this paper proposes a multi-level accumulation-based rectification method as a simple CL-based method and its circuit implementation. The proposed method, which includes distortion correction, reduces addition operations by 29%, and removes multiplication operations by replacing the complex matrix computations and high-degree polynomial calculations of the conventional rectification with simple multi-level accumulations. The proposed rectification circuit can rectify $1,280{\times}720$ stereo images at a frame rate of 135 fps at a clock frequency of 125 MHz. Because the circuit is fully pipelined, it continuously generates a pair of left and right rectified pixels every cycle after 13-cycle latency plus initial image buffering time. Experimental results show that the proposed method requires significantly fewer hardware resources than the conventional method while the differences between the results of the proposed and conventional full rectifications are negligible.

• Journal of Korea Multimedia Society
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• v.10 no.6
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• pp.716-725
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• 2007

In this paper we describe a motion tracking algorithm for 3D human animation using stereo vision system. This allows us to extract the motion data of the end effectors of human body by following the movement through segmentation process in HIS or RGB color model, and then blob analysis is used to detect robust shape. When two hands or two foots are crossed at any position and become disjointed, an adaptive algorithm is presented to recognize whether it is left or right one. And the real motion is the 3-D coordinate motion. A mono image data is a data of 2D coordinate. This data doesn't acquire distance from a camera. By stereo vision like human vision, we can acquire a data of 3D motion such as left, right motion from bottom and distance of objects from camera. This requests a depth value including x axis and y axis coordinate in mono image for transforming 3D coordinate. This depth value(z axis) is calculated by disparity of stereo vision by using only end-effectors of images. The position of the inner joints is calculated and 3D character can be visualized using inverse kinematics.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.4B
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• pp.763-770
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• 2000

In this paper, a new stereo image compression algorithm is suggested in which the residual image extracted from the stereo image by the disparity-compensated prediction method is compressed using the wavelet transform considering the inter & intra correlation between subbands. The compression performance of the proposed method is significantly improved by comparing with the conventional algorithm such as EPIC, EPWIC & JPEG through the computer simulation and the PSNR is also increased about 3.5dB compared with the EPIC. Finally, the stereo image having a good 3D effect can be reconstructed from the compressed image data by the proposed method.

• 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 Korea Multimedia Society
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• v.9 no.10
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• pp.1296-1303
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• 2006

The range-based method is easy to get the 3D data in detail, but the image-based is not. In this paper, I suggests the new approach to get the 3D data in detail from the magnified stereo image. Main idea is using the magnifying lens. The magnifying lens not only magnifies the object but also increases the depth resolution. The relation between the amplification of the disparity and the increase of the depth resolution is verified mathematically and the method to improve the original 3D data is suggested.

• Korean Journal of Remote Sensing
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• v.16 no.4
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• pp.347-353
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• 2000

The purpose of this paper is to extract DEM (Digital Elevation Model) using KOMPSAT images. DEM extraction consists of three parts. First part is the modeling of satellite position and attitude, second part is the matching of two images to find corresponding points of them and third part is to calculate the elevation of each point by using the result of the first and second part. The position and attitude modeling of satellite is processed by using GCPs. Area based matching method is used to find the corresponding points between the stereo satellite images. The elevation of each point is calculated using the exterior orientation information obtained from sensor modeling and the disparity from the stereo matching. In experiment, the KOMPSAT images, 2592$\times$2796 panchromatic images are used to extract DEM. The experiment result show the DEM using KOMPSAT images.