• Journal of Korean Society for Geospatial Information Science
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• v.3 no.1 s.5
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• pp.55-66
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• 1995

Most studies using satellite images have been performed to determine three dimensional positioning by stereoscopic analysis for stereo-pair or to extract digital elevation model by stereo matching using image correlation techniques. Because the small errors on the ground control points have a great impact on the results, however, it is hard to get reliable products when we analyze satellite orbital parameters or acquire digital elevation model by using only stereo-pair. Also, if there are noises, shadows, or clouds on the one of stereo pair, it is difficult to produce DEM(digital elevation model) on the area under analysis or to have good accuracy. In these case, it can be solved by systematic analysis of the multiple stereo images. This paper suggests the improvements on the accuracy of the digital elevation model by the developments of stereoscopic analysis techniques for the triplet of SPOT satellite images on the same area.

• 한국지형공간정보학회:학술대회논문집
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• 1995.10a
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• pp.99-119
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• 1995

Most studies using satellite images have been performed to determine three dimensional positioning by stereoscopic analysis for stereo-pair or to extract digital elevation model by stereo matching using image correlation techniques. Because the small errors on the ground control points have a great impact on the results, honorer, it is hard to get reliable products when we analyze satellite orbital parameters or acquire digital elevation model by using only stereo-pair. Also, if there are noises, shadows, or clouds on the one of stereo pair, it is difficult to produce DEM(digital elevation model) on the area under analysis or to have good accuracy. In these case, it can be solved by systematic analysis of the multiple stereo images. This paper suggests the improvements on the accuracy of the digital elevation model by the developments of stereoscopic analysis techniques for the triplet of SPOT satellite images on the same area.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.31 no.11
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• pp.904-911
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• 2007

A measurement technique fur void fraction has been proposed using a time-resolved two-phase PIV system and the bubble dynamics has been investigated in gas-liquid two-phase flows. For the three-dimensional evaluation of the bubble information, both the images from the front and side views are simultaneously recorded into a high speed CCD camera by reflecting the side view image on a $45^{\circ}$ oriented mirror to be juxtaposed with the front view image. Then, a stereo-matching technique is applied to calculate the void fraction, bubble size and shape. To obtain the rising bubble velocities, the 2-frame PTV method was adopted. The present technique is applied to freely rising bubby flows in stagnant liquid. The results show that the increase of bubble flow rate gives rise to the increase of bubble size and rising velocity at first. If it goes over a certain level, the rising velocity becomes constant and the horizontal velocity grows bigger instead due to the obstruction of other bubbles.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.502-508
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• 2015

Stereo images have an advantage of calculating depth(distance) values which can not analyze from 2-D images. However, depth information obtained by stereo images has due to following reasons: it can be obtained by computation process; mismatching occurs when stereo matching is processing in occlusion which has an effect on accuracy of calculating depth information. Also, if global method is used for stereo matching, it needs a lot of computation. Therefore, this paper proposes the method obtaining disparity map which can reduce computation time and has higher accuracy than established method. Edge extraction which is image segmentation based on feature is used for improving accuracy and reducing computation time. Color K-Means method which is image segmentation based on color estimates correlation of objects in an image. And it extracts region of interest for applying Loopy Belief Propagation(LBP). For this, disparity map can be compensated by considering correlation of objects in the image. And it can reduce computation time because of calculating region of interest not all pixels. As a result, disparity map has more accurate and the proposed method reduces computation time.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_2
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• pp.587-597
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• 2007

This study determines matching size for digital elevation model (DEM) production according to land cover property from IKONOS Geo-level stereo image. We applied area based matching method using correlation coefficient of pixel brightness value between the two images. After matching line (where "matching line" implies straight line that is approximated to complex non-linear epipolar geometry) is established by exterior orientation parameters to minimize search area, the matching is carried out based on this line. The experiment is performed according to land cover property, which is divided off into four areas (water, urban land, forest land and agricultural land). In each of the test areas, matching size is selected using a correlation-coefficient image and parallax image. As the results, optimum matching size of the images was selected as $81{\times}81$ pixels window, $21{\times}21$ pixels window, $119{\times}119$ pixels window and $51{\times}51$ pixels window in the water area, urban land, forest land and agricultural land, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.10C
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• pp.787-797
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• 2008

In this paper, we propose a fast disparity estimation method considering temporal and spatial redundancy based on a dynamic programming for stereo matching. For the first step, the dynamic programming is performed to estimate disparity vectors with correlation between neighboring pixels in an image. Next, we efficiently compensate regions, which disparity vectors are not allocated, with neighboring disparity vectors assuming that disparity vectors in same object are quite similar. Moreover, in case of video sequence, we can decrease a complexity with temporal redundancy between neighboring frames. For performance comparison, we generate an intermediate-view image using the estimated disparity vector. Test results show that the proposed algorithm gives $0.8{\sim}2.4dB$-increased PSNR(peak signal to noise ratio) compared to a conventional block matching algorithm, and the proposed algorithm also gives approximately 0.1dB-increased PSNR and $48{\sim}68%$-lower complexity compared to the disparity estimation method based on general dynamic programming.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.102-113
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• 2014

This paper suggests a novel hardware architecture of a real-time rectification which is to remove vertical parallax of an image occurred in the pre-processing stage of stereo matching. As an off-line step, Matlab Toolbox which was designed by J.Y Bouguet, was used to calculate calibration parameter of the image. Then, based on the Heikkila and Silven's algorithm, rectification hardware was designed. At this point, to enhance the precision of the rectified image, floating-point unit was generated by using Xilinx Core Generator. And, we confirmed that proposed hardware design had higher precision compared to other designs while having the ability to do rectification in real-time.

• Journal of Broadcast Engineering
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• v.26 no.3
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• pp.283-295
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• 2021

This paper proposes a method for generating dense depth map using information of color images and depth map generated based on lidar based on self-organizing map. The proposed depth map upsampling method consists of an initial depth prediction step for an area that has not been acquired from LiDAR and an initial depth filtering step. In the initial depth prediction step, stereo matching is performed on two color images to predict an initial depth value. In the depth map filtering step, in order to reduce the error of the predicted initial depth value, a self-organizing map technique is performed on the predicted depth pixel by using the measured depth pixel around the predicted depth pixel. In the process of self-organization map, a weight is determined according to a difference between a distance between a predicted depth pixel and an measured depth pixel and a color value corresponding to each pixel. In this paper, we compared the proposed method with the bilateral filter and k-nearest neighbor widely used as a depth map upsampling method for performance comparison. Compared to the bilateral filter and the k-nearest neighbor, the proposed method reduced by about 6.4% and 8.6% in terms of MAE, and about 10.8% and 14.3% in terms of RMSE.