• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.47-51
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• 2005

Using satellite images, an optimal solution to water motion has been presented in this study. Since temperature patterns are suitable tracers in water motion, Sea Surface Temperature (SST) images of Caspian Sea taken by MODIS sensor on board Terra satellite have been used in this study. Two daily SST images with 24 hours time interval are used as input data. Computation of templates correspondence between pairs of images is crucial within motion algorithms using non-rigid body objects. Image matching methods have been applied to estimate water body motion within the two SST images. The least squares matching technique, as a flexible technique for most data matching problems, offers an optimal spatial solution for the motion estimation. The algorithm allows for simultaneous local radiometric correction and local geometrical image orientation estimation. Actually, the correspondence between the two image templates is modeled both geometrically and radiometrically. Geometric component of the model includes six geometric transformation parameters and radiometric component of the model includes two radiometric transformation parameters. Using the algorithm, the parameters are automatically corrected, optimized and assessed iteratively by the least squares algorithm. The method used in this study, has presented more efficient and robust solution compared to the traditional motion estimation schemes.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.34-39
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• 2002

Road information is very important for topographic mapping, transportation application, urban planning and other related application fields. Therefore, automatic detection of road networks from spatial imagery, such as aerial photos and satellite imagery can play a central role in road information acquisition. In this paper, we use least squares correlation matching alone for road center tracking and show that it works. We assumed that (bright) road centerlines would be visible in the image. We further assumed that within a same road segment, there would be only small differences in brightness values. This algorithm works by defining a template around a user-given input point, which shall lie on a road centerline, and then by matching the template against the image along the orientation of the road under consideration. Once matching succeeds, new match proceeds by shifting a matched target window further along road orientation at the target window. By repeating the process above, we obtain a series of points, which lie on a road centerline successively. A 1m resolution IKONOS images over Seoul and Daejeon were used for tests. The results showed that this algorithm could extract road centerlines in any orientation and help in fast and exact he ad-up digitization/vectorization of cartographic images.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.182-187
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• 1999

Stereo matching is one of the most crucial parts in DEM generation. Naive stereo matching algorithms often create many holes and blunders in a DEM and therefore a carefully designed strategy must be employed to guide stereo matching algorithms to produce “good” 3D information. In this paper, we describe one such a strategy designed by the use of scene geometry, in particular, the epipolarity for generation of a DEM from linear pushbroom images. The epipolarity for perspective images is a well-known property, i.e., in a stereo image pair, a point in the reference image will map to a line in the search image uniquely defined by sensor models of the image pair. This concept has been utilized in stereo matching by applying epipolar resampling prior to matching. However, the epipolar matching for linear pushbroom images is rather complicated. It was found that the epipolarity can only be described by a Hyperbola- shaped curve and that epipolar resampling cannot be applied to linear pushbroom images. Instead, we have developed an algorithm of incorporating such epipolarity directly in least squares correlation matching. Experiments showed that this approach could improve the quality of a DEM.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.46-48
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• 2004

This paper presents a simple method of rotational and translational motion estimation for digital image stabilization. The scheme first computes the rotation center by taking least squares of selected local velocity vectors, and the rotational angle is found from special subset of motion vectors. And then translational motion can be estimated by the relation among movement of rotation center, rotation angle and translation movement. To show the effectiveness of our approach, the synthetic images are evaluated, resulting in better performance.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.243-243
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• 2000

This paper introduces a visual inspecting and monitoring system based on an image processing technique. We propose an image processing method for analyzing landslide movement in real time. The method adopts Laplacian of Gaussian operator to extract linear features for the captured images and uses a linear matching algorithm to distinguish the matching error for those features. When the algorithm is processed, motion parameters such as displacement area and its direction are computed. Once movement is recognized, displacements are estimated graphically with statistical amount in the image plane. The simulation results are shown us to verify the effectiveness of the developed method.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.269-277
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• 2006

We propose a new expert system for recovering the broken fragments of relics into an original form using computer graphics and image processing. This paper presents a system with an application to tombstones objects of flat plane with letters carved in for assembling the fragments by placing their respective fragments in the right position. The matching process contains three sub-processes: aligning the front and letters of an object, identifying the matching directions, and determining the detailed matching positions. We apply least squares fitting, vector inner product, and geometric and RGB errors to the matching process. It turned out that 2-D translations via fragments-alignment enable us to save the computational load significantly. Based on experimental results from the damaged cultural fragments, the performance of the proposed method is illustrated.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.4
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• pp.321-330
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• 1999

Automation of observation and positioning of fiducial marks is made possible with the application of image matching technique, developed through the cooperative research effort of computer vision and digital photogrammetry. The major problem in such automation effort is to minimize the computing time and to increase the positional accuracy. Except for scanning and ground control surveying, the interior orientation process was automated in this study, through the development of an algorithm which applies the image matching and image processing techniques. The developed system was applied to close-range photogrammetry and the analysis of the results showed 54% improvement in processing time. For fiducial mark observation during interior orientation, the Laplacian of Gaussian transformation and the Hough transformation were applied to determine the accurate position of the center point, and the correlation matching and the least squares matching method were then applied to improve the accuracy of automated observation of fiducial marks. Image pyramid concept was applied to reduce the computing time of automated positioning of fiducial mark.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.7
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• pp.611-617
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• 2004

This paper presents a simple method of rotational motion estimation and correction for roll axis stabilization of an image. The scheme first computes the rotation center by taking least squares of selected local velocity vectors, and the rotational angle is found from special subset of motion vectors. Roll motion correction is then performed by the nearest neighbor interpolation technique. To show the effectiveness of our approach, the synthetic and real images are evaluated, resulting in better performance than the previous ones.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.4
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• pp.241-248
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• 2017

GCPs (Ground Control Points) are needed to correct the DEM (Digital Elevation Model) produced from high-resolution satellite images and the RPC (Rational Polynomial Coefficient). It is difficult to acquire the GCPs through field surveys such as GPS surveys and to read the image coordinates corresponding to the GCPs. In addition, GCPs cannot cover the entire image of the test site, and the RPC correction results may be influenced by the arrangement and distribution of the GCPs in the image. Therefore, a new method for the RPC correction is needed. In this study, an LHD (Least-squares Height Difference) DEM matching method was applied using previous DEMs: SRTM DEM, digital map DEM, and corrected IKONOS DEM. This was carried out to correct the DEM produced from KOMPSAT-3 satellite images and the provided RPC without GCPs. The IKONOS DEM had the highest accuracy, and the height accuracy was about ${\pm}3m$ RMSE in a mountainous area and about ${\pm}2m$ RMSE in an area with only low heights.

• Korean Journal of Remote Sensing
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• v.9 no.2
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• pp.7-19
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• 1993

In stereo vision, when we use two parallel axis images, small portion of object is contained and B/H(Base-line to Height) ratio is limited due to the size of object and depth information is inaccurate. To overcome these difficulities we take a non-parallel axis image which is rotated $\theta$ about y-axis and match other parallel-axis image. Epipolar lines of non-parallel axis image are not same as those of parallel-axis image and we can't match these two images directly. In this paper, we transform the non-parallel axis image geometrically with camera parameters, whose epipolar lines are alingned parallel. NCC(Normalized Cross Correlation) is used as match measure, area-based matching technique is used find correspondence and 9$\times$9 window size is used, which is chosen experimentally. Focal length which is necessary to get depth information of given object is calculated with least-squares method by CCD camera characteristics and lenz property. Finally, we select 30 test points from given object whose elevation is varied to 150 mm, calculate heights and know that height RMS error is 7.9 mm.