• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.423-425
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• 2003

In this paper, we propose a new scheme for matching specified areas in a satellite image, which is a very efficient method because it can be effectively applied to images that have various features. These features may include different spatial resolution and brightness; sometimes they may different geometrical property. The proposed method can be applied to some application fields such as mosaicing of satellite imagery, GCP matching.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.171-176
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• 2004

Image matching is fundamental process in photogrammetry and computer vision to identify and to measure corresponding features on the multiple images. Uniqueness of the matching entities and robustness of the algorithm are the key issues that have influence on quality of the matching result. The optimal solution could be obtained by utilizing appropriate matching entities in the first place. In this study, candidate matching points were extracted by interest operator, and an area-based matching method was applied with characteristics of the gray value distribution as the matching entities. The characteristic information is based on the concept of "intrinsic image" (or parameter image). The information was utilized as additional and/or complementary matching entities. Matching on interest points with the characteristic information resulted in high quality of matching because matching windows were created with surrounding pixels of the interest points that contain distinct and unique features. The experiment shows that matching quality and reliability increase by exploiting interest operator, and the characteristic information has potential to be matching entity.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1175-1180
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• 1991

The purpose of this paper is to improve the matching accuracy in identifying corresponding points in the area-based matching for the processing of stereo vision. For the selection of window size, a new method is proposed based on frequency domain analysis. The effectiveness of the proposed method is confirmed through a series of experiments. To overcome disproportionate distortion in stereo image pair, a new matching method using the warped window is also proposed. In the algorithm, the window is warped according to imaging geometry. Experiments on a synthetic image show that the matching accuracy is improved by 14.1% and 4.2% over the rectangular window method and image warping method each.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.73-82
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• 2010

TERCOM(TERrain COntour Matching), which is the one of the Terrain Referenced Navigation and used in the cruise missile navigation system, is still under development. In this study, the TERCOM based on area-based matching algorithm and extended Kalman filter is analysed through simulation. In area-based matching, the mean square difference (MSD) and cross-correlation matching algorithms are applied. The simulation supposes that the barometric altimeter, radar altimeter and SRTM DTM loaded on board. Also, it navigates along the square track for 545 seconds with the velocity of 1000km per hour. The MSD and cross-correlation matching algorithms show the standard deviation of position error of 99.6m and 34.3m, respectively. The correlation matching algorithm is appeared to be less sensitive than the MSD algorithm to the topographic undulation and the position accuracy of the both algorithms is extremely depends on the terrain. Therefore, it is necessary to develop an algorithm that is more sensitive to less terrain undulation for reliable terrain referenced navigation. Furthermore, studies on the determination of proper matching window size in long-term flight and the determination of the best terrain database resolution needed by the flight velocity and area should be conducted.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1591-1602
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• 1995

A hybrid approach which includes edge- and region-based methods is considered. The modified non-linear Laplacian(MNL) filter is used for feature extraction. The matching algorithm has three steps which are edge, signed region, and residual region matching. At first, the edge points are matched using the sign and direction of edges. Then, the disparity is propagated from edge to inside region. A variable window is used to consider the local method which give accurate matched points and area-based method which can obtain full-resolution disparity map. In addition, a new relaxation algorithm for considering matching possibility derived from normalized error and regional continuity constraint is proposed to reduce the mismatched points. By the result of simulation for various images, this algorithm is insensitive to noise and gives full- resolution disparity map.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.201-205
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• 2007

In the post-Cold War era, acquisition technique of high-resolution satellite imagery (HRSI) has begun to commercialize. IKONOS-2 satellite imaging data is supplied for the first time in the 21st century. Many researchers testified mapping possibility of the HRSI data instead of aerial photography. It is easy to renew and automate a topographical map because HRSI not only can be more taken widely and periodically than aerial photography, but also can be directly supplied as digital image. In this study matching size of IKONOS Geo-level stereo image is presented lot production of digital elevation model (DEM). 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 (EOPs) to minimize search area, the matching is tarried out based on this line. The experiment on matching size 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, window size for the highest correlation coefficient is selected as propel size for matching. As the results of experiment, the proper size was selected as $123{\times}123$ pixels window, $13{\times}13$ pixels window, $129{\times}129$ pixels window and $81{\times}81$ pixels window in the water area, urban land, forest land and agricultural land, respectively. Of course, determination of the matching size by the correlation coefficient may be not absolute appraisal method. Optimum matching size using the geometric accuracy therefore, will be presented by the further work.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.209-214
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• 2004

This paper presents a RFM-based image matching algorithm which put constraints on the search space through the object-space approach. Also, the detail procedure of generating 3-D surface models from the RFM is introduced as an end-user point of view. The proposed algorithm provides the PML (Piecewise Matching Line) for image matching and reduces the search space to within the confined line-shape area.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.256-267
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• 2017

An active research area in computer vision, stereo matching is aimed at obtaining three-dimensional (3D) information from a stereo image pair captured by a stereo camera. To extract accurate 3D information, a number of studies have examined stereo matching algorithms that employ adaptive support weight. Among them, the adaptive census transform (ACT) algorithm has yielded a relatively strong matching capability. The drawbacks of the ACT, however, are that it produces low matching accuracy at the border of an object and is vulnerable to noise. To mitigate these drawbacks, this paper proposes and analyzes the features of an improved stereo matching algorithm that not only enhances matching accuracy but also is also robust to noise. The proposed algorithm, based on the ACT, adopts the truncated absolute difference and the multiple sparse windows method. The experimental results show that compared to the ACT, the proposed algorithm reduces the average error rate of depth maps on Middlebury dataset images by as much as 2% and that is has a strong robustness to noise.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1467-1469
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• 2003

Image matching is fundamental process to identify conjugate points on the stereo images. However, standard methods or general solutions for matching problem have not been found yet, in spite of long history. Quality of the matching basically depends on uniqueness of the matching entity and robustness of the algorithm. In this study, conjugate points were extracted by implementing interest operator, then area based matching method was applied to the topographical characteristics of the gray value as the matching entities. The matching entities were utilized based on the concept of the intrinsic image.