• 한국측량학회지
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• 제28권4호
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• pp.463-469
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• 2010

화성의 고도정보를 제공하는 MOLA 센서는 화성전역에 대한 데이터를 제공하지 못하므로, 수치표고모형을 만들기 위해서는 MOC-NA영상을 이용한 영상정합이 수행되어야만 한다. 그러나 특색(feature)이 적고 명암대비가 낮은 화성영상의 특성상, 자동 영상정합은 어려운 실정이다. 본 논문은 MOC-NA 영상에 대하여 영역기준 영상정합에 기반한 반 자동 영상정합의 알고리즘을 다룬다. 공액점을 나타내는 시드(seed)포인트 들이 수동으로 스테레오 영상에 추가되고 이를 바탕으로 특징점들이 자동으로 삽입된다. 각 영상의 특징점들은 서로의 초기 공액점으로 사용되며, 영역기준 영상정합으로 정제된다. 영상정합의 과정 중 정합에 실패한 점들은 초기 공액점의 위치를 정합에 성공한 주변의 여섯 점들을 이용하여 재 계산한 후 정제된다. 타깃영상과 검색영상의 역할을 바꾸어 수행한 영상정합의 질적 평가 결과, 97.3%의 점들이 한 화소 이하의 절대거리를 나타내었다.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2007년도 춘계학술발표회 논문집
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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.

• 전자공학회논문지B
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• 제29B권4호
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• pp.48-57
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• 1992

This paper addresses the estimation of the shift vector from aerial image sequences. The conventional feature-based and area-based matching methods are simulated for determining the suitable image registration scheme. Computer simulations show that the feature-based matching schemes based on the co-occurrence matrix, autoregressive model, and edge information do not give a reliable matching for aerial image sequences which do not have a suitable statistical model or significant features. In area-based matching methods we try various similarity functions for a matching measure and discuss the factors determining the matching accuracy. To reduce the estimation error of the shift vector we propose the reference window selection scheme. We also discuss the performance of the proposed algorithm based on the simulation results.

• 대한공간정보학회지
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• 제4권1호
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• pp.93-102
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• 1996

수치표고모델(DEM, Digital Elevation Model)을 컴퓨터를 이용하여 자동으로 생성할 때 입체영상매칭(stereo matching) 연산은 많은 수행시간이 소요된다. 매칭연산은 일반적으로 상관계수(correlation)에 의한 방법이 사용되고 있으며, 매칭점 분포가 균등한 지역기반방식(area-based method)이 주로 이용되고 있다. 본 논문에서는 지형을 식별하여 매칭연산에서 검색영역(search area)과 기준윈도우(mask window)의 크기를 조정하여 효율적인 매칭을 수행하는 방안을 제시하였다. 영상을 분할하기 위하여 경계보호평활화 필터(edge-preserving smoothing filter)를 사용하여 전처리를 수행하였으며, 필터를 거친 영상에 대해서 영역성장 알고리듬을 적용하였다. 분할된 영역은 MRF(Markov Random Field) 모델에 의한 식별과정을 통하여 산악, 평야, 수계지역으로 식별된다. 영상매칭은 예비시차(predicted parallex) 계산과 상세매칭(fine matching)의 두 단계를 거치며, 예비시차를 이용하여 상세매칭단계에서 검색영역의 위치를 결정한다. 검색영역과 기준윈도우의 크기는 화소에 대한 지형식별정보에 의해 결정된다. 주변화소와 시차가 유사한 평야지역과 수계지역의 검색영역을 축소함으로서 매칭연산시간을 단축시켰다. 대전-금산지역의 $10km{\times}10km(1024{\times}1024)$ 영상을 4개 사용하여 실험한 결과 지형식별정보를 이용하지 않았을 경우보다 영상매칭 수행시간이 $25%{\times}35%$정도 단축시킬 수 있음을 보였다.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권4호
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• pp.277-285
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• 2004

We present a new hybrid stereo matching technique in terms of the co-operation of area-based stereo and feature-based stereo. The core of our technique is that feature matching is carried out by the reference of the disparity evaluated by area-based stereo. Since the reference of the disparity can significantly reduce the number of feature matching combinations, feature matching error can be drastically minimized. One requirement of the disparity to be referenced is that it should be reliable to be used in feature matching. To measure the reliability of the disparity, in this paper, we employ the self-consistency of the disunity Our suggested technique is applied to the detection of 3D line segments by 2D line matching using our hybrid stereo matching, which can be efficiently utilized in the generation of the rooftop model from urban imagery. We carry out the experiments on our hybrid stereo matching scheme. We generate synthetic images by photo-realistic simulation on Avenches data set of Ascona aerial images. Experimental results indicate that the extracted 3D line segments have an average error of 0.5m and verify our proposed scheme. In order to apply our method to the generation of 3D model in urban imagery, we carry out Preliminary experiments for rooftop generation. Since occlusions are occurred around the outlines of buildings, we experimentally suggested multi-image hybrid stereo system, based on the fusion of 3D line segments. In terms of the simple domain-specific 3D grouping scheme, we notice that an accurate 3D rooftop model can be generated. In this context, we expect that an extended 3D grouping scheme using our hybrid technique can be efficiently applied to the construction of 3D models with more general types of building rooftops.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2004년도 춘계학술발표회논문집
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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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.594-597
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• 2006

Image co-registration is the process of overlaying two images of the same scene. One of which is a reference image, while the other (sensed image) is geometrically transformed to the one. Numerous methods were developed for the automated image co-registration and it is known as a time-consuming and/or computation-intensive procedure. In order to improve efficiency and effectiveness of the co-registration of satellite imagery, this paper proposes a pre-qualified area matching, which is composed of feature extraction with Laplacian filter and area matching algorithm using correlation coefficient. Moreover, to improve the accuracy of co-registration, the outliers in the initial matching point should be removed. For this, two outlier detection techniques of studentized residual and modified RANSAC algorithm are used in this study. Three pairs of Landsat images were used for performance test, and the results were compared and evaluated in terms of robustness and efficiency.

• 대한원격탐사학회지
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• 제22권5호
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• pp.439-443
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• 2006

Image co-registration is the process of overlaying two images of the same scene. One of which is a reference image, while the other (sensed image) is geometrically transformed to the one. Numerous methods were developed for the automated image co-registration and it is known as a timeconsuming and/or computation-intensive procedure. In order to improve efficiency and effectiveness of the co-registration of satellite imagery, this paper proposes a pre-qualified area matching, which is composed of feature extraction with Laplacian filter and area matching algorithm using correlation coefficient. Moreover, to improve the accuracy of co-registration, the outliers in the initial matching point should be removed. For this, two outlier detection techniques of studentized residual and modified RANSAC algorithm are used in this study. Three pairs of Landsat images were used for performance test, and the results were compared and evaluated in terms of robustness and efficiency.

• 대한공간정보학회지
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• 제3권2호
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• pp.213-220
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• 1995

차량 항법 시스템에서 맵 매칭(map matching) 방법은 데드 레코닝(dead reckoning) 기법으로 구한 차량의 위치가 지도상의 도로위에 일치하지 않는 경우에 이를 도로위에 매칭(matching) 시키기 위한 방법이다. 본 논문에서는 교차로 부근에서 차량의 회전을 감지함으로서 센서 및 수치지도자료에 기인하는 위치오류를 보정할 수 있는 새로운 맵 매칭 알고리즘을 제안하였다. 제안된 알고리즘은 진행거리의 상대오차와 도로거리의 절대오차를 이용하여 교차로 지역의 범위를 설정하고, 설정된 지역에서 차량의 진행각을 감지하여 교차로에 연결된 각 도로에 대한 출발지점을 알아내고, 회전각과 진행각을 비교하여 새로운 위치를 알아내는 방법이다. 제안된 알고리즘의 효율성을 검증하기 위하여 거리센서로 속도센서를, 방향센서로서 광 자이로를 이용하여 휴대용 컴퓨터에 시스템을 구성하고, 대전지역을 시범지역으로 선정하여 알고리즘을 실험한 결과 그 유효성을 입증할 수 있었다.

• 전자공학회논문지B
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• 제33B권2호
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• pp.77-87
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• 1996

Existing area-based stereo matching algorithms utilize a single rectangular correlation area for computing cross-correlation between corresponding points in stereo images, and compute disparity by finding the peak in the vicinity of depth discontinuity, since, because of inconstnat disparities around discontinuities, the cross-correlation becomes low in such area. Inthis paper, a new area-based matching strategy is proposed exploiting multiple directional correlation masks instead of a single one. The proposed technique computes multiple cross-covariance functions using each oriented mask. Peaks are detected from each covariance function and the disparity is computed by choosing the location with the highest covariance value. Proposed approach can also be applied to compute disparity gradients without obtaining dense depth data. A number of examples are presented using synthetic and natural stereo images.