• 한국CDE학회논문집
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• 제14권2호
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• pp.129-136
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• 2009

Generally, curved surfaces of ship hull are deformed by flame bending (line heating), multi-press forming, and die-less forming method. The forming methods generate the required in-plane/bending strain or displacement on the flat plate to make the curved surface. Multi-press forming imposes the forced displacements on the flat plate by controlling the position of each pressing points based upon the shape difference between the unfolded flat plate and the curved object shape. The flat plate has been obtained from the unfolding system that is independent of the ship CAD. Apparently, the curved surface and the unfolded-flat surface are expressed by different coordinate systems. Therefore, one of the issues is to find a registration of the unfolded surface and the curved shape for the purpose of minimum amount of forming works by comparing the two surfaces. This paper presents an efficient algorithm to get an optimized registration of two different surfaces in the multi-press forming of ship hull plate forming. The algorithm is based upon the ICP (Iterative Closest Point) algorithm. The algorithm consists of two iterative procedures including a transformation matrix and the closest points to minimize the distance between the unfolded surface and curved surfaces. Thereby the algorithm allows the minimized forming works in ship-hull forming.

• 전자공학회논문지
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• 제53권1호
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• pp.118-125
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• 2016

본 논문에서는 얼굴 영역 수술용 네비게이션을 위한 스테레오 비전과 CT 영상을 이용하여 환자-영상 간 정합(Image to patient registration) 알고리즘의 성능을 평가한다. 환자 영상 간 정합은 스테레오 비전 영상의 특징점 추출과 이를 통한 3차원 좌표 계산, 3차원 좌표와 3차원 CT 영상과의 정합 과정을 거친다. 스테레오 비전 영상에서 3가지 얼굴 특징점 추출 방법과 3가지 정합 방법을 사용하여 생성될 수 있는 5가지 조합 중 정합 정확도가 가장 높은 방법을 평가한다. 또한 머리의 회전에 따라 환자 영상 간 정합의 정확도를 비교한다. 실험을 통해 머리의 회전 각도가 약 20도의 범위 내에서 Active Appearance Model과 Pseudo Inverse Matching을 사용한 정합의 정확도가 가장 높았으며, 각도가 20도 이상일 경우 Speeded Up Robust Features와 Iterative Closest Point를 사용하였을 때 정합 정확도가 높았다. 이 결과를 통해 회전각도가 20도 범위 내에서는 Active Appearance Model과 Pseudo Inverse Matching 방법을 사용하고, 20도 이상의 경우 Speeded Up Robust Features와 Iterative Closest Point를 이용하는 것이 정합의 오차를 줄일 수 있다.

• 한국CDE학회논문집
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• 제21권2호
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• pp.151-158
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• 2016

It is important to analyze dimensional errors occurring during shipbuilding process. A ship is constructed by assembling blocks and installing outfits in assembled ship structure. Blocks and outfits have a main direction that has greater importance than other directions from the view point of dimensional error. Therefore, a main direction should have a greater weighting factor than other directions in order to achieve meaningful inspection results. In this paper, a modified point registration method based on iterative closest point (ICP) is proposed. In this method, a user determines one or two main directions among x, y, and z directions, and then each main direction is made to have a greater weighting factor than other directions. For points registration, mapping between measured points and design points are performed by the modified ICP in which weighting factor assigned to each main direction is considered.

• 한국BIM학회 논문집
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• 제11권4호
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• pp.1-9
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• 2021

Comparing to general scanning data, the 3D digital map for large construction sites and complex buildings consists of millions of points. The large construction site needs to be scanned multiple times by drone photogrammetry or terrestrial laser scanner (TLS) survey. The scanned point cloud data are required to be registrated with high resolution and high point density. Unlike the registration of 2D data, the matrix of translation and rotation are used for registration of 3D point cloud data. Archiving high accuracy with 3D point cloud data is not easy due to 3D Cartesian coordinate system. Therefore, in this study, iterative closest point (ICP) registration method for improve accuracy of 3D digital map was employed by different overlap ratio on 3D digital maps. This study conducted the accuracy test using different overlap ratios of two digital maps from 10% to 100%. The results of the accuracy test presented the optimal overlap ratios for an ICP registration method on digital maps.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권9호
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• pp.401-410
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• 2016

3차원 정합이란 다시점에서 획득한 3차원 점군들을 정렬하는 기술로써 지난 수십 년간 많은 연구가 진행되고 있는 분야이다. 이러한 3차원 정합은 ICP(Iterative Closest Point) 알고리즘을 시작으로 많은 변형 ICP가 소개되고 있다. 하지만 ICP 계열의 알고리즘들은 최근접점을 대응점으로 간주하여 알고리즘을 수행한다. 그렇기 때문에 3차원 점군의 초기 오차가 큰 경우 정확한 대응점 탐색에 실패할 수 있다. 이런 문제점을 해결하기 위해 본 논문에서는 색상과 3차원 거리가 융합된 6차원 거리와 색상분포 유사도를 이용한다. 더 나아가 색상 분할 기반 적응형 샘플링을 이용하여 알고리즘 연산 속도를 감소시키고 성능을 향상시키는 것을 목표로 한다. 마지막으로 실험을 통해 기존의 방법과 본 논문에서 제안하는 방법의 성능을 비교한다.

• 한국측량학회지
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• 제38권1호
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• pp.35-41
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• 2020

3D 레이저 스캐너는 대상물에 대한 많은 양의 데이터를 빠른 시간 내에 취득할 수 있는 효과적인 방법으로 최근 측량, 변위측정, 대상물의 3차원 데이터 생성, 실내공간정보 구축, BIM (Building Information Model) 등 다양한 분야에 활용되고 있다. 3D 레이저 스캐너를 통해 취득되는 점군데이터의 활용을 위해서는 정합과정을 거쳐 많은 측점에서 취득한 데이터를 통일된 좌표체계를 가진 하나의 데이터로 만드는 과정이 필요하다. 따라서 정합 방법에 따른 점군데이터의 정확도에 대한 분석적 연구가 필요하다 이에 본 연구에서는 3D 레이저 스캐너를 통해 취득되는 점군데이터의 정합방법에 따른 정확도를 분석하고자 하였다. 3D 레이저 스캐너를 통해 연구대상지의 점군데이터를 취득하고, 자료처리를 통해 ICP (Iterative Closest Point) 와 형상정합 방법에 의해 점군데이터를 정합하였으며, 토털스테이션 측량성과와 비교하여 정확도를 분석하였다. 정확도 평가 결과 ICP와 형상정합 방법은 각각 토털스테이션 성과와 0.002~0.005m, 0.002~0.009m의 차이를 나타내었다. 각각의 정합 방법은 실험결과 모두 0.01m 미만의 편차를 나타내어 1:1,000 수치지형도의 허용정확도를 만족하였으며, ICP 및 형상정합을 이용한 점군데이터의 정합이 공간정보 구축에 충분히 활용 가능함을 제시하였다. 향후 형상정합 방법에 의한 점군데이터의 정합은 3D 레이저 스캐너를 활용한 공간정보 구축 과정에서 타겟의 설치를 줄임으로써 생산성 향상에 기여할 것이다.

• International Journal of CAD/CAM
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• 제9권1호
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• pp.37-46
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• 2010

Existing methods for the registration of blurred images are efficient for the artificially blurred images or a planar registration, but not suitable for the naturally blurred images existing in the real image mosaic process. In this paper, we attempt to resolve this problem and propose a method for a distortion-free stitching of naturally blurred images for image mosaic. It adopts a multi-resolution and robust feature based inter-layer mosaic together. In each layer, Harris corner detector is chosen to effectively detect features and RANSAC is used to find reliable matches for further calibration as well as an initial homography as the initial motion of next layer. Simplex and subspace trust region methods are used consequently to estimate the stable focal length and rotation matrix through the transformation property of feature matches. In order to stitch multiple images together, an iterative registration strategy is also adopted to estimate the focal length of each image. Experimental results demonstrate the performance of the proposed method.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1996년도 추계학술대회
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• pp.155-156
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• 1996

The intercommissural(AC-PC) line is automatically detected for HR and PET images. With the detected AC-PC lines from MR and PET images, fully non-iterative automatic co- registration is accomplished. It provides a new automated method for image co-registration.

• 대한의용생체공학회:의공학회지
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• 제30권1호
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• pp.34-40
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• 2009

Transforms including translation and rotation are required for registering two or more images. In medical applications, different registration methods have been applied depending on the structures: for rigid bodies such as bone structures, affine transformation was widely used. In most previous research, a single transform was used for registering the whole images, which resulted in low registration accuracy especially when the degree of deformation was high between two images. In this paper, a novel registration method is introduced which is based image sub-division and bilinear interpolation of transformations. The proposed method enhanced the registration accuracy by 40% comparing with Trimmed ICP for registering color and MRI images.

• 한국CDE학회논문집
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• 제5권3호
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• pp.285-292
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• 2000

A new method for the 3D automatic registration of the multiple range images has been developed for the 3D scanners(non-contact coordinates measurement systems). In the existing methods, the user usually has to input more than 3 pairs of corresponding points for the iterative registration process. The major difficulty of the existing systems lies in that the input corresponding points must be selected very carefully because the optimal searching process and the registration results mostly depend upon the accuracy of the selected points. In the proposed method, this kind of difficulty is greatly mitigated even though it needs only 2 pairs of the corresponding input points. Several registration examples on the 3D measured data have been presented and discussed with the introduction to the proposed algorithm.