• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.799-806
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• 2021

The transforming process of point clouds with its local coordinates into a global coordinate is called registration. In contrast to the local registration which takes a long time to calculate and performs precision registration after initial rough positioning, the global registration calculates the corresponding points for registration and performs at once, so it is generally faster than the local registration, and can perform it regardless of the initial position. Among the global methods, the Fast Global Registration is one of the widely used methods due to its fast performance. However, lots of parameters should be set to increase the registration accuracy and speed. In this paper, after analyzing and experimenting the parameters and propose parameters that work effectively in actual registration. The proposed result will be helpful in setting the direction when it is necessary to use the Fast Global Registration method.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.671-674
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• 2000

In this paper, we present a robust global registration algorithm for multi-frame image mosaics. When we perform a pair-wise registration recovering a projective transformation between two consecutive frames, severe mis-registration among multiple frames, which are not consecutive, can be detected. It is because the concatenation of those pair-wise transformations leads to global alignment errors. To overcome those mis-registrations, we propose a new algorithm using multiple frames for constructing image mosaics. We use a graph to represent the temporal and spatial connectivity and show that global registration can be obtained through the search for an optimal path in the constructed graph. The definition of an adequate objective function characterizing the global registration provides a direct manipulation of the graph. In the presence of moving objects, especially large ones compared with low texture backgrounds, by using the likelihood ratio as the objective function, we can deal with some of the most challenging videos like basketball or soccer Moreover, the algorithm can be parallelized so it can be more efficiently implemented. Finally, we give some experimental results from real videos.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.2
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• pp.281-287
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• 2010

This study compares transformation algorithms for co-registration of terrestrial laser scan data. Pair-wise transformation which is used for transformation of scan data from more than two different view accumulates errors. ICP algorithm commonly used for co-registration between scan data needs initial geometry information. And it is difficult to co-register simultaneously because of too many control points when managing scan at the same time. Therefore, this study perform global registration technique using matching points. Matching points are extracted automatically from intensity image by SIFT and global registration is performed using GP analysis. There are advantages for operation speed, accuracy, automation in suggested global registration algorithm. Through the result from it, registration algorithms can be developed by considering accuracy and speed.

• Journal of Korea Multimedia Society
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• v.7 no.1
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• pp.106-112
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• 2004

Mobile IP is a solution for mobility on the global Internet. However, it causes service delay incase of frequent movement of mobile node and mobile users. Mobile IP regional registration is Proposed to reduce the service delay. In this paper, we introduce an optimal regional location management mechanism for Mobile IP that reduces the registration delay. The movement of mobile node is described by a discrete analytical model. The proposed model explains analytically average packet rate and registration cost as the size of regional networks with mobility of mobile node.

• Journal of the Korea Society of Computer and Information
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• v.22 no.8
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• pp.15-23
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• 2017

In this paper, the block reweighting and global offset initialization methods are proposed to complement the improved IRLMS algorithm which is the effective algorithm in registration based SBNUC algorithm. Proposed block weighting method reweights the error map whose abnormal data are excluded. The global offset initialization method compensates the global nonuniformity initially. The ordinary registration based SBNUC algorithm is hard to compensate global nonuniformity because of low scene motion. We employ the proposed methods to improved IRLMS algorithm, and apply it to real-world infrared raw image stream. The result shows that new implementation provides 3.5~4.0dB higher PSNR and convergence speed 1.5 faster then the improved IRLMS algorithm.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.4
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• pp.524-531
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• 2009

In this paper, we propose an automatic image registration method for multi-sensor image fusion such as visible and infrared images. The registration is achieved by finding corresponding feature points in both input images. In general, the global statistical correlation is not guaranteed between multi-sensor images, which bring out difficulties on the image registration for multi-sensor images. To cope with this problem, mutual information is adopted to measure correspondence of features and to select faithful points. An update algorithm for projective transform is also proposed. Experimental results show that the proposed method provides robust and accurate registration results.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1901-1910
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• 2022

Arriving in the new space age, securing technology for fusion application of KOMPSAT-3·3A and global satellite images is becoming more important. In general, multi-sensor satellite images have relative geometric errors due to various external factors at the time of acquisition, degrading the quality of the satellite image outputs. Therefore, we propose a fine-image registration methodology to minimize the relative geometric error between KOMPSAT-3·3A and global satellite images. After selecting the overlapping area between the KOMPSAT-3·3A and foreign satellite images, the spatial resolution between the two images is unified. Subsequently, tie-points are extracted using a hybrid matching method in which feature- and area-based matching methods are combined. Then, fine-image registration is performed through iterative registration based on pyramid images. To evaluate the performance and accuracy of the proposed method, we used KOMPSAT-3·3A, Sentinel-2A, and PlanetScope satellite images acquired over Daejeon city, South Korea. As a result, the average RMSE of the accuracy of the proposed method was derived as 1.2 and 3.59 pixels in Sentinel-2A and PlanetScope images, respectively. Consequently, it is considered that fine-image registration between multi-sensor satellite images can be effectively performed using the proposed method.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.5
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• pp.311-317
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• 2015

The aim of this study is to develop a 3D registration algorithm for positron emission tomography/computed tomography (PET/CT) and magnetic resonance (MR) images acquired from independent PET/CT and MR imaging systems. Combined PET/CT images provide anatomic and functional information, and MR images have high resolution for soft tissue. With the registration technique, the strengths of each modality image can be combined to achieve higher performance in diagnosis and radiotherapy planning. The proposed method consists of two stages: normalized mutual information (NMI)-based global matching and independent component analysis (ICA)-based refinement. In global matching, the field of view of the CT and MR images are adjusted to the same size in the preprocessing step. Then, the target image is geometrically transformed, and the similarities between the two images are measured with NMI. The optimization step updates the transformation parameters to efficiently find the best matched parameter set. In the refinement stage, ICA planes from the windowed image slices are extracted and the similarity between the images is measured to determine the transformation parameters of the control points. B-spline. based freeform deformation is performed for the geometric transformation. The results show good agreement between PET/CT and MR images.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.8
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• pp.3687-3696
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• 2016

Cancer registration, an important component of cancer surveillance, is essential to a unified, scientific and public health approach to cancer prevention and control. India has one of the highest cancer incidence and mortality rates in the world. A good surveillance system in the form of cancer registries is important for planning and evaluating cancer-control activities. Cancer registration in India was initiated in 1964 and expanded since 1982, through initiation of the National Cancer Registry Program (NCRP) by the Indian Council of Medical Research. NCRP currently has twenty-six population based registries and seven hospital based registries. Yet, Indian cancer registries, mostly in urban areas, cover less than 15% of the population. Other potential concerns about some Indian registries include accuracy and detail of information on cancer diagnosis, and timeliness in updating the registry databases. It is also important that necessary data collection related quality assurance measures be undertaken rigorously by the registries to ensure reliable and valid information availability. This paper reviews the current status of cancer registration in India and discusses some of the important pitfalls and issues related to cancer registration. Cancer registration in India should be complemented with a nationwide effort to foster systematic investigations of cancer patterns and trends by states, regions and sub populations and allow a continuous cycle of measurement, communication and action.

• Korean Journal of Remote Sensing
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• v.30 no.6
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• pp.787-795
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• 2014

This paper presents an image registration using Triangulation-based Local Transformation (TLT) applied to the remaining matched points after elimination of the matched points with gross error. The corners extracted using geometric mean-based corner detector are matched using Pearson's correlation coefficient and then accepted as initial matched points only when they satisfy the Left-Right Consistency (LRC) check. We finally accept the remaining matched points whose RANdom SAmple Consensus (RANSAC)-based global transformation (RGT) errors are smaller than a predefined outlier threshold. After Delaunay triangulated irregular networks (TINs) are created using the final matched points on reference and sensed images, respectively, affine transformation is applied to every corresponding triangle and then all the inner pixels of the triangles on the sensed image are transformed to the reference image coordinate. The proposed algorithm was tested using KOMPSAT-2 images and the results showed higher image registration accuracy than the RANSAC-based global transformation.