• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6C
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• pp.349-354
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• 2011

Passive millimeter wave imaging has the capability of detecting concealed objects under clothing. Also, passive millimeter imaging can obtain interpretable images under low visibility conditions like rain, fog, smoke, and dust. However, the image quality is often degraded due to low spatial resolution, low signal level, and low temperature resolution. This paper addresses image registration and fusion between passive millimeter images and visual images. The goal of this study is to combine and visualize two different types of information together: human subject's identity and concealed objects. The image registration process is composed of body boundary detection and an affine transform maximizing cross-correlation coefficients of two edge images. The image fusion process comprises three stages: discrete wavelet transform for image decomposition, a fusion rule for merging the coefficients, and the inverse transform for image synthesis. In the experiments, various types of metallic and non-metallic objects such as a knife, gel or liquid type beauty aids and a phone are detected by passive millimeter wave imaging. The registration and fusion process can visualize the meaningful information from two different types of sensors.

• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.1960-1969
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• 2016

In this paper, we propose an automatic hierarchical organ segmentation method on abdominal CT images. First, similar atlases are selected using bone-based similarity registration and similarity of liver, kidney, and pancreas area. Second, each abdominal organ is roughly segmented using image-based similarity registration and intensity-based locally weighted voting. Finally, the segmented abdominal organ is refined using mask-based affine registration and intensity-based locally weighted voting. Especially, gallbladder and pancreas are hierarchically refined using location information of neighbor organs such as liver, left kidney and spleen. Our method was tested on a dataset of 12 portal-venous phase CT data. The average DSC of total organs was $90.47{\pm}1.70%$. Our method can be used for patient-specific abdominal organ segmentation for rehearsal of laparoscopic surgery.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.124-129
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• 2003

A novel watermark registration method based on Radon transform for self-referencing watermark is presented. The novel method was successful in registering the watermark of the image that has undergone affine transformations and severe lossy compression, whereas the conventional method based on peak detection failed to register the watermark.

• Journal of radiological science and technology
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• v.32 no.2
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• pp.195-203
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• 2009

Image registration is a fundamental task in image processing used to match two or more images. It gives new information to the radiologists by matching images from different modalities. The objective of this study is to develop 2D image registration algorithm for PET/CT and CT images acquired by different systems at different times. We matched two CT images first (one from standalone CT and the other from PET/CT) that contain affluent anatomical information. Then, we geometrically transformed PET image according to the results of transformation parameters calculated by the previous step. We have used Affine transform to match the target and reference images. For the similarity measure, mutual information was explored. Use of particle swarm algorithm optimized the performance by finding the best matched parameter set within a reasonable amount of time. The results show good agreements of the images between PET/CT and CT. We expect the proposed algorithm can be used not only for PET/CT and CT image registration but also for different multi-modality imaging systems such as SPECT/CT, MRI/PET and so on.

• Journal of the Semiconductor & Display Technology
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• v.12 no.3
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• pp.59-64
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• 2013

Accurate and robust image registration is important task in many applications such as image retrieval and computer vision. To perform the image registration, essential required steps are needed in the process: feature detection, extraction, matching, and reconstruction of image. In the process of these function, feature extraction not only plays a key role, but also have a big effect on its performance. There are two representative algorithms for extracting image features, which are scale invariant feature transform (SIFT) and speeded up robust feature (SURF). In this paper, we present and evaluate two methods, focusing on comparative analysis of the performance. Experiments for accurate and robust feature detection are shown on various environments such like scale changes, rotation and affine transformation. Experimental trials revealed that SURF algorithm exhibited a significant result in both extracting feature points and matching time, compared to SIFT method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.5
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• pp.409-416
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• 2006

To use image data obtained from different sensors and different techniques, the preprocessing step that registers them in a common coordinate system is needed. For this purpose, we developed the methodology to register middle- and low-resolution satellite images automatically. Firstly, candidate matching points were extracted using the Harris and Harris-affine algorithm. Secondly, we used the correlation coefficient, normalized correlation coefficient and SIFT algorithm to detect conjugate matching points from candidates. Then, to test the feasibility of approaches, we applied the developed methodology to various kinds of satellite images and compared results. The results clearly demonstrate that the methology using the SIFT is appropriate to register these multi-resolution satellite images automatically, compared with the classical cross-correlation.

• Journal of Biomedical Engineering Research
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• v.23 no.1
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• pp.39-47
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• 2002

The objective of this study is to provide a Precise form of spatial normalization with affine transformation. The quantitative comparison of the brain architecture across different subjects requires a common coordinate system. For the common coordinate system, not only global brain but also a local region of interest should be spatially normalized. Registration using mutual information generally matches the whose brain well. However. a region of interest may not be normalized compared to the feature-based methods with the landmarks. The hybrid method of this Paper utilizes feature information of the local region as well as intensity similarity. Central gray nuclei of a brain including copus callosum, which is used for feature in Schizophrenia detection, is appropriately normalized by the hybrid method. In the results section. our method is compared with mutual information only method and Talairach mapping with schizophrenia Patients. and is shown how it accurately normalizes feature .

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.161-166
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• 2007

Multi-functional Transport Satellite lR(MTSAT-lR)과 같은 정지궤도 기상위성의 지상 전처리 과정에는 영상위치보정(Image navigation and registration)이 포함된다. 영상위치보정은 위성 영상의 기하학적인 왜곡을 보정하는 과정이다. 랜드마크를 이용하는 영상위치보정 과정은 랜드마크 결정과 센서 모델 추정， 리샘플링(Resampling)의 세 가지 단계로 나눌 수 있다. MTSAT-1R의 High Resolution Image Data(HiRID)는 이미 영상위치보정이 수행되었지만, 기하학적인 오차가 남아있는 영상을 포함하기도 한다. 본 연구에서는 이런 기하학적인 오차를 제거하기 위해서 강인추정 기법에 기반한 기하보정을 수행하였다. 이태윤 등 (2005)은 강인추정 기법과 Direct Linear Transformation (DLT)에 기반한 오정합 판별 방법을 제안하였다. 이 판별 방법을 적용하여 추정된 DLT로 MTSAT-1R 영상의 기하보정을 수행한 결과에는 향상된 정확도로 기하보정 된 영상 뿐만 아니라 비교적 큰 오차를 포함하는 영상도 있었다. 이를 해결하기 위해서 본 연구에서는 강인추정 기법과 Affine 변환을 이용한 방법을 적용하였다. 본 연구에서는 기준 해안선에서 추출한 1,407개의 랜드마크와 8개의 MTSAT-1R 영상을 이용하였으며，강인추정 기법에 DLT를 적용한 방법과 Affine 변환을 적용한 방법으로 자동 기하보정을 수행하여 그 결과를 비교하였다. 또한 강인추정 기볍 중 RANSAC과 MSAC의 적용 결과를 비교하여 보았다. 그 결과，DLT로 기하보정 시，본 논문에서 제안된 방법이 강인추정 기법에 DLT를 적용한 방법 보다 더 좋은 성능을 보여주었다.

• 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.

• Journal of KIISE:Software and Applications
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• v.37 no.1
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• pp.9-18
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• 2010

This paper proposes a deformable registration method using a demon algorithm for aligning the lungs between end-exhale and end-inhale CT scans. The lungs are globally aligned by affine transformation and locally deformed by a demon algorithm. The use of floating gradient force allows a fast convergence in the lung regions with a weak gradient of the reference image. The active-cell-based demon algorithm helps to accelerate the registration process and reduce the probability of deformation folding because it avoids unnecessary computation of the displacement for well-matched lung regions. The performance of the proposed method was evaluated through comparisons of methods that use a reference gradient force or a combined gradient force as well as methods with and without active cells. The results show that the proposed method can accurately register lungs with large deformations and can reduce the processing time considerably.