• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.813-816
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• 2002

컴퓨터 보급의 급속한 발전과 멀티미디어의 등장으로 기존의 텍스트와 이미지, 음성, 오디오, 동영상 등의 정보를 디지털화하고, 컴퓨터를 이용하여 저장, 처리, 전송하게 되면서 의료 분야에서도 상당한 업무의 변화를 요구하게 되었다. 의료 분야에서의 이러한 급격한 개방과 더불어 초고속 정보 통신의 발달은 원격진료라는 또 다른 요구를 대두시키고 있다. 이를 위해서는, 멀티미디어 기술, 대용량의 정보를 저장하는 데이터베이스 기술, 초속의 광 대역 기술 등을 통합하여 종합적인 멀티미디어 의료 정보 시스템을 구축하는 것이 시급하다. 이러한 이유들로 본 논문에서는 병원/의원의 의료진들로 하여금 의료영상이나 자료를 상호 전송하여 환자의 진료 또는 검진결과를 확인하고 전문가의 조언 등을 구하는 원격 진료용 의료영상 시스템의 요구사항을 분석, 설계하고 구현하였다. 본 시스템은 클라이언트/서버 구조로써 영상 획득 및 출력, 의료영상 국제 표준 포맷인 DICOM 포맷으로의 영상 저장, MCA(Multi Channel Analyzer), ROI(Region Of Interest) 등의 영상 분석, 필터링 및 영상 확대/축소/회전 등의 각종 영상 처리의 주요 기능을 갖으며, 사용자가 편리하고 쉽게 사용할 수 있도록 아이콘(icon) 중심의 직관적인 인터페이스를 갖는다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.2
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• pp.141-147
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• 2011

This study analyzed the issues and its usefulness of drawing small-scale digital map by using the large-scale digital map which was producted with high-resolution digital aerial photograph which are commonly photographed in recent years. To this end, correlation analysis of the feature categories on the digital map was conducted, and this map was processed by inputting data, organizing, deleting, editing, and supervising feature categories according to the generalization process. As a result, 18 unnecessary feature codes were deleted, and the accuracy of 1/5,000 for the digital map was met. Although the size of the data and the number of feature categories increased, this was proven to be shown due to the excellent description of the digital aerial photograph. Accordingly, it was shown that drawing a small-scale digital map with the large-scale digital map by digital aerial photograph provided excellent description and high-quality information for digital map.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.1
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• pp.28-37
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• 2007

This paper addresses a new surface reconstruction scheme for approximating the isosurface from a set of tomographic cross sectional images. Differently from the novel Marching cube algorithm, our method does not extract iso-density surface(isosurface) directly from the voxels but calculates the iso-density point(isopoint) first. After building the relatively coarse initial mesh by the Cell-boundary algorithm approximating the isosurface, it produces the final isosurface by iteratively shrinking and smoothing the initial mesh. Comparing with the Marching Cube algorithm, our method is robust and does not make any crack in resulting surface model. Furthermore, the proposed method surmounts the O(1)-adjacency limitation of MC in defining the isopoints by permitting the O(2) and O(3)-adjacent isopoints in surface reconstruction, and can produce more accurate isosurface. According to experiments, it is proved to be very robust and efficient for isosurface reconstruction from cross sectional images.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.4 s.310
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• pp.87-95
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• 2006

In this paper, we propose a novel feature extraction method for face recognition, based on Discrete Cosine Transform (DCT), Energy Probability (EP), and Linear Discriminant Analysis (LDA). We define an energy probability as magnitude of effective information and it is used to create a frequency mask in OCT domain. The feature extraction method consists of three steps; i) the spatial domain of face images is transformed into the frequency domain called OCT domain; ii) energy property is applied on DCT domain that acquire from face image for the purpose of dimension reduction of data and optimization of valid information; iii) in order to obtain the most significant and invariant feature of face images, LDA is applied to the data extracted using frequency mask. In experiments, the recognition rate is 96.8% in ETRI database and 100% in ORL database. The proposed method has been shown improvements on the dimension reduction of feature space and the face recognition over the previously proposed methods.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.29-35
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• 2010

This paper discusses a method that can enhance the exactness of depth estimation of an image by PCA(Principle Component Analysis) based on feature reduction through learning algorithm. In estimation of the depth of an image, hyphen such as energy of pixels and gradient of them are found, those selves and their relationship are used for depth estimation. In such a case, many features are obtained by various filter operations. If all of the obtained features are equally used without considering their contribution for depth estimation, The efficiency of depth estimation goes down. This paper proposes a method that can enhance the exactness of depth estimation of an image and its processing speed is considered as the contribution factor through PCA. The experiment shows that the proposed method(30% of an feature vector) is more exact(average 0.4%, maximum 2.5%) than using all of an image data in depth estimation.

• The Journal of the Korea Contents Association
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• v.8 no.2
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• pp.74-82
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• 2008

This paper investigates chrominance reconstruction methods that reconstruct subsampled chrominance information efficiently using the correlation between luminance and chrominance components in the decompression process of compressed images, and analyzes drawbacks involved in the adaptive-weighted 2-dimensional linear interpolation among the methods, which shows higher efficiency in the view of computational complexity than other methods. To improve the drawback that the spatial frequency distribution is not considered for the decompressed image and to support the application on a low-performance system in behalf of 2-dimensional linear interpolation, this paper proposes the multi-mode reconstruction method which uses three reconstruction methods having different computational complexity from each other according to the degree of edge response of luminance component. The performance evaluation on a development platform for embedded systems showed that the proposed reconstruction method supports the similar level of image quality for decompressed images while reducing the overall computation time for chrominance reconstruction in comparison with the 2-dimensional linear interpolation.

• The Transactions of the Korea Information Processing Society
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• v.5 no.5
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• pp.1367-1376
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• 1998

In this paper we propose an real time marker detection algorithm for motion analysis both in 2 dimensions and 3 dimensions with CCD camera and rfame grabber only which has no image processor. The main algorithm consists of the following 3 algorithms; 1) the tracing algorithm that makes it possible to predict the expected marker location by narrowing the searching boundary, 2) the searching algorithm that detects the marker in the expected boundary using Ad-hoc previous screen search technique, tornado search method rotate diagonal search method search technique, 3) the algorithm that finds the central point of the detected marker. We try to narrow the searching boundary for real time processing. Also, it is able to find the central point of the detected marker much faster than typical contour tracing algorithm.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.4
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• pp.141-147
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• 2014

The wavelet shrinkage is a technique that reduces the wavelet coefficients to minimize the MSE(Mean Square Error) between the signal and the noisy signal by making use of the threshold determined by the variance of the wavelet coefficients. In this paper, by using the monotonic transform and the power of wavelet subbands, new thresholds applicable to the high and the low frequency wavelet bands are proposed, and the thresholds are applied to the ST(soft-threshold) technique to denoise on image signals with additive Gaussian noise. And the results of PSNRs are compared with the results obtained by the VisuShrink technique and those of [15]. The results shows the validity of this technique.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.1111-1116
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• 2009

In this paper, we discuss distortion-tolerant pattern recognition using computational integral imaging reconstruction. Three-dimensional object information is captured by the integral imaging pick-up process. The captured information is numerically reconstructed at arbitrary depth-levels by averaging the corresponding pixels. We apply Fisher linear discriminant analysis combined with principal component analysis to computationally reconstructed images for the distortion-tolerant recognition. Fisher linear discriminant analysis maximizes the discrimination capability between classes and principal component analysis reduces the dimensionality with the minimum mean squared errors between the original and the restored images. The presented methods provide the promising results for the classification of out-of-plane rotated objects.

• The Transactions of the Korea Information Processing Society
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• v.6 no.12
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• pp.3703-3709
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• 1999

We propose a new approach to reconstruct the surface shape of an object from a shaded image. We use genetic algorithm instead of gradient descent algorithm which is apt to take to local minima and also proposes genetic representation and suitable genetic operators for manipulating 2-D image. And for more effective execution, we suggest hierarchical process to reconstruct minutely the surface of an object after coarse and global reconstruction. A modified Lambertian illumination model including the distance factor was herein adopted to get more reasonable result and an experiment was performed with synthesized and real images to demonstrate the devised method, of which results show the usefulness of our method.