• Journal of Korean Society for Geospatial Information Science
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• v.16 no.2
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• pp.49-56
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• 2008

Image registration is one of the critical techniques of image mosaic which has many applications such as generating panoramas, video monitoring, image rendering and reconstruction, etc. The fundamental tasks of image registration are point features extraction and tracking which take much computation time. KLT(Kanade-Lucas-Tomasi) feature tracker has proposed for extracting and tracking features through image sequences. The aim of this study is to demonstrate the usage of effective and robust KLT feature detector and tracker for an image registration using the sequence image frames captured by UAV video camera. In result, by using iterative implementation of the KLT tracker, the features extracted from the first frame of image sequences could be successfully tracked through all frames. The process of feature tracking in the various frames with rotation, translation and small scaling could be improved by a careful choice of the process condition and KLT pyramid implementation.

• Journal of Korea Multimedia Society
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• v.9 no.4
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• pp.421-433
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• 2006

This paper proposes a method to insert virtual objects into a real video stream based on feature tracking and camera pose estimation from a set of single-camera video frames. To insert or modify 3D shapes to target video frames, the transformation from the 3D objects to the projection of the objects onto the video frames should be revealed. It is shown that, without a camera calibration process, the 3D reconstruction is possible using multiple images from a single camera under the fixed internal camera parameters. The proposed approach is based on the simplification of the camera matrix of intrinsic parameters and the use of projective geometry. The method is particularly useful for augmented reality applications to insert or modify models to a real video stream. The proposed method is based on a linear parameter estimation approach for the auto-calibration step and it enhances the stability and reduces the execution time. Several experimental results are presented on real-world video streams, demonstrating the usefulness of our method for the augmented reality applications.

• Journal of Korea Multimedia Society
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• v.10 no.8
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• pp.1017-1028
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• 2007

In this paper we propose an effective method to represent, store, and retrieve video streams efficiently from a video database. We extract features from each video frame, normalize the feature values, and represent them as values in the range [0,1]. In this way a video frame with f features can be represented by a point in the f-dimensional space $[0,1]^f$, and thus the video stream is represented by a trail of points in the multidimensional space. The video stream is partitioned into video segments based on camera shots, each of which is represented by a trend vector which encapsulates the moving trend of points in a segment. The video stream query is processed depending on the comparison of those trend vectors. We examine our method using a collection of video streams that are composed of sports, news, documentary, and educational videos. Experimental results show that our trend vector representation reduces a reconstruction error remarkably (average 37%) and the retrieval using a trend vector achieves the high precision (average 2.1 times) while maintaining the similar response time and recall rate as existing methods.

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.547-552
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• 2007

This study presents an approach to reconstruct high-resolution imagery for multispectral imagery of low-resolution using panchromatic imagery of high-resolution. The proposed scheme reconstructs a high-resolution image which agrees with original spectral values. It uses a linear model of high-and low- resolution images and consists of two stages. The first one is to perform a global estimation of the least square error on the basis of a linear model of low-resolution image associated with high-resolution feature, and next local correction then makes the reconstructed image locally fit to the original spectral values. In this study, the new method was applied to KOMPSAT-1 EOC image of 6m and LANDSAT ETM+ of 30m, and an 1m RGB image was also generated from 4m IKONOS multispectral data. The results show its capability to reconstruct high-resolution imagery from multispectral data of low-resolution.

• Journal of KIISE:Software and Applications
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• v.27 no.4
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• pp.422-431
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• 2000

This paper proposes a new codebook generation method, called a PCA-Based VQ, that incorporates the PCA (Principal Component Analysis) technique into VQ (Vector Quantization) codebook design. The PCA technique reduces the data dimensions by transforming input image vectors into the feature vectors. The cluster of feature vectors in the transformed domain is bisectioned into two subclusters by an optimally chosen partitioning hyperplane. We expedite the searching of the optimal partitioning hyperplane that is the most time consuming process by considering that (1) the optimal partitioning hyperplane is perpendicular to the first principal axis of the feature vectors, (2) it is located on the equilibrium point of the left and right cluster's distortions, and (3) the left and right cluster's distortions can be adjusted incrementally. This principal axis bisectioning is successively performed on the cluster whose difference of distortion between before and after bisection is the maximum among the existing clusters until the total distortion of clusters becomes as small as the desired level. Simulation results show that the proposed PCA-based VQ method is promising because its reconstruction performance is as good as that of the SOFM (Self-Organizing Feature Maps) method and its codebook generation is as fast as that of the K-means method.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.624-630
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• 2010

As electrocardiogram(ECG) signals are generally sampled with a frequency of over 200Hz, a method to compress diagnostic information without losing data is required to store and transmit them efficiently. In this paper, an ECG signal compression method, which uses feature points based on curvature, is proposed. The feature points of P, Q, R, S, T waves, which are critical components of the ECG signal, have large curvature values compared to other vertexes. Thus, these vertexes are extracted with the proposed method, which uses local extremum of curvatures. Furthermore, in order to minimize reconstruction errors of the ECG signal, extra vertexes are added according to the iterative vertex selection method. Through the experimental results on the ECG signals from MIT-BIH Arrhythmia database, it is concluded that the vertexes selected by the proposed method preserve all feature points of the ECG signals. In addition, they are more efficient than the AZTEC(Amplitude Zone Time Epoch Coding) method.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.13-20
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• 2023

In this paper, we propose a water temperature prediction method using feature extraction and reconstructed data based on LSTM-Autoencoder. We used multivariate time series data such as sea surface water temperature in the Naksan area of the East Sea where the cold water zone phenomenon occurred, and wind direction and wind speed that affect water temperature. Using the LSTM-Autoencoder model, we used three types of data: feature data extracted through dimensionality reduction of the original data combined with multivariate data of the original data, reconstructed data, and original data. The three types of data were trained by the LSTM model to predict sea surface water temperature and evaluated the accuracy. As a result, the sea surface water temperature prediction accuracy using feature extraction of LSTM-Autoencoder confirmed the best performance with MAE 0.3652, RMSE 0.5604, MAPE 3.309%. The result of this study are expected to be able to prevent damage from natural disasters by improving the prediction accuracy of sea surface temperature changes rapidly such as the cold water zone.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.29 no.2
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• pp.387-396
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• 1999

In dentistry. RadioVisioGraphy was introduced as a first electronic dental x-ray imaging modality in 1989. Thereafter. many types of direct digital radiographic system have been produced in the last decade. They are based either on charge-coupled device(CCD) or on storage phosphor technology. In addition. new types of digital radiographic system using amorphous selenium. image intensifier etc. are under development. Advantages of digital radiographic system are elimination of chemical processing, reduction in radiation dose. image processing, computer storage. electronic transfer of images and so on. Image processing includes image enhancement. image reconstruction. digital subtraction, etc. Especially digital subtraction and reconstruction can be applied in many aspects of clinical practice and research. Electronic transfer of images enables filmless dental hospital and teleradiology/teledentistry system. Since the first image management and communications system(IMACS) for dentomaxillofacial radiology was reported in 1992. IMACS in dental hospital has been increasing. Meanwhile. researches about computer-assisted diagnosis, such as structural analysis of bone trabecular patterns of mandible. feature extraction, automated identification of normal landmarks on cephalometric radiograph and automated image analysis for caries or periodontitis. have been performed actively in the last decade. Further developments in digital radiographic imaging modalities. image transmission system. imaging processing and automated analysis software will change the traditional clinical dental practice in the 21st century.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.389-390
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• 2018

본 논문에서는 3차원 복원을 위한 특징점 추출 및 매칭에 대한 보다 정확한 방법을 제안한다. 이 방법은 컴퓨터 비전의 기본이 되는 분야로 복원뿐 만 아니라 SLAM과 같은 지도 작성 및 자율 운행에도 필요한 방법이다. 본 연구는 3차원 물체 복원을 위해서 사용하는 방법 중 하나인 Column space fitting(CSF)을 이용하여 turntable-image data에 적용하여 성능을 평가하여 정확성을 검증을 한다. 오늘날 3D scanner를 이용하여 물체를 3차원 모델을 획득하고 3D프린터를 이용하여 다양한 분야에 적용한다. 그러나 고가의 장비이기 때문에 접근성이 떨어진다. 본 연구는 영상들만을 가지고 기하학적 계산을 통해 3차원 모델을 획득한다. 본 연구결과는 기존의 방법인 KLT 알고리즘과 비교하여 RMSE의 값을 약 5배를 줄이는 성능 향상을 보인다.