• Journal of Korean Society for Geospatial Information Science
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• v.16 no.3
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• pp.79-85
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• 2008

This study aims to derive a practical coordinate transformation method for the existing geographic information database. After analyzing the status and problems of existing 1/1,000 digital base map and GIS application database, the transformation parameters are estimated and the accuracy of the transformation is determined based on the transformed coordinates. We analyzed the accuracy of a transformation using the published national transformation coefficients as well as the estimated local transformation coefficients using national and urban control points in a study area. In addition, the 1/1,000 digital base map from aerial triangulation is compared with respect to the coordinates of urban control points. Based on the comparison, the biases on the national control points which were used at the time of digital map generation was analyzed. Then, the accuracy of transformed coordinates based on the world geodetic system using local transformation coefficients estimated from urban control points are determined. We also analyzed the transformation accuracy of underground infrastructure database using the same transformation method as the case of 1/1,000 digital base map. Through this study, it was found that the estimation of transformation coefficients by Molodensky-Badekas using urban control points was suitable for a local government. Furthermore, it was obvious that the accuracy of a 2-dimensional affine transformation was comparable to that of 7 parameter transformation for a local area. Applying the coordinate transformation and bias correction, we could transform GIS application database which was built by an offset surveying based on digital base map within the transformation accuracy of 10 cm. Therefore, it was judged that there will not be a big problem on the transformation of the GIS DB to the world geodetic system.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.2
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• pp.79-88
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• 2004

Gaze detection is to locate the position on a monitor screen where a user is looking by computer vision. Gaze detection systems have numerous fields of application. They are applicable to the man-machine interface for helping the handicapped to use computers and the view control in three dimensional simulation programs. In our work, we implement it with a computer vision system setting a IR-LED based single camera. To detect the gaze position, we locate facial features, which is effectively performed with IR-LED based camera and SVM(Support Vector Machine). When a user gazes at a position of monitor, we can compute the 3D positions of those features based on 3D rotation and translation estimation and affine transform. Finally, the gaze position by the facial movements is computed from the normal vector of the plane determined by those computed 3D positions of features. In addition, we use a trained neural network to detect the gaze position by eye's movement. As experimental results, we can obtain the facial and eye gaze position on a monitor and the gaze position accuracy between the computed positions and the real ones is about 4.8 cm of RMS error.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.3
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• pp.23-35
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• 2002

Gaze detection is to locate the position on a monitor screen where a user is looking. In our work, we implement it with a computer vision system setting a single camera above a monitor and a user moves (rotates and/or translates) his face to gaze at a different position on the monitor. To detect the gaze position, we locate facial region and facial features(both eyes, nostrils and lip corners) automatically in 2D camera images. From the movement of feature points detected in starting images, we can compute the initial 3D positions of those features by camera calibration and parameter estimation algorithm. Then, when a user moves(rotates and/or translates) his face in order to gaze at one position on a monitor, the moved 3D positions of those features can be computed from 3D rotation and translation estimation and affine transform. Finally, the gaze position on a monitor is computed from the normal vector of the plane determined by those moved 3D positions of features. As experimental results, we can obtain the gaze position on a monitor(19inches) and the gaze position accuracy between the computed positions and the real ones is about 2.01 inches of RMS error.

• Journal of Korea Multimedia Society
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• v.15 no.9
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• pp.1102-1111
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• 2012

In this paper, we propose a robust watermarking method against geometric attack even though the watermarked image is partially damaged. This method consists of standard image normalization which transforms any image into a predefined standard image and embedding watermark in DCT domain of standard normalized image using spread spectrum technique. The proposed standard image normalization method has an improvement over existing image normalization method, so it is robust to partial damage and geometric attack. The watermark embedding method using spread spectrum technique also has a robustness to image losses such as blurring, sharpening and compressions. In addition, the proposed watermarking method does not need an original image to detect watermark, so it is useful to public watermarking applications. Several experimental results show that the proposed watermarking method is robust to partial damage and various attacks including geometric deformation.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.3
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• pp.15-24
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• 1998

In this paper, we present new method of fractal image coding based on iterated function system(IFS) suggested by Barnsley. In previous Fractal coding using full searching algorithm, the quality of reconstructed image was better than other fractal coding method's, but it took a long time in that algorithm for searching domain blocks matched. And it is performed through linear affine transform, therefore it is difficult to approximate the complex range blocks. In this paper, using quadtree partitioning, complex blocks are divided into more smaller blocks, and simple blocks are merged to more larger blocks. So, we can got more precisely approximated range blocks and reduce the number of transformations. Hence, we have improved the compression ratio. In addition, we restrict the region of searching domains in order to reduce the searching time and coding time. Compared with full searching algorithm, we reduced coding time drastically, and quality of reconstructed image was better in terms subjective criteria. And compared with Monro's, our method is slower, but we could obtain a reconstructed image with better quality.

• Journal of IKEEE
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• v.24 no.1
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• pp.147-153
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• 2020

Conventional lane detection algorithms have problems in that the detection rate is lowered in road environments having a large change in curvature and illumination. The probabilistic Hough transform method has low lane detection rate since it exploits edges and restrictive angles. On the other hand, the method using a sliding window can detect a curved lane as the lane is detected by dividing the image into windows. However, the detection rate of this method is affected by road slopes because it uses affine transformation. In order to detect lanes robustly and avoid obstacles, we propose driving assist system using semantic segmentation based on deep learning. The architecture for segmentation is SegNet based on VGG-16. The semantic image segmentation feature can be used to calculate safety space and predict collisions so that we control a vehicle using adaptive-MPC to avoid objects and keep lanes. Simulation results with CARLA show that the proposed algorithm detects lanes robustly and avoids unknown obstacles in front of vehicle.

• Spatial Information Research
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• v.9 no.2
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• pp.263-275
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• 2001

With increasing of utilization of 1/1,000 Digital-Map being constructed with NGIS project, it is entering upon the stage that problem as the inspection of position accuracy of Digital-Map before its use. This paper evaluated position accuracy of Digital-Map being based on the construction of college facility management system into small area and presented modification/renovation of Digital-Map on area being occurred position error. With a view to evaluation of position error to building using RTK survey, position error was shown more than 0.5mm that is prescribed in survey-law. In order to acquire good position accuracy, first we carried out Static survey to college and selected control point. And, we evaluated coordinate value to important building from control point and compared these results with RTK survey results. Second, we carried out Affine transform based on the control point of building being surveyed with RTK, transformed pre-constructed Digital-Map and compared these results with RTK Survey results. We analyzed first and second method and presented improvement method of position accuracy of Digital-Map suited on small area.

• Journal of Broadcast Engineering
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• v.17 no.1
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• pp.17-25
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• 2012

In this paper, we propose a method of producing a stereoscopic image from multiple images captured from a monoscopic camera. By translating a camera in the horizontal direction, left and right images are chosen among N captured images. For this, image edges are extracted and a rotational angle is estimated from edge orientation. Also, a translational vector is also estimated from the correlation of projected image data. Then, two optimal images are chosen and subsequently compensated using the rotational angle as well as the translational vector in order to make a satisfactory stereoscopic image. The proposed method was performed on thirty-two test image set. The subjective visual fatigue test was carried out to validate the 3D quality of stereoscopic images. In terms of visual fatigue, the 3D satisfaction ratio reached approximately 84%.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.727-731
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• 2003

ASTER image has some advantages for classification such as 15 spectral bands and 15m ${\sim}$ 90m spatial resolution. However, in the classification using general remote sensing image, shadow areas are often classified into water area. It is very difficult to divide shadow and water. Because reflectance characteristics of water is similar to characteristics of shadow. Many land cover items are consisted in one pixel which is 15m spatial resolution. Nowadays, very high resolution satellite image (IKONOS, Quick Bird) and Digital Surface Model (DSM) by air borne laser scanner can also be used. In this study, mixed pixel analysis of ASTER image has carried out using IKONOS image and DSM. For mixed pixel analysis, high accurated geometric correction was required. Image matching method was applied for generating GCP datasets. IKONOS image was rectified by affine transform. After that, one pixel in ASTER image should be compared with corresponded 15×15 pixel in IKONOS image. Then, training dataset were generated for mixed pixel analysis using visual interpretation of IKONOS image. Finally, classification will be carried out based on Linear Mixture Model. Shadow extraction might be succeeded by the classification. The extracted shadow area was validated using shadow image which generated from 1m${\sim}$2m spatial resolution DSM. The result showed 17.2% error was occurred in mixed pixel. It might be limitation of ASTER image for shadow extraction because of 8bit quantization data.

• Progress in Medical Physics
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• v.21 no.2
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• pp.153-164
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• 2010

To determine the clinical target volumes considering vascularity and cellularity of tumors, the software was developed for mapping of the analyzed biological clinical target volumes on anatomical images using regional cerebral blood volume (rCBV) maps and apparent diffusion coefficient (ADC) maps. The program provides the functions for integrated registrations using mutual information, affine transform and non-rigid registration. The registration accuracy is evaluated by the calculation of the overlapped ratio of segmented bone regions and average distance difference of contours between reference and registered images. The performance of the developed software was tested using multimodal images of a patient who has the residual tumor of high grade gliomas. Registration accuracy of about 74% and average 2.3 mm distance difference were calculated by the evaluation method of bone segmentation and contour extraction. The registration accuracy can be improved as higher as 4% by the manual adjustment functions. Advanced MR images are analyzed using color maps for rCBV maps and quantitative calculation based on region of interest (ROI) for ADC maps. Then, multi-parameters on the same voxels are plotted on plane and constitute the multi-functional parametric maps of which x and y axis representing rCBV and ADC values. According to the distributions of functional parameters, tumor regions showing the higher vascularity and cellularity are categorized according to the criteria corresponding malignant gliomas. Determined volumes reflecting pathological and physiological characteristics of tumors are marked on anatomical images. By applying the multi-functional images, errors arising from using one type of image would be reduced and local regions representing higher probability as tumor cells would be determined for radiation treatment plan. Biological tumor characteristics can be expressed using image registration and multi-functional parametric maps in the developed software. The software can be considered to delineate clinical target volumes using advanced MR images with anatomical images.