• Journal of Korean Society for Geospatial Information Science
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• v.10 no.3 s.21
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• pp.79-87
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• 2002

This paper proposes an approach of automatically identifying the center of fiducial marks using radiometric and geometric characteristics of those marks existing on aerial photographs. Candidate region surrounding a mark, based on radiometric strategy, is determined by producing a bi-level image and by applying morphological operations. Based on geometric strategy, the central location of a mark is determined by applying ${\bigtriangledown}^G$ filtering and symmetry-enhancement filtering, and by finding peak location of symmetry. Evaluation with respect to 66 cases of sub-images containing a fiducial mark showed that the central location of the mark is determined up to around one pixel difference whit it is compared to a manual inspection.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.63-69
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• 2013

Change detection technique is essential to various applications of Very High-Resolution(VHR) satellite imagery and land monitoring. However, change detection accuracy of VHR satellite imagery can be decreased due to various geometrical dissimilarity. In this paper, the existing fusion evaluation indexes were revised and applied to improve VHR imagery based change detection accuracy between multi-temporal images. In addition, appropriate change detection methodology of VHR images are proposed through comparison of general change detection algorithm with cross-sharpened image based change detection algorithm. For these purpose, ERGAS, UIQI and SAM, which were representative fusion evaluation index, were applied to unsupervised change detection, and then, these were compared with CVA based change detection result. Methodologies for minimizing the geometrical error of change detection algorithm are analyzed through evaluation of change detection accuracy corresponding to image fusion method, also. The experimental results are shown that change detection accuracy based on ERGAS index by using cross-sharpened images is higher than these based on other estimation index by using general fused image.

• Korean Journal of Remote Sensing
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• v.28 no.2
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• pp.191-202
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• 2012

A high resolution satellite imagery such as KOMPSAT-2 includes a material containing rational polynomial coefficient (RPC) for three-dimensional geopositioning. However, image geometries which are calculated from the RPC must have inevitable systematic errors. Thus, it is necessary to correct systematic errors of the RPC using several ground control points (GCPs). In this paper, we propose an efficient method for automatic correction of image geometries using tie points of a stereo pair and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) without GCPs. This method includes four steps: 1) tie points extraction, 2) determination of the ground coordinates of the tie points, 3) refinement of the ground coordinates using SRTM DEM, and 4) RPC adjustment model parameter estimation. We validates the performance of the proposed method using KOMPSAT-2 stereo pair. The root mean square errors (RMSE) achieved from check points (CPs) were about 3.55 m, 9.70 m and 3.58 m in X, Y;and Z directions. This means that we can automatically correct the systematic error of RPC using SRTM DEM.

• Investigative Magnetic Resonance Imaging
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• v.14 no.1
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• pp.47-55
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• 2010

Purpose : To evaluate the geometry of carotid artery by assessing the images of contrast-enhanced MR angiography (CE-MRA) and interrelationships between the geometry of carotid artery and clinical factors. Materials and Methods : 216 consecutive patients who performed supraaortic CE-MRA with fast spoiled gradient-echo imaging were included. Their medical records were reviewed for variable information including risk factors predictive of generalized atherosclerotic disease (age, hypertension (HTN), diabetes mellitus, hyperlipidema, and smoking), sex, body weight, height, and body mass index (BMI). We reviewed the CE-MRA with carotid origin (3 types), carotid artery tortuosity, angle of internal carotid artery bifurcation, the type of aortic arch branching, and the presence of the coiling of carotid artery. Results : Multinomial logistic regression analysis showed that significantly contributed clinical backgrounds for carotid origin were the age and the BMI. With an increase of age at 1, the probability that the type of carotid origin become from type 1 to type 2 was 0.9 times (p=0.004) in right carotid artery (RCA), 0.9 times (p = 0.031) in left carotid artery (LCA), 0.9 times that are likely to be type3 from type 2 (p<0.001) in RCA and 0.9 times in LCA (p=0.009). Increase in BMI at 1 increased odds of becoming type 2 as 1.1 times (p = 0.067) in RCA, 1.1 times (p=0.009) in LCA and increased chance of becoming type 3 as 1.2 times (p = 0.001) in RCA, 1.2 times (p=0.003) in LCA. Mean value of right and left carotid tortuosity were $240.9{\pm}69.0^{\circ}$and $154.4{\pm}55.0^{\circ}$, respectively. Conclusion : The BMI, age, sex and presence of HTN affects the geometry of carotid arteries, the site of origin and tortuosity of carotid artery specifically.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1135-1147
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• 2021

In order to geometrically correct high-resolution satellite imagery, the sensor modeling process that restores the geometric relationship between the satellite sensor and the ground surface at the image acquisition time is required. In general, high-resolution satellites provide RPC (Rational Polynomial Coefficient) information, but the vendor-provided RPC includes geometric distortion caused by the position and orientation of the satellite sensor. GCP (Ground Control Point) is generally used to correct the RPC errors. The representative method of acquiring GCP is field survey to obtain accurate ground coordinates. However, it is difficult to find the GCP in the satellite image due to the quality of the image, land cover change, relief displacement, etc. By using image maps acquired from various sensors as reference data, it is possible to automate the collection of GCP through the image matching algorithm. In this study, the RPC of KOMPSAT-3A satellite image was corrected through the extracted matching point using the UAV (Unmanned Aerial Vehichle) imagery. We propose a pre-porocessing method for the extraction of matching points between the UAV imagery and KOMPSAT-3A satellite image. To this end, the characteristics of matching points extracted by independently applying the SURF (Speeded-Up Robust Features) and the phase correlation, which are representative feature-based matching method and area-based matching method, respectively, were compared. The RPC adjustment parameters were calculated using the matching points extracted through each algorithm. In order to verify the performance and usability of the proposed method, it was compared with the GCP-based RPC correction result. The GCP-based method showed an improvement of correction accuracy by 2.14 pixels for the sample and 5.43 pixelsfor the line compared to the vendor-provided RPC. In the proposed method using SURF and phase correlation methods, the accuracy of sample was improved by 0.83 pixels and 1.49 pixels, and that of line wasimproved by 4.81 pixels and 5.19 pixels, respectively, compared to the vendor-provided RPC. Through the experimental results, the proposed method using the UAV imagery presented the possibility as an alternative to the GCP-based method for the RPC correction.

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.2 s.25
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• pp.61-66
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• 2003

The change detection using the satellite imagery and airphotos has been carried out in the application of terrain mapping, environment, forestry, facility detection, etc. The low-spatial resolution data such as Landsat, NOAA satellite images is generally used for automatic change detection, while on the other hand the high-spatial resolution data is used for change detection by image interpretation. The research to integrate automatic method with manual change detection through the high-spatial resolution satellite image is performed. but the problem such as shadow, building 'lean' due to perspective geometry and precision geocorrection was found. In this paper we performed change detection using the IKONOS satellite images, and present the concerning problem.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.1
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• pp.13-19
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• 2002

In a different way of satellite image or aerial photo, the image acquired by terrestrial photogrammetry has to go through complicated management according to the desired precision and analysis range. In this study, digital surface model and ortho image for cultural asset, terrestrial structures, were made by reflectorless total station in order to increase the application of single photo, and that was analyzed and compared with the method using stereo image. Single photo is expected to be utilized as the measuring method for drawing cultural assets or examining the stability of slope in which high precision doesn't need by performing the various geometric and visual analysis using ortho image made by excluding the difficulties of acquisition and plotting of stereo image.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.1
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• pp.41-48
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• 2009

This Paper introduces representation and coding schemes of multiview texture and depth data for complex three-dimensional scenes. We represent input color and depth images using compressed texture and depth map pairs. The proposed X-codec encodes them further to increase compression ratio in a near-lossless way. Our system resolves two problems. First, rendering time and output visual quality depend on input image resolutions rather than scene complexity since a depth image-based rendering techniques is used. Second, the random access problem of conventional image-based rendering could be effectively solved using our image block-based compression schemes. From experimental results, the proposed approach is useful to graphics applications because it provides multiview rendering, selective decoding, and scene manipulation functionalities.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.2
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• pp.20-32
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• 2010

Previous hardware devices to capture human motion have many limitations; expensive equipment, complexity of manipulation or constraints of human motion. In order to overcome these problems, real-time motion capture algorithms based on computer vision have been actively proposed. This paper presents an efficient analysis method of multiple view images for real-time motion capture. First, we detect the skin color regions of human being, and then correct the image coordinates of the regions by using camera calibration and epipolar geometry. Finally, we track the human body part and capture human motion using kalman filter. Experimental results show that the proposed algorithm can estimate a precise position of the human body.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.657-664
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• 2003

Reliable tracking of moving humans is essential to motion estimation, video surveillance and human-computer interface. This paper presents a new approach to human motion tracking that combines appearance-based and model-based techniques. Monocular color video is processed at both pixel level and object level. At the pixel level, a Gaussian mixture model is used to train and classily individual pixel colors. At the object level, a 3D human body model projected on a 2D image plane is used to fit the image data. Our method does not use inverse kinematics due to the singularity problem. While many others use stochastic sampling for model-based motion tracking, our method is purely dependent on nonlinear programming. We convert the human motion tracking problem into a nonlinear programming problem. A cost function for parameter optimization is used to estimate the degree of the overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. Our method is used to recognize various human motions. Motion tracking results from video sequences are very encouraging.