• Journal of the Korean Society for Precision Engineering
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• v.18 no.11
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• pp.51-57
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• 2001

A light beam reflected from a machined surface generally containes information concerning about its surface roughness. This study examines and proposes a surface roughness measurement technique for on-machine measurement of machined surfaces. The technique is based on the measurement of a reflected laser beam pattern and the statistical analysis of its light intensity distribution. The surface roughness was found to be closely related to the standard deviation of the light intensity on the primary axis of the reflected pattern. An image acquisition device is made up of a laser diode, a half mirror, a screen, and a CCD camera. The exact image with the primary and secondary axes of a reflected laser beam pattern is calculated through such image processing algorithm as thresholding, edge detection, image rotation, segmentation, etc. A median filter and a surrounding light correction algorithm are improve the image quality and reduce the measuring error. Using the developed measuring device the effect of screen materials and workpiece and workpiece materials was investigated. Experimental results regarding to relatively high-quality surfaces machined by grinding, polishing, lapping processes have shown the measurement error is within 10% in the range of $0.1{mu}m~0.8{\mu}m R_q.$Therefore, the proposed method is thought to be effectively used when quick measurements is needed with workpieces fixed on the machine.

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.123-129
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• 2006

High-resolution aerial color image offers great possibilities for geometric and semantic information for spatial data generation. However, shadow casts by buildings and trees in high-density urban areas obscure much of the information in the image giving rise to potentially inaccurate classification and inexact feature extraction. Though many researches have been implemented for solving shadow casts, few studies have been carried out about the extraction of features hindered by shadows from aerial color images in urban areas. This paper presents a asphalt road boundary extraction technique that combines information from aerial color image and LIDAR (LIght Detection And Ranging) data. The following steps have been performed to remove shadow effects and to extract road boundary from the image. First, the shadow regions of the aerial color image are precisely located using LEAR DSM (Digital Surface Model) and solar positions. Second, shadow regions assumed as road are corrected by shadow path reconstruction algorithms. After that, asphalt road boundary extraction is implemented by segmentation and edge detection. Finally, asphalt road boundary lines are extracted as vector data by vectorization technique. The experimental results showed that this approach was effective and great potential advantages.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.363-371
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• 2015

Feature extraction is one of the main goals in many remote sensing analyses. After high-resolution imagery became more available, it became possible to extract more detailed and specific features. Thus, considerable image segmentation algorithms have been developed, because traditional pixel-based analysis proved insufficient for high-resolution imagery due to its inability to handle the internal variability of complex scenes. However, the individual segmentation method, which simply uses color layers, is limited in its ability to extract various target features with different spectral and shape characteristics. Spectral indices can be used to support effective feature extraction by helping to identify abundant surface materials. This study aims to evaluate a feature extraction method based on a segmentation technique with spectral indices. We tested the extraction of diverse target features-such as buildings, vegetation, water, and shadows from eight band WorldView-2 satellite image using decision tree classification and used the result to draw the appropriate spectral indices for each specific feature extraction. From the results, We identified that spectral band ratios can be applied to distinguish feature classes simply and effectively.

• Journal of ILASS-Korea
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• v.15 no.2
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• pp.53-60
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• 2010

This study presents development of digital particle holographic system and its application to spray field to measure three-dimensional velocities and sizes of spray droplets. A double exposure hologram recording system with synchronization system for time control was established and digital holograms can be recorded in a short time interval. To process recorded holograms, the correlation coefficient method was used for focal plane determination of particles. To remove noises and improve the quality of holograms and reconstructed images, the Wiener filter was adopted. The two-threshold and image segmentation methods were used in binary image transformation. For particle pairing, the match probability method was adopted. The developed system was applied to spray field and three-dimensional velocities and sizes of spray droplets were measured. The measurement results of digital holographic system were compared with those made by laser instruments, PDPA(Phase Doppler Particle Analyzer), which proved the feasibility of in-line digital particle holographic system as a good measurement tool for spray droplets.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.137-140
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• 2021

본 논문에서는 X-Ray 영상에서 발 뼈의 골절 영역을 분석 및 진단하기 위한 전단계로서 X-Ray 영상에서 뼈와 피부 영역을 분할하는 방법을 제안한다. 제안된 방법은 X-Ray 영상의 피부 영역과 발 뼈 영역을 분할하기 위해 가우시안 필터를 적용하여 DOG 영상을 생성한다. 그러나 기존의 가우시안 필터는 정적으로 적용되기 때문에 영상을 촬영하는 부위와 각도에 따라 영상의 특성이 달라지는 X-Ray 영상에 적용하기에 부적합하다. 따라서 부위와 각도에 따라 영상의 특성 변화에 민감하지 않는 동적 가우시안 필터를 제안한다. 실험 결과에서는 제안하는 동적 가우시안 필터와 기존의 정적인 가우시안 필터를 각각 적용하여 생성된 DOG 영상에 대해서 발 뼈 영역과 피부 영역을 분할하고, 효율성을 TPR과 특이도로 분석한 결과, 제안된 동적 가우시안 필터를 적용한 방법이 정적 가우시안 필터보다 평균적으로 TPR는 0.12%와 특이도는 평균적으로 0.36%가 개선된 것을 확인하였다.

• Journal of applied mathematics & informatics
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• v.42 no.4
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• pp.931-943
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• 2024

Accurately identifying brain tumors is crucial for medical imaging's precise diagnosis and treatment planning. This study presents a novel approach that uses cutting-edge image processing techniques to automatically segment brain tumors. with the use of the Pyramid Network algorithm. This technique accurately and robustly delineates tumor borders in MRI images. Our strategy incorporates special algorithms that efficiently address problems such as tumor heterogeneity and size and shape fluctuations. An assessment using the RESECT Dataset confirms the validity and reliability of the method and yields promising results in terms of accuracy and computing efficiency. This method has a great deal of promise to help physicians accurately identify tumors and assess the efficacy of treatments, which could lead to higher standards of care in the field of neuro-oncology.

• Imaging Science in Dentistry
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• v.45 no.1
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• pp.7-13
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• 2015

Purpose: We have developed a new method of segmenting the areas of absorbable implants and bone using region-based segmentation of micro-computed tomography (micro-CT) images, which allowed us to quantify volumetric bone-implant contact (VBIC) and volumetric absorption (VA). Materials and Methods: The simple threshold technique generally used in micro-CT analysis cannot be used to segment the areas of absorbable implants and bone. Instead, a region-based segmentation method, a region-labeling method, and subsequent morphological operations were successively applied to micro-CT images. The three-dimensional VBIC and VA of the absorbable implant were then calculated over the entire volume of the implant. Two-dimensional (2D) bone-implant contact (BIC) and bone area (BA) were also measured based on the conventional histomorphometric method. Results: VA and VBIC increased significantly with as the healing period increased (p<0.05). VBIC values were significantly correlated with VA values (p<0.05) and with 2D BIC values (p<0.05). Conclusion: It is possible to quantify VBIC and VA for absorbable implants using micro-CT analysis using a region-based segmentation method.

• Journal of Korea Multimedia Society
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• v.16 no.9
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• pp.1031-1043
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• 2013

Extracting hand region from images is the first part in the process to recognize hand posture and gesture interaction. Therefore, a good segmenting method is important because it determines the overall performance of hand recognition systems. Conventional hand segmentation researches were prone to changing illumination conditions or limited to the ability to detect multiple people. In this paper, we propose a robust technique based on the fusion of skin-color data and depth information for hand segmentation process. The proposed algorithm uses skin-color data to localize accurate seed location for region-growing from a complicated background. Based on the seed location, our algorithm adjusts each detected blob to fill up the hole region. A region-growing algorithm is applied to the adjusted blob boundary at the detected depth image to obtain a robust hand region against illumination effects. Also, the resulting hand region is used to train our skin-model adaptively which further reduces the effects of changing illumination. We conducted experiments to compare our results with conventional techniques which validates the robustness of the proposed algorithm and in addition we show our method works well even in a counter light condition.

• The KIPS Transactions:PartD
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• v.10D no.1
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• pp.109-116
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• 2003

In this paper, a CORBA-based collaborative medical image analysis and visualization system, which provides high accessibility and usability of the system for the users on distributed environment is introduced. The system allows us to manage datasets and manipulates medical images such as segmentation and volume visualization of computed geometry from biomedical images in distributed environments. Using Bayesian classification technique and an active contour model the system provides classification results of medical images or boundary information of specific tissue. Based on such information, the system can create real time 3D volume model from medical imagery. Moreover, the developed system supports collaborative work among multiple users using broadcasting and synchronization mechanisms. Since the system is developed using Java and CORBA, which provide distributed programming, the remote clients can access server objects via method invocation, without knowing where the distributed objects reside or what operating system it executes on.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1713-1720
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• 1999

This paper presents a new key frame extraction technique, for scene change detection, using the proposed AHIM (Accumulative Histogram Intersection Measure) from the DC image constructed by DCT DC coefficients in the compressed video sequence that is video compression standard such as MPEG. For fast content-based browsing and video retrieval in a video database, we also provide a novel coarse-to-fine video indexing scheme. In the extracted key frame, we perform the region segmentation as a preprocessing. First, the segmented image is projected with the horizontal direction, then we transform the result into a histogram, which is saved as a database index. In the second step, we calculate the moments and change them into a distance value. From the simulation results, the proposed method clearly shows the validity and superiority in respect of computation time and memory space, and that in conjunction with other techniques for indexing, such as color, can provide a powerful framework for image indexing and retrieval.