• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.763-765
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• 2004

현재 의료 영상을 이용한 신속하고 정확한 진단과 치료를 위하여 각 기관별로 영상을 분할하는 방식이 기본적으로 사용되고 있다. 본 논문에서는 워터쉐드(Watershed) 알고리즘을 이용하여 해부학적 기관 중 폐 영역을 분할하는 방식을 제안한다. 초기에 소벨 에지 마스크(Sobel Edge Mask)를 이용하여 윤곽선을 강조하여 워터쉐드 알고리즘을 적용하였을 경우 과다 분할되는 문제점이 발생한다. 이를 해결하기 위하여 제거(Opening) 연산과 채움(Closing) 연산을 이용하여 마커(Marker) 정보를 추출하여 워터쉐드 알고리즘을 재적용하여 폐 영역 이미지를 분할하였다. 본 논문에서 제안한 마커 정보를 이용한 워터쉐드 재적용 방식은 폐 영역 효율적이고 정확하게 추출한다.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.9 s.351
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• pp.44-52
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• 2006

Recently developed MPEG-4 Part 2 compression standard provides a novel capability to handle arbitrary video objects. To support this capability, an efficient object segmentation technique is required. This paper proposes a real-time algorithm for foreground object segmentation in video sequences. The proposed algorithm consists of two steps: the first step that segments a video frame into multiple sub-regions using Spatio-Temporal Watershed Transform and the second step in which a foreground object segment is extracted from the sub-regions generated in the first step. For real-time processing, the algorithm is partitioned into hardware and software parts so that computationally expensive parts are off-loaded from a processor and executed by hardware accelerators. Simulation results show that the proposed implementation can handle QCIF-size video at 15 fps and extracts an accurate foreground object.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.1
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• pp.210-216
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• 2010

In this study, we propose a novel method to segment an individual tooth region in a true color image. The difference of the intensity in RGB is initially extracted and subsequent morphological reconstruction is applied to minimize the spurious segmentation regions. Multiple seeds in the tooth regions are chosen by searching regional minima and a Sobel-mask edge operations is performed to apply MCWA(Marker-Controlled Watershed Algorithm). As the results of applying MCWA transform for our proposed tooth segmentation algorithm, the individual tooth region can be resolved in a CCD tooth color image.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.71-74
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• 2012

본 논문에서는 새로운 문자 분할 알고리즘을 제안한다. 고전적인 문자 분할 알고리즘은 학술적인 문서영상과 같이 단순한 구조를 가진 문서영상을 대상으로 하여 좋은 성능을 보였지만 다양한 문자 크기와 색상, 그림, 복잡한 배경 등으로 구성된 문서영상에서는 좋지 못한 성능을 보인다. 최근에 제안고 있는 방법들은 복잡한 문서영상에서도 좋은 성능을 보이도록 다양한 기법들을 적용하여 우수한 성능을 보이고 있지만, 대부분의 방법들이 영상을 일정한 크기의 블록으로 나누어 문자분할을 하기 때문에 세밀한 부분에서는 성능이 어느 정도 한계를 보인다. 따라서 본 논문에서는 블록의 크기에 제한을 갖지 않는 새로운 방법으로서, watershed 알고리즘을 이용한 문자분할 방법을 제시한다. 구체적으로, watershed 알고리즘을 이용하여 문서영상의 구조(docstrum)를 파악하고 이를 기반으로 문자를 분할한다. 제안하는 방법은 크게 엣지 검출, distance transform, watershed 알고리즘을 이용한 docstrum 분석, 문자 분할의 네 단계를 거친다. 실험 결과 블록에 기반한 기존의 방법들이 놓치는 세밀한 부분에서도 제안된 알고리즘은 올바른 분할결과를 얻을 수 있음을 확인하였다.

• Journal of Korea Water Resources Association
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• v.53 no.3
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• pp.155-166
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• 2020

In this study, we applied the Radar-AWS Rainrates (RAR), weather radar-based quantitative precipitation estimations (QPEs), to the Yongdam study watershed in order to perform the flood runoff simulation and calculate the inflow of the dam during flood events using hydrologic model. Since the Yongdam study watershed is a representative area of the mountainous terrain in South Korea and has a relatively large number of monitoring stations (water level/flow) and data compared to other dam watershed, an accurate analysis of the time and space variability of radar rainfall in the mountainous dam watershed can be examined in the flood modeling. HEC-HMS, which is a relatively simple model for adopting spatially distributed rainfall, was applied to the hydrological simulations using HEC-GeoHMS and ModClark method with a total of eight independent flood events that occurred during the last five years (2014 to 2018). In addition, two NCL and Python script programs are developed to process the radar-based precipitation data for the use of hydrological modeling. The results demonstrate that the RAR QPEs shows rather underestimate trends in larger values for validation against gauged observations (R² 0.86), but is an adequate input to apply flood runoff simulation efficiently for a dam watershed, showing relatively good model performance (E_NS 0.86, R² 0.87, and PBIAS 7.49%) with less requirements for the calibration of transform and routing parameters than the spatially averaged model simulations in HEC-HMS.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.670-675
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• 2001

This paper is to propose image segmentation method based on chamfer algorithm. First, we get original image from CCD camera and transform it into gray image. Second, we extract maximum gray value of background and reconstruct and eliminate the background using surface fitting method and bilinear interpolation. Third, we subtract the reconstructed background from gray image to remove noises in gray image. Fourth, we transform the subtracted image into binary image using Otsu's optimal thresholding method. Fifth, we use morphological filters such as areaopen, opening, filling filter etc. to remove noises and isolated points. Sixth, we use chamfer distance or Euclidean distance to this filtered image. Finally, we use watershed algorithm and count microorganisms in image by labeling. To prove the effectiveness, we apply the proposed algorithm to one of Ammonia-oxidizing bacteria, Acinetobacter sp. It is shown that both Euclidean algorithm and chamfer algorithm show over-segmentation. But Chamfer algorithm shows less over-segmentation than Euclidean algorithm.

• Journal of ILASS-Korea
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• v.7 no.4
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• pp.23-31
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• 2002

In this study, the sizes and velocities of droplets in sprays were measured by image processing method from digital images of local region of sprays. The morphological method based on the Euclidean distance transform, Watershed separation, and perimeter image was adopted for the recognition and separation of overlapped particles. The match probability method was used for the particle tracking and pairing. The measurement results show that the present method may be reliable for the analysis of the motion and distribution of droplets produced by spray and atomization devices.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.419-424
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• 2000

The land cover classification by using remotely sensed image becomes necessary and useful for hydrologic and water quality related applications. The purpose of this study is to obtain land classification map by using remotely sensed data : Landsat TM and KOMPSAT-1 EOC. The classification was conducted by maximum likelihood method with training set and Tasseled Cap Transform. The best result was obtain from the Landsat TM merged by KOMPSAT EOC, that is, similar with statistical data. This is caused by setting more precise training set with the enhanced spatial resolution by using KOMPSAT EOC(6.6m${\times}$6.6m).

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05a
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• pp.535-538
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• 2003

본 논문에서는 얼굴 영상으로부터 워터쉐드 변환을 이용하여 3차원 얼굴 모델을 구성하는 방법을 제안한다. 워터쉐드 변환으로 분할된 각각의 영역으로부터 얼굴의 특징점들을 추출하고 MPEG-4에서 정의해놓은 FDP(Facial Definition Parameter)를 기반으로 얼굴 메쉬모델을 생성한다. 워터쉐드 변환시 발생하는 영역 기반의 과분할 결과에서 얻어지는 정확한 정보와 MPEG-4의 FDP를 기반으로 한 Candide Model을 이용함으로써 매우 간편하게 3D 얼굴 모델을 생성할 수 있고 영상 압축 및 전송에 매우 효율적으로 이용될 수 있다.

• Journal of information and communication convergence engineering
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• v.16 no.2
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• pp.114-124
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• 2018

This paper illustrates a vision-based vehicle positioning method at an intersection to support the C-ITS. It removes the minor shadow that causes the merging problem by simply eliminating the fractional parts of a quotient image. In order to separate the occlusion, it firstly performs the distance transform to analyze the contents of the single foreground object to find seeds, each of which represents one vehicle. Then, it applies the watershed to find the natural border of two cars. In addition, a general vehicle model and the corresponding space estimation method are proposed. For performance evaluation, the corresponding ground truth data are read and compared with the vision-based detected data. In addition, two criteria, IOU and DEER, are defined to measure the accuracy of the extracted data. The evaluation result shows that the average value of IOU is 0.65 with the hit ratio of 97%. It also shows that the average value of DEER is 0.0467, which means the positioning error is 32.7 centimeters.