• 전기학회논문지
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• 제61권3호
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• pp.472-478
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• 2012

The inspection machine in PCB (printed circuit board) assembly line checks assembly errors by inspecting the images inside of the component inspection region. The component inspection region consists of region of component package and region of soldering. It is necessary to extract the regions automatically for auto-teaching system of the inspection machine. We propose an image segmentation method to extract the component inspection regions automatically from images of PCB. The acquired image is transformed to HSI color model, and then segmented by several regions by clustering method. We develop a modified K-means algorithm to increase the accuracy of extraction. The heuristics generating the initial clusters and merging the final clusters are newly proposed. The vertical and horizontal projection is also developed to distinguish the region of component package and region of soldering. The experimental results are presented to verify the usefulness of the proposed method.

• 한국지반환경공학회 논문집
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• 제17권8호
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• pp.39-44
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• 2016

도심지와 같이 공사 현장에 인접 구조물이 많은 경우, 지반 구조물의 안정성 확보와 더불어 지반의 변형 역시 엄격하게 관리해야 한다. 따라서 공사 중 현장에서 발생하는 지반의 침하를 정확하게 계측하는 것은 매우 중요하다. 지반의 침하는 침하계를 이용하여 계측하는 것이 일반적이나, 최근 전자기술의 발달로 3차원 스캔이 가능한 장치들을 지반 침하 계측에 사용하고 있다. 그러나 이 3차원 스캔장치의 경우 지반 침하의 전체적인 양상을 평가하기는 용이하나 직접 침하를 측정하지 않아 정밀도에 있어서 한계가 있다. 또한, 침하계의 경우 침하계가 설치된 지점에서만 침하값을 측정하기 때문에 전체적인 침하의 양상을 평가하는 데는 한계가 있다. 본 논문에서는 침하계가 측정한 값과 스캐너가 측정한 값을 합성하는 조건부 합성 기법에 대해 연구하였다. 가상의 침하양상과 이를 바탕으로 가상의 스캔한 침하 양상을 생성시켜 연구를 진행하였다. 조건부 합성을 통해 침하 양상의 오차를 획기적으로 줄일 수 있는 것으로 나타났다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.211-230
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• 2022

In image segmentation, for the condition that objects (targets) and background in an image are intertwined or their common boundaries are vague as well as their textures are similar, and the targets in images are greatly variable, the deep learning might be difficult to use. Hence, a new method based on graph theory and guided feathering is proposed. First, it uses a guided feathering algorithm to initially separate the objects from background roughly, then, the image is separated into two different images: foreground image and background image, subsequently, the two images are segmented accurately by using the improved graph-based algorithm respectively, and finally, the two segmented images are merged together as the final segmentation result. For the graph-based new algorithm, it is improved based on MST in three main aspects: (1) the differences between the functions of intra-regional and inter-regional; (2) the function of edge weight; and (3) re-merge mechanism after segmentation in graph mapping. Compared to the traditional algorithms such as region merging, ordinary MST and thresholding, the studied algorithm has the better segmentation accuracy and effect, therefore it has the significant superiority.

• 한국측량학회지
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• 제9권2호
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• pp.103-113
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• 1991

SPOT 수치영상자료를 이용한 지상좌표 계산 과정에서 지상기준점 및 검사점 입럭좌표의 정확도는 계산 결과의 신빙성에 커다란 영향을 준다. CRT 모니터상에 직접 나타난 SPOT 원초 영상은 일반적으로 지상물체의 판별과 점위치 결정에 적합하지 않으므로 전체영상의 대비개선, 영상소보간, 경계선강조, 공간필터처리 등 적절한 영상처리 기병의 적용이 필요하다. 본 연구에서는 대상지역의 3차원 위치 결정과 파장대특성분석에 이용되는 SPOT 위성영상의 시각분석에 필요한 수치영상처리기법의 원리를 고찰하고, 그 적용을 위한 알고리즘을 개발하여 프로그래밍 하였으며, 실제 P-mode 및 XS-mode의 SPOT 영상을 이용하여 고해상도 천연색영상인 SPOT P＋XS 영상으로 합성하였다.

• Journal of information and communication convergence engineering
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• 제16권2호
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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.

• 한국멀티미디어학회논문지
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• 제22권12호
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• pp.1404-1414
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• 2019

Educational books commonly use some copyrighted images with various kinds of deformation for helping students understanding. When using several copyrighted images made by merging or editing distortion in legal, we need to pay a charge to original copyright holders for each image. In this paper, we propose an efficient matching algorithm by separating each copyrighted image with the merged and edited type including rotation, illumination change, and change of size. We use the Oriented FAST and Rotated BRIEF (ORB) method as a basic feature matching scheme. To improve the matching accuracy, we design a new MIN-MAX similarity in matching stage. With the distorted dataset, the proposed method shows up-to 97% of precision in experiments. Also, we demonstrate that the proposed similarity measure also outperforms compared to other measure which is commonly used.

• 대한원격탐사학회지
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• 제16권4호
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• pp.367-373
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• 2000

Natural and artificial land features are very dynamic, changing somewhat repidly in our lifetime. It is important that such changes are inventoried accurately so that the physical and human processes at work can be more fully understood. Change detection is a technique used to determine the change between two or more time periods of a particular object of study. Change detection is an important process in monitoring and managing natural resources and urban development because it provides quantitative analysis of the spatial distribution in the population of interest. The purpose of this research is to detect environmental changes surrounding an area of Mountain Moscow, Idaho using Landsat Thematic Maper (TM) images of (July 8, 1990 and July 20, 1991). For accurate classification, the Image enhancement process was performed for improving the image quality of each image. A SPOT image (Aug. 14, 1992) was used for image merging in this research. Supervised classification was performed using the maximum likelihood method. Accuracy assessments were done for each classification. Two images were compared on a pixel-by-pixel basis using the post-classification comparison method that is used for detecting the changes of the study area in this research. The 'from-to' change class information can be detected by post classification comparison using this method and we could find which class change to another.

• Journal of Information Processing Systems
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• 제19권1호
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• pp.109-117
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• 2023

Defect detection is one of the key factors in fabric quality control. To improve the speed and accuracy of denim fabric defect detection, this paper proposes a defect detection algorithm based on cascading feature extraction architecture. Firstly, this paper extracts these weight parameters of the pre-trained VGG16 model on the large dataset ImageNet and uses its portability to train the defect detection classifier and the defect recognition classifier respectively. Secondly, retraining and adjusting partial weight parameters of the convolution layer were retrained and adjusted from of these two training models on the high-definition fabric defect dataset. The last step is merging these two models to get the defect detection algorithm based on cascading architecture. Then there are two comparative experiments between this improved defect detection algorithm and other feature extraction methods, such as VGG16, ResNet-50, and Xception. The results of experiments show that the defect detection accuracy of this defect detection algorithm can reach 94.3% and the speed is also increased by 1-3 percentage points.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권6호
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• pp.2826-2840
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• 2018

Traditional methods based active contours or region merging are powerless in processing images with blurring border or hair occlusion. In this paper, a structure based convolutional neural networks is proposed to solve segmentation of skin lesion image. The structure mainly consists of two networks which are segmentation net and discrimination net. The segmentation net is designed based U-net that used to generate the mask of lesion, while the discrimination net is designed with only convolutional layers that used to determine whether input image is from ground truth labels or generated images. Images were obtained from "Skin Lesion Analysis Toward Melanoma Detection" challenge which was hosted by ISBI 2016 conference. We achieved segmentation average accuracy of 0.97, dice coefficient of 0.94 and Jaccard index of 0.89 which outperform the other existed state-of-the-art segmentation networks, including winner of ISBI 2016 challenge for skin melanoma segmentation.

• 대한원격탐사학회지
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• 제36권5_2호
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• pp.949-959
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• 2020

본 논문에서는 CAS500-1/2를 이용한 산출물 생성에 관련한 선행연구로 KOMPSAT-3A 스트립영상을 사용한 비접근 지역의 도엽별 DSM을 생성하는 방법을 제안한다. 제안 기법은 1:5000 도엽정보의 입력을 통한 영역 설정을 통해 도엽별로 산출물이 나올 수 있도록 설계됐으며, 강인한 스테레오 영상정합 기법인 MDR을 적용하여 스테레오 페어에서도 최적의 DSM이 나오도록 설정했다. 스트립 영상이 분할된 여러 장의 단위 영상으로 들어오는 것을 고려하여 하나의 도엽에 여러 쌍의 영상 페어를 처리하여 통합하는 방식으로 DSM의 생성을 시도했으며, 처리결과 도엽 간의 접합부분에서 이격 발생을 최소화한 DSM의 생성이 가능함을 확인할 수 있었다. 최종적으로 GCP와의 비교를 통한 정확도 검증결과 5 m 이내의 정확도가 나타나는 것을 확인할 수 있었다.