• 한국산업융합학회 논문집
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• 제22권3호
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• pp.293-301
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• 2019

Based on accurate image processing technology, a system for measuring displacement in ${\mu}m$ for drive error (position error, straightness error, flatness error) at a distance using parallel light and image sensor is developed, and a system for applying this technology development to a large 3D rapid prototyping machine and compensating in real time is developed to dramatically reduce the range of measurement error and enable intelligent 3D production of high quality products.

• 한국소음진동공학회논문집
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• 제15권3호
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• pp.329-333
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• 2005

This paper is concerned with the measurement of low-frequency vibrations of structures using the image processing method. To measure the vibrations visually, the measurement system consists of a camera, an image grabber board, and a computer. The specific target installed on the structure is used to calculate the vibration of structure. The captured image is then converted into a pixel-based data and then analyzed numerically. The limitation of the system depends on the image capturing speed and the size of image. In this paper, we propose the methodology for the vibration measurement using the image processing method. The method enables us to measure the displacement directly without any contact. The current resolution of the vibration measurement is limited to sub centimeter scale. However, the frequency bandwidth and resolution can be enhanced by a high-speed and high-resolution image processing system.

• Journal of information and communication convergence engineering
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• 제10권2호
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• pp.194-199
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• 2012

This paper proposes a novel scheme of a gray scale fingerprint image for a high-accuracy capacitive sensor chip. The conventional grayscale image scheme uses a digital-to-analog converter (DAC) of a large-scale layout or charge-pump circuit with high power consumption and complexity by a global clock signal. A modified capacitive detection circuit for the charge sharing scheme is proposed, which uses a down literal circuit (DLC) with a floating-gate metal-oxide semiconductor transistor (FGMOS) based on a neuron model. The detection circuit is designed and simulated in a 3.3 V, 0.35 ${\mu}m$ standard CMOS process. Because the proposed circuit does not need a comparator and peripheral circuits, the pixel layout size can be reduced and the image resolution can be improved.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2002년도 학술발표회 논문집(II)
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• pp.982-988
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• 2002

• Korean Journal of Geomatics
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• 제1권1호
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• pp.7-13
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• 2001

Geospatial Information Systems (GIS) have been employed to systematically manage and design land use in urban areas. This has increased the need for more accurate vector and raster data. In Korea, l/l,000-scale digital maps are used as vector data for the facility management in urban areas. This has increased the need for large scale orthoimages. Orthoimages generated from aerial imagery can provide accurate information, making possible the more effective city management. However, there is a large problem in using the orthoimages, i.e., currently available conventional orthoimages have not been generated based on Digital Elevation Model (DEM) that takes into account the building heights. So this causes the displacements of building image in large scale orthoimages. The present study is an attempt to generate the large scale orthoimages based on building DEM. The semiautomatic building extraction method can detect building outlines by mouse clicking on either building roofs or corners. Building DEM, based on the outline and calculated building height, was used to produce the large scale orthoimages with the corrected building occlusion.

• 대한원격탐사학회지
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• 제36권6_1호
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• pp.1393-1405
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• 2020

무인항공기(UAV: Unmanned Aerial Vehicle)는 저고도 비행으로 인해 고해상도 영상을 취득할 수 있으며, 수시촬영이 가능하여 지도제작에 있어 수시갱신이 가능하다. 이러한 이점으로 인해 무인항공기 영상을 이용한 대축척 수치지도 제작 가능성에 대한 연구가 활발히 진행되고 있다. 정밀한 수치지도는 디지털트윈이나 스마트시티의 기반 데이터로 활용될 수 있다. 정밀한 수치지도를 제작하기 위해서는 지상기준점을 이용한 정밀 센서모델링이 반드시 선행되어야 한다. 본 연구에서는, 자체 개발한 정밀 센서모델링 알고리즘을 통해 무인항공기 영상의 기하모델을 수립하였다. 그리고 수치지도를 제작하여 대축척 수치도화 가능성을 평가하였다. 연구 데이터는 인천 간석동과 서울 여의도를 대상으로 영상 및 지상기준점을 취득하였다. 정밀 센서모델링 정확도 분석 결과, 두 지역에 대해서 체크 점 평균오차 3 픽셀 이내, RMSE 4 픽셀 이내의 높은 정확도를 확인하였다. 수치도화 정확도 분석 결과, 국토지리정보원에서 고시한 1:1,000 세부도화 수평오차(0.4 m) 및 표고오차(0.4 m)를 만족하는 범위의 정확도를 확인하였다. 따라서 본 연구에서 자체개발한 정밀 센서모델링 기술은 무인항공기 영상의 1:1,000 대축척 수치도화 제작 가능성을 시사한다.

• Journal of Korean Medical Science
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• 제33권43호
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• pp.239.1-239.12
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• 2018

Background: We described a novel multi-step retinal fundus image reading system for providing high-quality large data for machine learning algorithms, and assessed the grader variability in the large-scale dataset generated with this system. Methods: A 5-step retinal fundus image reading tool was developed that rates image quality, presence of abnormality, findings with location information, diagnoses, and clinical significance. Each image was evaluated by 3 different graders. Agreements among graders for each decision were evaluated. Results: The 234,242 readings of 79,458 images were collected from 55 licensed ophthalmologists during 6 months. The 34,364 images were graded as abnormal by at-least one rater. Of these, all three raters agreed in 46.6% in abnormality, while 69.9% of the images were rated as abnormal by two or more raters. Agreement rate of at-least two raters on a certain finding was 26.7%-65.2%, and complete agreement rate of all-three raters was 5.7%-43.3%. As for diagnoses, agreement of at-least two raters was 35.6%-65.6%, and complete agreement rate was 11.0%-40.0%. Agreement of findings and diagnoses were higher when restricted to images with prior complete agreement on abnormality. Retinal/glaucoma specialists showed higher agreements on findings and diagnoses of their corresponding subspecialties. Conclusion: This novel reading tool for retinal fundus images generated a large-scale dataset with high level of information, which can be utilized in future development of machine learning-based algorithms for automated identification of abnormal conditions and clinical decision supporting system. These results emphasize the importance of addressing grader variability in algorithm developments.

• 한국자동차공학회논문집
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• 제14권3호
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• pp.142-149
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• 2006

A PIV(Particle Image Velocimetry) was applied to measure in-cylinder velocity field according to inlet valve angle during intake stroke. Two engines, one is conventional DOHC 4 valve and the other is narrow valve angle, were used to compare real intake flow. The results show that the intake flow pattern of conventional engine is more complicated than that of narrow angle one in horizontal plane and the vertical component of in-cylinder flow is rapidly decayed at the end stage of intake. On the other hand, the flow pattern of narrow angle one is relatively well arranged in horizontal plane and the vertical velocity component remains so strongly that forms large-scale strong tumble. Two engines also form commonly three tumble; two are small and bellow the intake valve and one is large-scale. The center of large scale tumble moves to bottom of cylinder as the vertical velocity increases.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.149-158
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• 2024

Traditional manual identification of crop leaf diseases is challenging. Owing to the limitations in manpower and resources, it is challenging to explore crop diseases on a large scale. The emergence of artificial intelligence technologies, particularly the extensive application of deep learning technologies, is expected to overcome these challenges and greatly improve the accuracy and efficiency of crop disease identification. Crop leaf disease identification models have been designed and trained using large-scale training data, enabling them to predict different categories of diseases from unlabeled crop leaves. However, these models, which possess strong feature representation capabilities, require substantial training data, and there is often a shortage of such datasets in practical farming scenarios. To address this issue and improve the feature learning abilities of models, this study proposes a deep transfer learning adaptation strategy. The novel proposed method aims to transfer the weights and parameters from pre-trained models in similar large-scale training datasets, such as ImageNet. ImageNet pre-trained weights are adopted and fine-tuned with the features of crop leaf diseases to improve prediction ability. In this study, we collected 16,060 crop leaf disease images, spanning 12 categories, for training. The experimental results demonstrate that an impressive accuracy of 98% is achieved using the proposed method on the transferred ResNet-50 model, thereby confirming the effectiveness of our transfer learning approach.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.631-634
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• 2006

Three main important sources for establishing GIS are the orthomap in scale 1:5 000 with Ground Sampling Distance of 0,5m; DEM/DTM data with height error of ${\pm}$1,0m and topographic map in scale 1: 10 000. The new era with Very High Resolution Satellite (VHRS) images as IKONOS, QuickBird, EROS, OrbView and other ones having Ground Sampling Distance (GSD) even lower than 1m has been in potential for producing orthomap in large scale 1:5 000, to update existing maps, to compile general-purpose or thematic maps and for GIS. The accuracy of orthomap generated from VHRS image affects strongly on GIS reliability. Nevertheless, orthomap accuracy taken from VHRS image is at first dependent on chosen sensor geometrical models. This paper presents, at fist, theoretical basic of the Rational Polynomial Coefficient (RPC) model installed in the commercial ImageStation Systems, realized for orthorectifying VHRS images. The RPC model of VHRS image is a replacement camera mode that represents the indirect relation between terrain and its image acquired on the flight orbit. At the end of this paper the practical accuracies of IKONOS and QuickBird image orthorectified by RPC model on Canadian PCI Geomatica System have been presented. They are important indication for practical application of producing digital orthomaps.