• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.2037-2038
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• 2011

피부에 나타나는 난치성 질환인 백반증은 심리적인 위축감을 주어 정상적인 생활에 지장을 줄 수 있는 질병이다. 이에 따라서 본 연구에서는 피부에 나타나는 백반증의 진행 상태를 판단하기 위하여 L*a*b* 컬러 공간으로 변환된 피부 영상에 Otsu 임계값 설정 기법을 적용하여 백반증의 발병 영역을 자동으로 판별하는 알고리즘을 제안하였다.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.535-544
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• 2021

Agricultural water occupies 48% of water demand, and management of agricultural reservoirs is essential for water resources management within agricultural basins. For more efficient use of agricultural water, monitoring the distribution of water resources in agricultural reservoirs and agricultural basins is required. Therefore, in this study, three threshold determination methods (i.e., fixed threshold, Otsu threshold, Kittler-Illingworth (KI) threshold) were compared to detect terrestrial water bodies using Sentinel-1 images for 3 years from 2018 to 2020. The purpose of this study was to evaluate methods for determining threshold values to more accurately estimate the reservoir area. In addition, by analyzing the relationship between the water surface and water storage at the Edong, Gosam, and Giheung reservoirs, water storage based on the SAR image was estimated and validated with observations. The thresholding method for detecting a waterbody was found to be the most accurate in the case of the KI threshold, and the water storage estimated by the KI threshold indicated a very high agreement (r = 0.9235, KGE' = 0.8691). Although the seasonal error characteristics were not observed, the problem of underestimation at high water levels may occur; the relationship between the water surface and the water storage could change rapidly. Therefore, it is necessary to understand the relationship between the water surface area and water storage through ground observation data for a more accurate estimation of water storage. If the use of SAR data through water resources satellites becomes possible in the future, based on the results of this study, it is judged that it will be beneficial for monitoring water storage and managing drought.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2924-2932
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• 2015

Image binarization is a process to divide the image into objects and backgrounds, widely applied to the fields of image analysis and its recognition. In the existing method of binarization, there is some uncertainty when there is insufficient brightness gap between objects and backgrounds in setting threshold. The method of fuzzy binarization has improved the features of objects efficiently. However, since this method sets ${\alpha}$-cut value statically, there remain some problems that important features of objects can be lost during binarization. Therefore, in this paper, we propose a binarization method which does not set ${\alpha}$-cut value statically. The proposed method uses fuzzy membership functions calculated by thresholds of mean, iterative, and Otsu binarization. Experiment results show the proposed method binaries various images with less loss than the existing methods.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.3
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• pp.42-50
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• 2008

We developed a visual inspection system for detecting defective products. Most existing inspection systems are designed to be dedicated to one product, which makes operator spend extra money and time to adopt other products. In this work, we propose a flexible visual inspection system that can inspect various products without any additional major job at a low-cost. The developed system contained image processing algorithm libraries and user-friendly graphic interface for adaptable image-based inspection system. We can find a proper threshold value using the proposed algorithm which uses correlation coefficient between a non-defective product and existing sample images of defective product. And We tested the performance of the proposed algorithm using Otsu's method. The proposed system is applied to a automated inspection line for cellular phone.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.543-548
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• 2011

Entropy measuring the richness in details of the image is generally obtained by using the histogram of gray levels in an image, and has been widely used as an index for thresholding of the image. In this paper, we propose an entropy-based thresholding method, where the entropy is obtained not by the histogram but by the variance of the gray levels, to binalize a given image. The effectiveness of the proposed method is demonstrated by thresholding experiments on nine test images and comparison with conventional two thresholding methods, that is, Otsu method and entropy-based method using the histogram.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.1
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• pp.45-52
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• 2015

The interoperability of multi-source data has become more important due to various digital maps, produced from public institutions and enterprises. In this study, the automatic matching algorithm of multi-source building data using hierarchical matching was proposed. At first, we divide digital maps into blocks and perform the primary geometric registration of buildings with the ICP algorithm. Then, corresponding building pairs were determined by evaluating the similarity of overlap area, and the matching threshold value of similarity was automatically derived by the Otsu binary thresholding. After the first matching, we extracted error matching candidates buildings which are similar with threshold value to conduct the secondary ICP matching and to make a matching decision using turning angle function analysis. For the evaluation, the proposed method was applied to representative public digital maps, road name address map and digital topographic map 2.0. As a result, the F measures of matching and non-matching buildings increased by 2% and 17%, respectively. Therefore, the proposed method is efficient for the matching of building polygons from multi-source digital maps.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.908-911
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• 2004

In this paper, we describe a robust image processing algorithm to recognize the road lane in real-time. For the real-time processing, a detection area is decided by a lane segment of a previous frame and edges are detected on the basis of the lane width. For the robust driving, the global threshold with the Otsu algorithm is used to get a binary image in a frame. Therefore, reliable edges are obtained from the algorithms suggested in this paper in a short time. Lastly, the lane segment is found by hough transform. We made a RC(Radio Control) car equipped with a vision system and verified these algorithms using the RC Car.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.870-872
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• 2015

본 연구에서는 웨이퍼의 자동광학검사를 위한 결점 검출 비전 시스템을 개발하였다. 성공적 결점 검출을 위해 몇 가지 이진화 방법을 비교하였고, bimodal과 unimodal 분포에 모두 좋은 결과를 나타낸 개선된 Otsu 방법을 선택하였다. 빠르고 정확한 임계값 계산을 위해 ROI 추출기능을 개발하였으며 최종적으로 웨이퍼의 검출 패턴은 정의된 기준에 따라 영상 분류되었고 성능평가를 위해 14개 이상의 웨이퍼 영상으로 테스트하였다.

• Smart Media Journal
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• v.6 no.3
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• pp.15-20
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• 2017

Recently, the automotive industry has been studied about driver assistance systems for helping drivers to drive their cars easily by integrating them with the IT technology. This study suggests a method of detecting lanes, robust to road condition changes and applicable to lane departure warning and autonomous vehicles mode. The proposed method uses the method of detecting candidate areas by using the Gaussian filter and by determining the Otsu threshold value and edge. Moreover, the proposed method uses lane gradient and width information through the Hough transform to detect lanes. The method uses road lane information detected before to detect dashed lines as well as solid lines, calculates routes in which the lanes will be located in the next frame to draw virtual lanes. The proposed algorithm was identified to be able to detect lanes in both dashed- and solid-line situations, and implement real-time processing where applied to Raspberry Pi 2 which is open source hardware.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.223-235
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• 2020

In this study, we propose an approach for calibrating the elevation of a DEM (Digital Elevation Model), one of the key data in realizing unmanned aerial vehicle image-based precision agriculture. First of all, radiometric correction is performed on the orthophoto, and then ExG (Excess Green) is generated. The non-vegetation area is extracted based on the threshold value estimated by applying the Otsu method to ExG. Subsequently, the elevation of the DEM corresponding to the location of the non-vegetation area is extracted as EIFs (Elevation Invariant Features), which is data for elevation correction. The normalized Z-score is estimated based on the difference between the extracted EIFs to eliminate the outliers. Then, by constructing a linear regression model and correcting the elevation of the DEM, high-quality DEM is produced without GCPs (Ground Control Points). To verify the proposed method using a total of 10 DEMs, the maximum/minimum value, average/standard deviation before and after elevation correction were compared and analyzed. In addition, as a result of estimating the RMSE (Root Mean Square Error) by selecting the checkpoints, an average RMSE was derivsed as 0.35m. Comprehensively, it was confirmed that a high-quality DEM could be produced without GCPs.