• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.12
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• pp.2953-2960
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• 2014

In the field of image recognition, circle detection is one of the most widely used techniques. Conventional algorithms are mainly based on Hough transform, which is the most straightforward algorithm for detecting circles and for providing enough robust algorithm. However, it suffers from large memory requirements and high computational loads, and sometimes tends to detect incorrect circles. This paper proposes an optimal circle detection and approximation method which is applicable for inspecting fiber optic connector endface. The proposed method finds initial center coordinates and radius based on the initial edge lines. Then, by introducing the simplified K-means algorithm, the proposed method investigates a substitute-circle by minimizing the area of non-overlapped regions. Through extensive simulations, it is shown that the proposed method can improve the error rate by as much as 67% and also can reduce the computing time by as much as 80%, compared to the Hough transform provided by the OpenCV library.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.06a
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• pp.36-37
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• 2014

기존에 영상 내에 원형 검출 방법으로 가장 널리 사용되는 방법은 허프 변환에 기초한다. 허프 변환은 해석적 곡선의 각 점을 원의 중심 좌표와 반지름으로 매핑 시키는 과정을 포함한다. 이러한 과정은 실행시간을 매우 많이 필요로 하고 또한 응용에 따라서 최적인 원 근사화 방법을 찾는데 문제점을 야기하기도 한다. 본 논문에서는 원형 모양인 광 연결 소자 장치로 제한된 응용환경에 대해 원 검출을 빠른 속도로 탐색하는 방법과 최적인 원 근사화 방법을 제안한다. 제안한 방법은 에지 검출과 검출된 에지를 이용한 중심좌표 및 반지름 탐색 그리고 최적화된 원 근사화 방법으로 구성된다. 모의실험을 통하여 제안한 방법은 기존의 오픈라이브러리로 제공되는 OpenCV의 허프 변환에 의한 방법에 비해 원 검출 및 근사화 방법에 있어 성능을 개선할 수 있음을 보인다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.167-170
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• 2010

In this paper, we proposed a novel algorithm to detect the center of pupil in frontal view face. This algorithm, at first, extract an eye region from the face image using integral projection function and variance projection function. In an eye region, detect the center of pupil positions using circular hough transform with sobel edge mask. The experimental results show good performance in detecting pupil center from FERET face image.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.135-142
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• 2020

In this paper, we describe the iris localization method in RGB images. Most of the iris localization methods are developed for infrared images, thus an iris localization method in RGB images is required for various applications. The proposed method consists of four stages: i) detection of the candidate irises using circular Hough transform (CHT) from an input image, ii) detection of a pupil center based on deep learning, iii) determine the iris using the pupil center, and iv) correction of the iris region. The candidate irises are detected in the order of the number of intersections of the center point candidates after generating the Hough space, and the iris in the candidates is determined based on the detected pupil center. Also, the error due to distortion of the iris shape is corrected by finding a new boundary point based on the detected iris center. In experiments, the proposed method has an improved accuracy about 27.4% compared to the CHT method.

• Korean Journal of Construction Engineering and Management
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• v.23 no.2
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• pp.45-53
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• 2022

The purpose of this study is to present a model for analyzing construction errors of reinforced concrete pile foundations using drones. First, a drone is used to obtain an aerial image of the construction site, and an orthomosaic image is generated based on those images. Then, the circular pile foundation is automatically recognized from the orthomosaic image by using the Hough transform circle detection method. Finally, the distance is calculated based on the the center point of the reinforced concrete pile foundation in the overlapped data. As a case study, the proposed concrete concrete pile foundation construction quality control model was applied to the real construction site in Incheon to evaluate the proposed model.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.267-270
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• 2001

홍채인식 시스템에서 입력된 영상으로부터 정합(matching)에 사용될 홍채 영역을 추출해 내는 과정은 필수적인 과정으로 빠른 처리 속도와 정확성을 요구한다. 기존의 원형검출기나 허프(Hough) 변환을 이용한 방법 등은 홍채의 바깥쪽과 안쪽 경계를 비교적 정확하게 검출해내는 장점이 있으나 탐색영역이 커서 수행시 간이 매우 많이 걸리는 단점이 있다. 따라서 본 논문에서는 이진화와 형태학적 연산(morphology)을 이용하는 새로운 탐색 영역 단축 방법을 제안한다. 제안한 방법은 기존의 홍채영역 검출 방법에 적용할 경우 수행 시간을 효율적으로 단축시킬 수 있다. 검출된 영역에 대해서 주성분 분석법(principal component analysis, PCA)을 이용해 매칭을 수행한 결과 약 95%의 인식율을 나타내었다.

• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.41-49
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• 2012

This paper proposes a detection method of wipes including horizontal wipes with linear moving borders, such as horizontal or vertical wipes, Barn Doors, and Iris Rounds with circular moving borders. The suggested method first obtains a difference image between two adjacent frames, and extracts lines and circles by applying Hough transformation to the extracted difference image. Then, we detect wipe transitions by employing an evaluation function that analyzes the number of moving trajectories of lines or circles, their moving direction and magnitude. To evaluate the performance of the suggested algorithm, experimental results show that the proposed method can effectively detect wipe transitions with linear and circular moving borders rather than some existing methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.3184-3190
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• 2011

In this paper, we propose a method to detect Iris Round wipe transitions with circular moving borders in digital video data. The suggested method robustly extracts circular moving borders from the input image using improved Hough transform, and finally detects Iris Round wipes by effectively analyzing their moving directions and shapes. In order to evaluate the performance of the suggested algorithm, the experimental results show that the proposed method can effectively detect Iris Rounds with circular moving borders in various video data.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1386-1389
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• 2013

조영증강 초음파 동영상은 측정 부위의 혈류의 형태와 특성 정보를 포함하지만, 이를 육안으로 판별하는 것은 매우 어려운 일이다. 이에 본 연구에서는 초음파 영상으로부터 간 질환과 연관한 병변을 추출하고 그 특성을 분석하기 위한 기반 기술로서 영상 처리 및 분석 기법을 제안한다. 마이크로버블(microbubble) 형태의 노이즈와 빠른 속도로 변화하는 각 프레임의 영상으로부터 근사화된 원형 또는 타원 형태로 나타나는 병변을 추출하기 위하여 허프 변환(Hough transform)을 이용한 병변 추출 기법을 제시하며, 추출한 병변의 특성을 분석하기 위하여 TIC(time intensity curve)의 특성과 조영 효과의 전이 시간(transit time) 등의 정보를 영상의 형태로 가시화하는 방법과 MRF(Markov random field) 모델을 이용한 영상개선 기법을 소개한다. 제안된 이론은 실제 간 질환 진단 초음파 영상에 적용하여 그 유용성을 평가한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1950-1956
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• 2017

In food-printing field, precise positioning technique for a printing object is very important. In this paper, we propose cup positioning method for a latte-art printer through image processing. A camera sensor is installed on the upper side of the printer, and the image obtained from this is projected and converted into a top-view image. Then, the edge lines of the image is detected first, and then the coordinate of the center and the radius of the cup are detected through a Circular Hough transformation. The performance evaluation results show that the image processing time is 0.1 ~ 0.125 sec and the cup detection rate is 92.26%. This means that a cup is detected almost perfectly without affecting the whole latte-art printing time. The center point coordinates and radius values of cups detected by the proposed method show very small errors less than an average of 1.5 mm. Therefore, it seems that the problem of the printing position error is solved.