• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.338-341
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• 2013

눈동자 검출은 운전 부주의 검출, 졸음 검출, 시선 검출 등 다양한 상황 인지에 이용되고 있다. 이러한 상황 인지를 위해 본 논문에서는 원 허프 변환(Hough Circle Transform)을 이용한 눈동자 검출방법을 제안한다. 이것은 영상 내 원을 검출하는 방법으로 연산량이 많아 실시간 처리에 문제가 된다. 이러한 문제를 해결하기 위해 눈 검출 후 눈 영역의 크기를 일정한 눈 크기로 정규화 하고 눈의 양쪽 끝점간 거리에 따른 대략적인 눈동자의 반지름 값 범위를 추정한다. 그 추정된 반지름 값 범위 내에서 Hough Circle Transform을 수행하면 연산량의 축소가 가능하며 그 결과 초당 21frames 정도의 눈동자 검출이 가능하였다.

• Korean Journal of Optics and Photonics
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• v.9 no.5
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• pp.321-325
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• 1998

We explain that a holographic filter of the generalized Hough transform can be easily obtained by use of rotational multiplexing in hologram recording. To show the feasibility of our approach experimentally, we recorded the Hough transform filter of both line and circle parameterization by combined use of rotational and angle multiplexing. Experimental results on the Hough transform for a few input patterns are presented.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.5-8
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• 2000

In this paper, a technique to estimate the circular object's center and radius under noisy condition is described. The technique is based on Davies'Hough transform approach to circular object location but more robust to noise and faster to estimate the circle by using an area of cumulated points.

• Journal of Internet Computing and Services
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• v.11 no.6
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• pp.31-39
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• 2010

The Generalized Hough Transform(GHT) is the most used algorithm for circle detection with high accuracy. However, it requires many computation time, because many different templates are applied in order to find circles of various size. In the case of circle detection and tracking in video, the classical approach applies GHT for each frame in video and thus needs much high processing time for all frames. This paper proposes the fast GHT algorithm in video, using two consecutive frames are similar. In the proposed algorithm, a change-driven method conducts GHT only when two consecutive frames have many changes, and trajectory-based method does GHT in candidate areas and with candidate radius using circles detected in a previous frame. The algorithm can reduce computation time by reducing the number of frames, the edge count, and the number of searching circles, as factors which affects the speed of GHT. Our experimental results show that the algorithm successfully detects circles with less processing time and no loss of accuracy in video acquisited by a fixed camera and a moving camera.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.90-93
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• 2013

This paper presents a multistage adaptive thresholding method and circular Hough transform for pupil detection. Multistage adaptive thresholding is a thresholding method that applies local image statistic within a neighborhood variable and the global thresholds. Therefore, the method can adopt the benefit of local thresholding and prevent an over segmentation at the same time because of the global image information. To detect a pupil, a circular Hough transform is applied to it in which the pupil pattern is considered as a circle shape. The experimental results show the reliability of our proposed method in detecting pupil properly.

• 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.

• Journal of Korea Multimedia Society
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• v.17 no.4
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• pp.413-419
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• 2014

In this paper, we develop an automatic coin calculation system by using digital image processing. Existing schemes have the problem that is not able to exclude non-circular shape from the calculation. We propose a method to detect only coins which have circular form by applying the circular Hough transform(CHT). However, the CHT has the drawback that detects multiple circles even for just one coin because of shadow noise, the patterns on coins, and non-circular edge detection. We propose a post processing algorithm to overcome these limitations. The proposed system was implemented and successfully calculated the coin amount in the case that non-circular objects are mixed with coins.

• Journal of the Korea Society of Computer and Information
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• v.23 no.2
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• pp.27-34
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• 2018

In this paper, we propose a new method to read the hand of analog gauges to prepare for the smart factory. In addition, we suggest a new and improved method that can apply, in general, diverse analog gauges even if their scale types and ranges are various. Many companies are making great efforts to build smart factories that increase energy efficiency and automation. Managers use a variety of equipment and tools to manage the production process at the factory. In this kind of factory, analog gauges have been often used with many equipment and tools. Analog gauges are mostly circular in shape, and most papers use circular hough transform to find the center and radius of a circle. However, when the object to be found is not of the correct circle type, it takes a long time to recognize the circle using the circular hough transform, and the center and radius of the circle can not be calculated accurately. The proposed method was tested on various circular analog gauges. As a result, we confirmed that our method is outstanding.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.9
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• pp.142-149
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• 1994

Hangul is expressed by the basic elements, twenty-four characters. Because these characters are composed of a circle and lines, Hough transform(HT), which has a powerful performance on the noise in extracting lines, is introduced. Many difficulties often occur when the original HT is used to extract strokes and it's direction, position and length from handwritten Hangul characters. Original HT has eight direction selected as samples in the transformed image should be calculated for these eight directions. In this paper, the generalized sampling rule is suggested. According to the rule, those directions which are possible to a line are the only thing to be calculated. The experoment result turned out to be higher than the method that Chen suggested in sampling rate. Anogher experiment result is done on the 1800 handwritten Hangul characters that 10 persons wrote. By feature extracting the oritinal HT and sampling HT. And as a result of six type classification, the suggested method came out higher than original HT.

• Journal of Korea Multimedia Society
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• v.16 no.12
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• pp.1385-1392
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• 2013

There is a variety of researches about recognition system using biometric data these days. In this study, we propose a new algorithm, uses simultaneous equation that made of the edge of objects, to segment an iris region without threshold values from an anterior eye image. The algorithm attempts to find a center area through calculated outskirts information of an iris, and decides the area where the most points are accumulated. To verify the proposed algorithm, we conducted comparative experiments to Hough transform and Daugman's method, based on 50 images anterior eye images. It was found that proposed algorithm is 5 and 75 times faster than on each algorithm, and showed high accuracy of detecting a center point (95.36%) more than Hough transform (92.43%). In foreseeable future, this study is expected to useful application in diverse department of human's life, such as, identification system using an iris, diagnosis a disease using an anterior image.