• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.175-182
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• 2010

The paper presents a novel algorithm for face and iris detection with the application for driver iris monitoring. The proposed algorithm consists of the following major steps: Skin-color segmentation, facial features segmentation, and iris positioning. For the skin-segmentation we applied a multi-layer perceptron to approximate the statistical probability of certain skin-colors, and filter out those with low probabilities. The next step segments the face region into the following categories: eye, mouth, eye brow, and remaining facial regions. For this purpose we propose a novel segmentation technique based on estimation of facial class probability density functions (PDF). Each facial class PDF is estimated on the basis of salient features extracted from a corresponding facial image region. Then pixels are classified according to the highest probability selected from four estimated PDFs. The final step applies the circular Hough transform to the detected eye regions to extract the position and radius of the iris. We tested our system on two data sets. The first one is obtained from the Web and contains faces under different illuminations. The second dataset was collected by us. It contains images obtained from video sequences recorded by a CCD camera while a driver was driving a car. The experimental results are presented, showing high detection rates.

• Journal of Broadcast Engineering
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• v.17 no.6
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• pp.1040-1047
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• 2012

In this paper, we propose a ball tracking algorithm robust against occlusion in broadcasting soccer video sequences. Soccer ball tracking is a challenging task due to occlusion, fast motion and fast direction changes. Many works have been proposed based on ball trajectory. However, this approach requires heavy computational complexity. We propose a ball tracking algorithm with occlusion handling capability. Initial ball location is calculated using the circular hough transform. Then, the ball is tracked using template matching. Occlusion is handled by matching score. In occlusion cases, we generate a set of ball candidates. The ball candidates which exist in the previous frame were removed. On the other hand, the new appearing candidate is determined as the ball. Experiments with several broadcasting soccer video sequences show that the proposed method efficiently handles the occlusion cases.

• 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 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.

• 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.

• 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 Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.185-196
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• 1999

In this dissertation, a new off-line recognition system is proposed using a subgraph pattern, neural network. After thinning is applied to input characters, balance having a noise elimination function on location is performed. Then as the first step for recognition procedure, circular elements are extracted and recognized. From the subblock HT, space feature points such as endpoint, flex point, bridge point are extracted and a subgraph pattern is formed observing the relations among them. A region where vowel can exist is allocated and a candidate point of the vowel is extracted. Then, using the subgraph pattern dictionary, a vowel is recognized. A same method is applied to extract horizontal vowels and the vowel is recognized through a simple structural analysis. For verification of recognition subgraph in this paper, experiments are done with the most frequently used Myngjo font, Gothic font for printed characters and handwritten characters. In case of Gothic font, character recognition rate was 98.9%. For Myngjo font characters, the recognition rate was 98.2%. For handwritten characters, the recognition rate was 92.5%. The total recognition rate was 94.8% with mixed handwriting and printing characters for multi-font recognition.

• 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 Korea Information Processing Society Conference
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• 2006.11a
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• pp.43-46
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• 2006

본 논문에서는 호프변환을 이용한 실시간 수신호 인식시스템에서 대상영역 분할의 오차와 추출된 특징의 위치 변화등의 영향을 개선하는 방법론을 제안한다. 원형호프변환을 기반으로 생성한 특징정보로부터 CNN(Convolution Neural Network) 모델의 계층적 구조를 통하여 단계적으로 일련의 특징지도가 추출된다. CNN 모델에서 샘플링 계층의 연결구조는 특징의 위치 변화에 강인한 추출기능을 지원하며, 상위계층에서 보다 함축적인 특징지도를 생성하게 된다. 원형 호프 변환은 손의 형태학적 주요 포인트를 효과적으로 추출할 수 있게 하고 또한 입력 영상의 회전으로 인한 제약을 극복할 수 있게 한다. 본 연구에서는 제안된 이론을 TV 원격 제어를 위한 수신호 인터페이스 시스템을 대상으로 적용함으로써 그 유용성을 고찰한다.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.79-82
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• 2018

Due to the handheld holding of smartphones and the presence of light leakage and non-balanced contrast, the detection of the retina area in smartphone-captured fundus images is more challenging than retinography-captured fundus images. This paper presents a computationally efficient image processing pipeline in order to detect and enhance the retina area in smartphone-captured fundus images. The developed pipeline consists of five image processing components, namely point spread function parameter estimation, deconvolution, contrast balancing, circular Hough transform, and retina area extraction. The results obtained indicate a typical fundus image captured by a smartphone through a D-EYE lens is processed in 1 second.