• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.751-756
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• 2018

This research extracted traffic light from input video, recognized colors of traffic light, and suggested traffic light color recognizing algorithm applicable to manless autonomous vehicle or ITS by distinguishing signs. To extract traffic light, suggested algorithm extracted the outline with CEA(Canny Edge Algorithm), and applied HCT(Hough Circle Transform) to recognize colors of traffic light and improve the accuracy. The suggested method was applied to the video of stream acquired on the road. As a result, excellent rate of traffic light recognition was confirmed. Especially, ROI including traffic light in input video was distinguished and computing time could be reduced. In even area similar to traffic light, circle was not extracted or V value is low in HSV space, so it's failed in candidate area. So, accuracy of recognition rate could be improved.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.1
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• pp.75-83
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• 2008

We propose a welfare interface using multiple fecial features tracking, which can efficiently implement various mouse operations. The proposed system consist of five modules: face detection, eye detection, mouth detection, facial feature tracking, and mouse control. The facial region is first obtained using skin-color model and connected-component analysis(CCs). Thereafter the eye regions are localized using neutral network(NN)-based texture classifier that discriminates the facial region into eye class and non-eye class, and then mouth region is localized using edge detector. Once eye and mouth regions are localized they are continuously and correctly tracking by mean-shift algorithm and template matching, respectively. Based on the tracking results, mouse operations such as movement or click are implemented. To assess the validity of the proposed system, it was applied to the interface system for web browser and was tested on a group of 25 users. The results show that our system have the accuracy of 99% and process more than 21 frame/sec on PC for the $320{\times}240$ size input image, as such it can supply a user-friendly and convenient access to a computer in real-time operation.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.705-711
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• 2011

This paper proposes an effective algorithm of license plate recognition for industrial applications. By applying Canny edge detection on a vehicle image, it is possible to find a connected rectangular, which is a strong candidate for license plate. The color information of license plate separates plates into white and green. Then, OTSU binary image processing and foreground neighbor pixel propagation algorithm CLNF will be applied to each license plates to reduce noise except numbers and letters. Finally, through labeling, numbers and letters will be extracted from the license plate. Letter and number regions, separated from the plate, pass through mesh method and thinning process for extracting feature vectors by X-Y projection method. The extracted feature vectors are compared with the pre-learned weighting values by backpropagation neural network to execute final recognition process. The experiment results show that the proposed license plate recognition algorithm works effectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.10C
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• pp.1414-1424
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• 2004

In this paper, an algorithm using wavelet transform for detecting a cut that is a radical scene transition point, and fade and dissolve that are gradual scene transition points is proposed. The conventional methods using wavelet transform for this purpose is using features in both spatial and frequency domain. But in the proposed algorithm, the color space of an input image is converted to YUV and then luminance component Y is transformed in frequency domain using 2-level lifting. Then, the histogram of only low frequency subband that may contain some spatial domain features is compared with the previous one. Edges obtained from other higher bands can be divided into global, semi-global and local regions and the histogram of each edge region is compared. The experimental results show the performance improvement of about 17% in recall and 18% in precision and also show a good performance in fade and dissolve detection.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.91-102
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• 2010

It is very difficult that a previous surveillance system and vehicle detection system find objects on a limited and unstable illumination condition. This paper proposes a robust method of adaptively updating a reference background image for solving problems that are generated by the unstable illumination. The first input image is set up as the reference background image, and is divided into three block categories according to an edge component. Then a block state analysis, which uses a rate of change of the brightness, a stability, a color information, and an edge component on each block, is applied to the input image. On the reference background image, neighbourhood blocks having the same state of a updated block are merged as a block. The proposed method can generate a robust reference background image because it distinguishes a moving object area from an unstable illumination. The proposed method very efficiently updates the reference background image from the point of view of the management and the processing time. In order to demonstrate the superiority of the proposed stable manner in situation that an illumination quickly changes.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.70-77
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• 2008

This paper proposes a new text candidate region detection method that uses genetic algorithm based on information of the segmented regions. In image segmentation, a classification of the pixels at each color channel and a reclassification of the region-unit for reducing inhomogeneous clusters are performed. EWFCM(Entropy-based Weighted C-Means) algorithm to classify the pixels at each color channel is an improved FCM algorithm added with spatial information, and therefore it removes the meaningless regions like noise. A region-based reclassification based on a similarity between each segmented region of the most inhomogeneous cluster and the other clusters reduces the inhomogeneous clusters more efficiently than pixel- and cluster-based reclassifications. And detecting text candidate regions is performed by genetic algorithm based on energy and variance of the directional edge components, the number, and a size of the segmented regions. The region information-based detection method can singles out semantic text candidate regions more accurately than pixel-based detection method and the detection results will be more useful in recognizing the text regions hereafter. Experiments showed the results of the segmentation and the detection. And it confirmed that the proposed method was superior to the existing methods.

• Textile Coloration and Finishing
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• v.20 no.1
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• pp.48-56
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• 2008

A real-time inspection system has been developed by combining CCD based image processing algorithm and a standard lighting equipment. The system was tested for defective fabrics showing nozzle contact scratch marks, which are one of the frequently occurring defects. Two algorithms used were compared according to both their processing time and detection rate. First algorithm (algorithm A) was based on morphological image processing such as dilation and opening for effective treatment of defective printing areas while second one (algorithm B) mainly employs well-defined edge detection technique based on canny detector and Zermike moment. It was concluded' that although both algorithms were quite successful, algorithm B showed relatively consistent performance than algorithm A in detecting complex patterns.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.1087-1104
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• 2015

An eye location based head posture recognition method is proposed in this paper. First, face is detected using skin color method, and eyebrow and eye areas are located based on gray gradient in face. Next, pupil circles are determined using edge detection circle method. Finally, head postures are recognized based on eye location information. The proposed method has high recognition precision and is robust for facial expressions and different head postures, and can be used in mouse operation. The experimental results reveal the validity of proposed method.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.173-176
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• 2009

본 논문에서는 피부색 정보와 눈, 입의 위치를 찾아 실시간으로 얼굴을 인식하는 랩을 제안한다. 먼저 노이즈를 제거하여 얼굴 후보 영역을 지정한다. 지정된 얼굴 후보 영역에서 눈과 입을 찾고, 찾은 눈과 입 사이의 영역에서 에지를 탐색하여 코의 존재 유무를 검증하고 이를 바탕으로 얼굴인지 판단하는 절차를 따른다. 제안한 기법은 피부색 검출을 위해 YCbCr 을 이용하여 피부 영역을 찾고 지정한 피부 영역에서 노이즈를 제거한 후, Eye Map의 EyeMapC 연산을 통해 눈을 Lip Map을 통해 입을 찾는다. 찾아낸 눈과 입의 사이의 영역에서 Canny Edge 연산을 수행하여 코의 존재 유무를 판단하여 최종적인 얼굴 영역을 판별하는 방법을 제안한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.223-226
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• 2009

유해 영상 검출은 유해 동영상을 내용 기반으로 검색하고 차단하기 위한 방법의 하나로써, 유해 동영상 추적 시스템의 전체 성능을 좌우하는 중요한 기술이다. 기존의 유해 영상 검출은 웹 사이트 내에 음란 콘텐츠를 추출함으로 유해 사이트를 차단하는데 사용되었으며, 주로 RGB 비율, Histogram 등을 이용한 Skin color와 Edge를 추적한 Texture를 기반으로 유해 영상을 검출하였다. 그러나 기존 방식은 UCC 유해 동영상과 같이 저화질 영상에서의 유해 여부를 판단하기에는 정확성이 낮다. 따라서 본 논문에서는 영상 크기에 따른 고/저화질 분류를 이용하여 동영상에서 보다 효과적인 유해 영상 검출할 수 있는 방법을 제안한다. 제안 방법의 성능을 확인하기 위해 고/저화질 분류 사용의 유/무에 따른 검출 실험을 하였으며, 그 결과 분류를 방법이 기존 방법보다 12%의 성능이 향상됨을 알 수 있었다.