• 대한임베디드공학회논문지
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• 제16권6호
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• pp.277-283
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• 2021

This paper addresses the problem of vision-based mask filter inspection for mask production systems. Machine learning-based approaches can be considered to solve the problem, but they may not be applicable to mask filter inspection if normal and anomaly mask filter data are not sufficient. In such cases, handcrafted image processing methods have to be considered to solve the problem. In this paper, we propose a hierarchical correlation-based approach that combines handcrafted image processing methods to detect anomaly mask filters. The proposed approach combines image rotation, cropping and resizing, edge detection of mask filter parts, average blurring, and correlation-based decision. The proposed approach was tested and analyzed with real mask filters. The results showed that the proposed approach was able to successfully detect anomalies in mask filters.

• 방송공학회논문지
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• 제24권7호
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• pp.1266-1275
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• 2019

With increasing criminal rates and number of CCTVs, much attention has been paid to intelligent surveillance system on the horizon. Object detection and tracking algorithms have been developed to reduce false alarms and accurately help security agents immediately response to undesirable changes in video clips such as crimes and accidents. Many studies have proposed a variety of algorithms to improve accuracy of detecting and tracking objects outside tunnels. The proposed methods might not work well in a tunnel because of low illuminance significantly susceptible to tail and warning lights of driving vehicles. The detection performance has rarely been tested against the tunnel environment. This study investigated a feasibility of object detection and tracking in an actual tunnel environment by utilizing YOLOv3 and Kernelized Correlation Filter. We tested 40 actual video clips to differentiate pedestrians and motorcycles to evaluate the performance of our algorithm. The experimental results showed significant difference in detection between pedestrians and motorcycles without false positive rates. Our findings are expected to provide a stepping stone of developing efficient detection algorithms suitable for tunnel environment and encouraging other researchers to glean reliable tracking data for smarter and safer City.

• 한국통신학회논문지
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• 제27권7A호
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• pp.695-704
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• 2002

멀티미디어에 삽입된 워터마크의 검출은 저작권 보호 및 인증 분야에서 매우 중요한 역할을 한다. 최근 워터마크의 검출에 많이 사용되는 유사도 기반 알고리즘은 대상 영상의 분포 특성을 이용하지 않기 때문에 검출 성능이 떨어지는 단점을 가진다. 따라서 본 논문에서는 웨이블릿 변환 영역에서 상승적 방법에 의하여 삽입된 워터마크에 대한 효율적인 검출 알고리즘을 제안하였다. 제안한 워터마크 검출 알고리즘은 통계적 판단 이론에 따라 Bayes 판단 이론, 웨이블릿 계수들의 확률 분포 모델, 및 Neyman-Pearson 정의에 기반을 둔다. 따라서 제안한 검출 알고리즘에서는 주어진 오류 검출 확률에 대하여 간과 검출 확률을 최소화할 수 있는 장점이 있다. 제안한 검출 알고리즘의 성능 평가는 견고성 측면에서 수행되었고, 실험 결과로부터 제안한 알고리즘이 유사도 기반 알고리즘에 비하여 우수한 성능을 나타냄을 확인하였다.

• 전자공학회논문지CI
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• 제40권1호
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• pp.39-49
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• 2003

멀티미디어에 삽입된 워터마크의 검출은 저작권 보호 및 인증 분야에서 매우 중요한 역할을 한다. 최근 워터마크의 검출에 많이 사용되는 유사도 기반 알고리즘은 상가성 방법을 제외한 워터마크 삽입 방법에 대해서는 효과적이지 못한 단점을 가진다. 따라서 본 논문에서는 웨이블릿 변환 영역에서 상승적 방법에 의하여 삽입된 워터마크에 대한 효율적인 검출 알고리즘을 제안하였다. 제안한 워터마크 검출 알고리즘은 통계적 판단 이론에 따라 Bayes 판단 이론, Neyman-Pearson 정의, 및 웨이블릿 계수들의 확률 분포 모델을 기반으로 도출되어서, 주어진 오류 검출 확률에 대하여 간과 검출 확률을 최소화할 수 있다. 제안한 검출 알고리즘의 성능 평가는 견고성 측면에서 수행되었고, 실험 결과로부터 제안한 알고리즘이 유사도 기반 알고리즘에 비하여 우수한 성능을 나타냄을 확인하였다.

• Journal of Positioning, Navigation, and Timing
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• 제4권4호
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• pp.173-180
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• 2015

In this study, an algorithm that detects a spoofing signal for a GPS L1 signal was proposed, and the performance was verified through RF spoofing signal simulation. The proposed algorithm determines the reception of a spoofing signal by detecting a correlation distortion of GPS L1 C/A code caused by the spoofing signal. To detect the correlation distortion, a detection criterion of a spoofing signal was derived from the relationship among the Early, Prompt, and Late tap correlation values of a receiver correlator; and a detection threshold was calculated from the false alarm probability of spoofing signal detection. In this study, an RF spoofing environment was built using the GSS 8000 simulator (Spirent). For the RF spoofing signal generated from the simulator, the RF spoofing environment was verified using the commercial receiver DL-V3 (Novatel Inc.). To verify the performance of the proposed algorithm, the RF signal was stored as IF band data using a USRP signal collector (NI) so that the data could be processed by a CNU software receiver (software defined radio). For the performance of the proposed algorithm, results were obtained using the correlation value of the software receiver, and the performance was verified through the detection of a spoofing signal and the detection time of a spoofing signal.

• 한국위성정보통신학회논문지
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• 제10권2호
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• pp.109-114
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• 2015

프레임 동기화는 전송 프레임 헤더에 주기적으로 삽입되는 프리엠블(preamble) 패턴과 수신 심볼 간의 상관 연산을 통해 이루어지며 프레임의 시작점 및 구조 검출을 하는 것이 목적이다. 본 논문은 위성 기반 DVB-S2 시스템 요구사항에 부합하는 프레임 구조 획득 방법에 대해 기술하였다. DVB-S2 수신 신호는 매우 낮은 신호 대 잡음비를 가지며 심볼 속도 대비 20%에 상응하는 주파수 오프셋 성분이 포함되어 있다. 또한 규격은 프레임 당 심볼 수가 상이한 16가지의 프레임 구조를 지원하고 있다. 본 논문에서는 위의 환경에서 정확하고 빠른 프레임 동기화를 위해 프레임 헤더의 SOF와 PLSC 정보를 이용하여 상관 열을 발생시키고 상관 값 누적을 통해 프레임 동기 및 구조 검출을 실시하였다 마지막으로 컴퓨터 모의실험을 통해 평균 획득 시간(mean acquisition time), 프레임 구조 검출 오류율에 대한 성능평가를 실시하였다.

• 한국항행학회논문지
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• 제18권1호
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• pp.7-13
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• 2014

본 논문에서는 GPS 전파 간섭 신호의 한 종류인 기만 신호를 검출하는 기법을 제안한다. 본 논문에서 기만의 대상이 되는 신호에는 민간에 구조가 공개된 GPS L1 C/A 신호로 선정하였으며 GPS L1 C/A 기만 신호의 영향을 분석하고 이를 통해서 기만 신호 검출 기법을 제안한다. 제안하는 기만 신호 검출 기법은 상관함수가 왜곡된 정도로 기만 신호의 인가를 판단한다. 기만 신호의 판단기준은 수신기 열잡음의 통계적 특성으로부터 정량적인 수치로 계산된 임계값을 이용하였다. 제안하는 기법을 검증하기 위한 시뮬레이션은 MATLAB을 기반으로 구성하였으며 기만 신호에 의한 상관함수 왜곡 및 코드 위상 오차를 확인하였다. 그리고 본 논문에서 제안하는 기만 신호 검출 기법을 적용하여 기만 신호의 검출 시뮬레이션을 수행하여 제안하는 기법에 의한 기만 신호 검출성능을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.381-404
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• 2022

Forest fires inflict great losses of human lives and serious damages to ecological systems. Hence, numerous fire detection methods have been proposed, one of which is fire detection based on sensors. However, these methods reveal several limitations when applied in large spaces like forests such as high cost, high level of false alarm, limited battery capacity, and other problems. In this research, we propose a novel forest fire detection method based on image processing and correlation coefficient. Firstly, two fire detection conditions are applied in RGB color space to distinguish between fire pixels and the background. Secondly, the image is converted from RGB to YCbCr color space with two fire detection conditions being applied in this color space. Finally, the correlation coefficient is used to distinguish between fires and objects with fire-like colors. Our proposed algorithm is tested and evaluated on eleven fire and non-fire videos collected from the internet and achieves up to 95.87% and 97.89% of F-score and accuracy respectively in performance evaluation.

• 대한의용생체공학회:의공학회지
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• 제32권4호
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• pp.336-344
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• 2011

This article describes a new recognition algorithm using correlation coefficient for intelligent wheelchair to avoid collision for elderly or disabled people. The correlation coefficient can be used to represent the relationship of two different areas. The algorithm has three steps: Firstly, we extract an edge vector using the Histogram of Oriented Gradients(HOG) which includes gradient information and unique magnitude for each cell. From this result, the correlation coefficients are calculated between one cell and others. Secondly, correlation coefficients are used as the weighting factors for normalizing the HOG cell. And finally, these features are used to classify or detect variable and complicated shapes of two wheelers using Adaboost algorithm. In this paper, we propose a new feature vectors which is calculated by weighted cell unit to classify with multiple view-based shapes: frontal, rear and side views($60^{\circ}$, $90^{\circ}$ and mixed angle). Our experimental results show that two wheeler detection system based on a proposed approach leads to a higher detection accuracy than the method using traditional features in a similar detection time.

• Journal of Multimedia Information System
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• 제3권4호
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• pp.119-128
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• 2016

This study aims to suggest a new algorithm for detecting two-wheelers on road that have various shapes according to the viewing angle for vision based intelligent vehicles. This article describes a new approach to two-wheelers detection algorithm riding on people based on modified Histogram of Oriented Gradients (HOG) using correlation coefficient (CC). The CC between two local area variables, in which one is the person riding a bike and other is its background, can represent correlation relation. First, we extract edge vectors using HOG which includes gradient information and differential magnitude as cell based. And then, the value, which is calculated by the CC between the area of each cell and one of two-wheelers, can be extracted as the weighting factor in process for normalizing the modified HOG cell. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.