• 한국위성정보통신학회논문지
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• 제8권4호
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• pp.100-104
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• 2013

본 논문은 열영상 기반의 화염 검출을 위한 기존의 문턱치 설정 기법들을 분석하고 최적 문턱치 설정 방안을 제시한다. 기존의 열영상 기반의 화염검출 알고리즘들은 보통 고정 문턱치를 이용하여 화염 후보영역을 추출하고 후처리를 통해 화염 검출을 최종 판정하므로 화염 후보영역의 결정 과정은 최종 화재 검출 결과에 많은 영향을 준다. 따라서 카메라의 종류나 운영 환경에 따라 입력 영상의 대비와 밝기의 변화가 발생하기 때문에 화염 검출 문턱치는 입력영상의 특성에 연동하여 설정되어져야 한다. 따라서 최적 문턱치 설정 방안을 제시하기 위해 고정 명암도, 평균값, 표준편차 및 최대값을 이용한 문턱치 설정 기법들을 비교 분석하였다. 결론적으로 최적 문턱치는 평균과 표준편차의 합보다 크며 최대값 보다는 작은 값으로 설정 한다면 화염 검출 정확도가 기존 고정 문턱치 방식에 비해 크게 개선될 것으로 기대된다.

• 한국컴퓨터산업학회논문지
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• 제2권3호
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• pp.389-396
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• 2001

압축된 동영상에서 인덱싱을 위한 장면전환 검출기법에서 기존의 방법들은 실험에 의한 고정 임계값을 설정하여 임계값 보다 크면 장면전환이라고 판단해왔다. 기존의 고정 임계값을 적용시켰을 때는 플래쉬나 카메라 움직임 등에 의한 오검출이 많은 문제점이 있었다. 본 논문에서는 장면 전환 검출을 위한 임계값을 동영상 특성 중, 장면전환점간격을 이용하여 임계값을 동적으로 변화시키는 방법이며, 고정 임계값을 사용하는 경우보다 오검출을 줄이는 향상된 장면전환 검출기법을 제안한다. 실험에서는 동영상 특성을 통계적으로 분석하여, 기존의 고정임계값과 제안한 동적임계값을 사용한 결과 값을 비교분석 하였다. 제안한 방법은 기존의 방법보다 30%정도 오검출이 줄었다.

• 한국위성정보통신학회논문지
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• 제8권1호
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• pp.66-70
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• 2013

본 논문에서는 협력하고자하는 소출력 기기들이 협력 스펙트럼 센싱 할 경우 협력 노드들이 같은 FA(False Alarm)가지고 있다 가정하며, 이때 최적의 임계값을 셋팅하고 서로 정보를 공유하는 시스템 모델을 제안하고 성능을 분석한다. 협력하고자하는 모든 노드의 False alarm이 같아도 각 채널에 따라 임계값이 달라지게 된다. 임계값이 낮아지면 검출확률이 낮아지게 되고, 반대로 임계값이 높을 때 검출확률은 높아지는 특성을 가지기 때문에, 따라서 가장 높음 임계값을 선택하여 세팅하고 공유하게 된다. 이는 협력스펙트럼 센싱시 가장 높은 임계값을 공유함으로써 고정되어 있는 임계값을 보다 높은 검출성능을 보일 수 있다.

• 한국정보시스템학회지:정보시스템연구
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• 제14권3호
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• pp.117-122
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• 2005

This paper presents a method for detecting scene changes in video sequences, in which the $chi^{2}$-test is modified by imposing weights according to NTSC standard. To automatically determine threshold values for scene change detection, the proposed method utilizes the frame differences that are obtained by the weighted $chi^{2}$-test. In the first step, the mean and the standard deviation of the difference values are calculated, and then, we subtract the mean difference value from each difference value. In the next step, the same process is performed on the remained difference values, mean-subtracted frame differences, until the stopping criterion is satisfied. Finally, the threshold value for scene change detection is determined by the proposed automatic threshold estimation algorithm. The proposed method is tested on various video sources and, in the experimental results, it is shown that the proposed method is reliably estimates the thresholds and detects scene changes.

• Journal of Electrical Engineering and Technology
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• 제13권5호
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• pp.2080-2085
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• 2018

There have been numerous studies that extract the R-peak from electrocardiogram (ECG) signals. All of these studies can extract R-peak from ECG. However, these methods are complicated and difficult to implement in a real-time portable ECG device. After filtration choosing a threshold value for R-peak detection is a big challenge. Fixed threshold scheme is sometimes unable to detect low R-peak value and adaptive threshold sometime detect wrong R-peak for more adaptation. In this paper, a simple and robustness algorithm is proposed to detect R-peak with less complexity. This method also solves the problem of threshold value selection. Using the adaptive filter, the baseline drift can be removed from ECG signal. After filtration, an appropriate threshold value is automatically chosen by using the minimum and maximum value of an ECG signals. Then the neighborhood searching scheme is applied under threshold value to detect R-peak from ECG signals. Proposed method improves the detection and accuracy rate of R-peak detection. After R-peak detection, we calculate heart rate to know the heart condition.

• 한국정보전자통신기술학회논문지
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• 제8권2호
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• pp.140-146
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• 2015

본 논문에서는 레이더 시스템에서 원하는 목표물 추정을 위한 효율적인 임계치 방법을 연구였다. 관심있는 목표물 탐지 검출 방법은 오 경보 확률을 변화 시켜 가면서 원하는 목표물을 추정한다. 이때 오 경보의 확률은 임계치와 밀접한 관계가 있다. 임계치를 낮게 하면 원하는 목표물의 정확도를 향상시킬 수 있지만 잡음 까지 추정하여 효율적인 신호처리 방법이 되지 못한다. 효율 적인 임계치 방법을 제안하여 원하는 목표물을 추정하는 연구 방법을 제안하다. 모의실험을 통해서 기존의 방법과 본 연구에서 제안한 방법을 비교 분석하였다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.229-232
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• 2009

Shot change detection is an important technique for effective management of video data, so detection scheme requires adaptive detection techniques to be used actually in various video. In this paper, we propose an adaptive shot change detection algorithm using the mean of feature value on variable reference blocks. Our algorithm determines shot change detection by defining adaptive threshold values with the feature value extracted from video frames and comparing the feature value and the threshold value. We obtained better detection ratio than the conventional methods maximally by 15% in the experiment with the same test sequence. We also had good detection ratio for other several methods of feature extraction and could see real-time operation of shot change detection in the hardware platform with low performance was possible by implementing it in TVUS model of HOMECAST Company. Thus, our algorithm in the paper can be useful in PMP or other portable players.

• 대한원격탐사학회지
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• 제37권1호
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• pp.69-84
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• 2021

Awareness of boats is a main issue in areas of fishery management, illegal fishing, and maritime traffic, etc. For the awareness, Automatic Identification System (AIS) and Vessel-Pass System (V-PASS) have been widely used to collect the boat-related information. However, only using these systems makes it difficult to collect the accurate information. Recently, satellite-based data has been increasingly used as a cooperative system. In 2015, U.S. National Oceanic and Atmospheric Administration (NOAA) developed a boat detection algorithm using Visible Infrared Imaging Radiometer Suite (VIIRS) Day & Night Band (DNB) data. Although the detections have been widely utilized in many publications, it is difficult to estimate the night-time fishing boats immediately. Particularly, it is difficult to estimate the threshold due to the lunar irradiation effect. This effect must be corrected to apply a single specific threshold. In this study, the moon phase was considered as the main frequency of this effect. Considering the moon phase, relational expressions are derived and then used as offsets for relative correction. After the correction, it shows a significant reduction in the standard deviation of the threshold compared to the threshold of NOAA. Through the correction, this study can set a constant threshold every day without determination of different thresholds. In conclusion, this study can achieve the detection applying the single specific threshold regardless of the moon phase.

• 반도체디스플레이기술학회지
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• 제18권4호
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• pp.110-115
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• 2019

Blur detection and segmentation play vital role in many computer vision applications. Among various methods, local binary pattern based methods provide reasonable blur detection results. However, in conventional local binary pattern based methods, the blur map is computed by using a fixed threshold irrespective of the type and level of blur. It may not be suitable for images with variations in imaging conditions and blur. In this paper we propose an effective method based on local binary pattern with adaptive threshold for blur detection. The adaptive threshold is computed based on the model learned through the multinomial logistic regression. The performance of the proposed method is evaluated using different datasets. The comparative analysis not only demonstrates the effectiveness of the proposed method but also exhibits it superiority over the existing methods.

• 반도체디스플레이기술학회지
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• 제19권3호
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• pp.7-11
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• 2020

Enormous methods have been proposed for the detection and segmentation of blur and non-blur regions of the images. Due to the limited available information about the blur type, scenario and the level of blurriness, detection and segmentation is a challenging task. Hence, the performance of the blur measure operators is an essential factor and needs improvement to attain perfection. In this paper, we propose an effective blur measure based on the local binary pattern (LBP) with the adaptive threshold for blur detection. The sharpness metric developed based on LBP uses a fixed threshold irrespective of the blur type and level which may not be suitable for images with large variations in imaging conditions and blur type and level. Contradictory, the proposed measure uses an adaptive threshold for each image based on the image and the blur properties to generate an improved sharpness metric. The adaptive threshold is computed based on the model learned through the support vector machine (SVM). The performance of the proposed method is evaluated using a well-known dataset and compared with five state-of-the-art methods. The comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all the methods.