• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.91-96
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• 2014

This paper proposed an adaptive threshold method for detecting flame candidate regions in a infrared image and it adapts according to the contrast and intensity changes in the image. Conventional flame detection systems uses fixed threshold method since surveillance environment does not change, once the system installed. But it needs a adaptive threshold method as requirements of surveillance system has changed. The proposed adaptive threshold algorithm uses the dynamic behavior of flame as featured parameter. The test result is analysed by comparing test result of proposed adaptive threshold algorithm and conventional fixed threshold method. The analysed data shows, the proposed method has 91.42% of correct detection rate and false detection is reduced by 20% comparing to the conventional method.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.938-940
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• 2017

본 논문에서는 화재의 조기 감지를 위하여 카메라 입력 영상으로부터 화염을 검출하는 알고리즘을 제안한다. 화염은 특정 RGB 좌표계를 가지며 지속해서 형태가 변화하며 움직인다. 제안하는 화염 검출 알고리즘은 먼저 야외 환경에서 조도의 변화에 관계없이 화염 검출 알고리즘을 적용하기 위해 Color Constancy 알고리즘을 적용한다. 그 후 화염의 RGB 좌표계와 움직임의 변화를 측정하여 후보영역을 설정하고 Opponent SURF와 SVM을 통해 최종 화염을 검출한다. 컴퓨터 시뮬레이션을 통하여 제안하는 알고리즘으로 화염을 검출할 수 있음을 확인하였다.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.89-94
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• 2013

This paper presents color video flame detection algorithm based on wavelet transform to remove detection of flickering non-flame objects. Conventional flame detection algorithms consist of simple or mixed functions using colors, temporal and spatial characteristics. But those algorithms detect non-flame objects as flame regions sometimes. False alarm reasons are flame-like objects with regular flickering lights such as car signal lamps, alarm lights etc. The proposed algorithm is to reduce false detection which is occurred in periodic flickering lights. At first, It segments the candidate flame regions by using frame difference, flame colors. Then it distinguish flame regions and non flame regions including flickering car lights by analyzing wavelet coefficients. Computer simulation results showed that the proposed algorithm removes false detection due to the periodic flickering lamps by performing 97.9% of correct detection rate while false detection rate is 7.3%.

• Journal of Korea Multimedia Society
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• v.12 no.11
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• pp.1547-1556
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• 2009

In this paper, we propose a flame detection method using region expansions and on-line variances in outdoor infrared video sequences. To segment flame candidates' regions in infrared images, we first, extract initial regions by high threshold values in infrared images and then the segmented regions are expanded to their neighbors with similar high intensity values. The segmented regions could be non-flame areas like bare-grounds and buildings. Therefore, to detect flame regions in the segmented regions, the segmented regions which have high intensity values in infrared image, are tracked using bounding regions in frame sequences. Variances in the tracked regions are calculated effectively by on-line updates to measure intensity variations on the tracked regions. Experiments show that the proposed method, which is based on region expansions and the average of on-line variances in the regions, is efficient to detect flames in infrared image.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.163-168
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• 2003

Several equipments for flame detection are employed in the power generations. But these flame detectors have some problems for the correct performance. So in this paper, we apply different techniques for the flame detection. Image processing techniques are broadly applied in industrial fields. In this paper, the image information is recorded by a camcoder and then these images are preprocessed for the input values of neural network model. We can test and evaluate the approach that uses image information for the flame detection of burners. If this technique is implemented in physical plant, the economical and effective operation could be achieved.

• Fire Science and Engineering
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• v.22 no.1
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• pp.68-75
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• 2008

In this paper, we introduce a fire-detection system which is robust to light sources and environment changing. We can decide the threshold values that classify the regions between a fire flame and light sources by analyzing them in RGB color space. But we could not discriminate quasi-flame region from fire flame region with the value. The difference of mean-histogram technique make it possible to extract flame region more efficient because fire flame is continuously changing after it occurs. In order to validate real fire, this paper uses regional compactness in the end of process. Computer simulation show that proposed method make more robust to light sources and environment changing.

• Proceedings of the Korea Contents Association Conference
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• 2005.05a
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• pp.382-386
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• 2005

In this paper we introduce a fire-detection system which is robust to light sources and environment changing. We can decide the threshold values that classify the regions between a fire flame and light sources by analyzing them in RGB color space. The mean histogram difference technique make it possible to extract flame region more efficient because fire flame is continuously changing after it occurs. In order to detect flame region, this paper proposes to count fire pixels.

• Fire Science and Engineering
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• v.22 no.4
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• pp.27-32
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• 2008

In this paper, a flame and smoke detection algorithm is proposed to recognize a fire. Flame and smoke have specific color distribution and continuously change shapes of them. In the proposed flame detection algorithm, specific regions are candidated as flame by color distributions and variations of frames of video. Some of candidated regions are decided as flame by the magnitude of motion vector. To determine smoke in the field of view of camera, edge is important because high frequency component is decreased by it. Candidated region of smoke is assigned by color distributions, inter-frame differences and the value of edge. The candidated region is settled as smoke region with magnitude of motion vector. As results of simulations, it is shown that the proposed algorithm is useful for flame and smoke detection.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.367-376
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• 2009

In this paper, we propose a flame detection method using Haar wavelet and moving averages in outdoor infrared video sequences. Our proposed method is composed of three steps which are Haar wavelet decomposition, flame candidates detection, and their tracking and flame classification. In Haar wavelet decomposition, each frame is decomposed into 4 sub- images(LL, LH, HL, HH), and also computed high frequency energy components using LH, HL, and HH. In flame candidates detection, we compute a binary image by thresholding in LL sub-image and apply morphology operations to the binary image to remove noises. After finding initial boundaries, final candidate regions are extracted using expanding initial boundary regions to their neighborhoods. In tracking and flame classification, features of region size and high frequency energy are calculated from candidate regions and tracked using queues, and we classify whether the tracked regions are flames by temporal changes of moving averages.

• Journal of Satellite, Information and Communications
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• v.9 no.2
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• pp.33-37
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• 2014

This paper propose a method to sort flame regions and non-flame regions in a color image based on temperature Characteristics of flame. The traditional algorithms simply detect flame regions those are colored between yellow and red and there are lot of false detection in this method. But the colors of real flame are fallen between white and red and flame color variation over the flame. In this paper, it reduce false detection by separating colors according to temperature Characteristics of flame. The proposed method firstly finds a color model to express the temperature Characteristics of fire and then the color model is non-linearly quantized based on color values and analyzed using histogram and finally detect the candidate flame regions. The proposed method has 71.8% of matching rate and if it is compared with non-matching rate of traditional algorithms, the non-matching rate is improved by 27 times than others.