• Journal of IKEEE
• /
• v.24 no.4
• /
• pp.1148-1155
• /
• 2020

Fire must be extinguished as quickly as possible because they cause a lot of economic loss and take away precious human lives. Especially, the detection of smoke, which tends to be found first in fire, is of great importance. Smoke detection based on image has many difficulties in algorithm research due to the irregular shape of smoke. In this study, we introduce a new real-time smoke detection algorithm that reduces the detection of false positives generated by irregular smoke shape based on faster r-cnn of factory-installed surveillance cameras. First, we compute the global frame similarity and mean squared error (MSE) to detect the movement of smoke from the input surveillance camera. Second, we use deep learning algorithm (Faster r-cnn) to extract deferred candidate regions. Third, the extracted candidate areas for acting are finally determined using space and temporal features as smoke area. In this study, we proposed a new algorithm using the space and temporal features of global and local frames, which are well-proposed object information, to reduce false positives based on deep learning techniques. The experimental results confirmed that the proposed algorithm has excellent performance by reducing false positives of about 99.0% while maintaining smoke detection performance.

• 한국HCI학회:학술대회논문집
• /
• 2008.02a
• /
• pp.215-217
• /
• 2008

The most widely used method for searching contents of mot ion picture compares contents by extracted cuts. The cut extract ion methods, such as CHD(Color Histogram Difference) or ECR(Edge Change Ratio), are very weak at modifications such as cropping, resizing and low bit rate. The suggested method uses audio contents for indexing and searching to make search be robust against these modification. Scenes of audio contents are extracted for modification-robust search. And based on these scenes, make spectral powers binary on each frequency bin. in the time-frequency domain. The suggested method shows failure rate less than 1% on the false positive error and the true negative error to the modified(using cropping, clipping, row bit rate, addtive frame) contents.

• 한국HCI학회:학술대회논문집
• /
• 2009.02a
• /
• pp.193-196
• /
• 2009

Most of studies adopt a fixed skin color model to segment skin color region in a single image. The methods, however, result in low detection rates or high false positive error rates since the distribution of skin color is varies depending on the characteristics of input image. For the effective skin color segmentation, therefore, we need a adaptive skin color model which changes the model depending on the color distribution of input image. In this paper, we propose a novel adaptive skin color segmentation algorithm consisting of 2 stages which results in both high detection rate and low false positive error rate.

• Journal of the Optical Society of Korea
• /
• v.14 no.4
• /
• pp.451-457
• /
• 2010

Increasing demands on the safety of public train services have led to the development of various types of security monitoring systems. Most of the surveillance systems are focused on the estimation of crowd level in the platform, thereby yielding too many false alarms. In this paper, we present a novel security monitoring system to detect critically dangerous situations such as when a passenger falls from the station platform, or when a passenger walks on the rail tracks. The method is composed of two stages of detecting dangerous situations. Objects falling over to the dangerous zone are detected by motion tracking. 3D depth information retrieved by the stereo vision is used to confirm fallen events. Experimental results show that virtually no error of either false positive or false negative is found while providing highly reliable detection performance. Since stereo matching is performed on a local image only when potentially dangerous situations are found; real-time operation is feasible without using dedicated hardware.

• Journal of the Architectural Institute of Korea Structure & Construction
• /
• v.36 no.3
• /
• pp.113-120
• /
• 2020

The purpose of this study is to compare the models of Deep Learning-based Convolution Neural Network(CNN) for concrete crack detection. The comparison models are AlexNet, GoogLeNet, VGG16, VGG19, ResNet-18, ResNet-50, ResNet-101, and SqueezeNet which won ImageNet Large Scale Visual Recognition Challenge(ILSVRC). To train, validate and test these models, we constructed 3000 training data and 12000 validation data with 256×256 pixel resolution consisting of cracked and non-cracked images, and constructed 5 test data with 4160×3120 pixel resolution consisting of concrete images with crack. In order to increase the efficiency of the training, transfer learning was performed by taking the weight from the pre-trained network supported by MATLAB. From the trained network, the validation data is classified into crack image and non-crack image, yielding True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), and 6 performance indicators, False Negative Rate (FNR), False Positive Rate (FPR), Error Rate, Recall, Precision, Accuracy were calculated. The test image was scanned twice with a sliding window of 256×256 pixel resolution to classify the cracks, resulting in a crack map. From the comparison of the performance indicators and the crack map, it was concluded that VGG16 and VGG19 were the most suitable for detecting concrete cracks.

• The Korean Journal of Cytopathology
• /
• v.19 no.2
• /
• pp.111-118
• /
• 2008

Background : Cervicovaginal cytology is a screening test of uterine cervical cancer. The sensitivity of cervicovaginal cytology is less than 50%, but studies of cytologic/histologic correlation are limited. We analyzed the diagnostic accuracy of cervicovaginal cytology in the detection of the squamous epithelial lesions of the uterine cervix and investigate the cause of diagnostic discordance. Materials and Methods : We collected a total of 481 sets of cervicovaginal cytology and biopsies over 5 years. The cytologic diagnoses were categorized based on The Bethesda System and the histologic diagnoses were classified as negative, flat condyloma, cervical intraepithelial neoplasia (CIN) I, CIN II, CIN III, or squamous cell carcinoma. Cytohistologic discrepancies were reviewed. Results: The concordance rate between the cytological and the histological diagnosis was 79.0%. The sensitivity and specificity of cervicovaginal cytology were 80.6% and 92.6%, respectively. Its positive predictive value and negative predictive value were 93.7% and 77.7%, respectively. The false negative rate was 19.4%. Among 54 false negative cytology cases, they were confirmed by histology as 50 flat condylomas, 2 CIN I, 1 CIN III, and 1 squamous cell carcinoma. The causes of false negative cytology were sampling errors in 75.6% and interpretation errors in 24.4%. The false positive rate was 7.4%. Among 15 false positive cytology cases, they were confirmed by histology as 12 atypical squamous cells of undetermined significance (ASCUS) and 3 low grade squamous intraepithelial lesions (LSIL). The cause of error was interpretation error in all cases. The overall diagnostic accuracy of cervicovaginal cytology was 85.7%. Conclusions : Cervicovaginal cytology shows high overall diagnostic accuracy and is a useful primary screen of uterine cervical cancer.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.41 no.2
• /
• pp.9-15
• /
• 2018

This study is intended to investigate that it is possible to analyze the public awareness and satisfaction of the weather forecast service provided by the Korea Meteorological Administration (KMA) through social media data as a way to overcome limitations of the questionnaire-based survey in the previous research. Sentiment analysis and association rule mining were used for Twitter data containing opinions about the weather forecast service. As a result of sentiment analysis, the frequency of negative opinions was very high, about 75%, relative to positive opinions because of the nature of public services. The detailed analysis shows that a large portion of users are dissatisfied with precipitation forecast and that it is needed to analyze the two kinds of error types of the precipitation forecast, namely, 'False alarm' and 'Miss' in more detail. Therefore, association rule mining was performed on negative tweets for each of these error types. As a result, it was found that a considerable number of complaints occurred when preventive actions were useless because the forecast predicting rain had a 'False alarm' error. In addition, this study found that people's dissatisfaction increased when they experienced inconveniences due to either unpredictable high winds and heavy rains in summer or severe cold in winter, which were missed by weather forecast. This study suggests that the analysis of social media data can provide detailed information about forecast users' opinion in almost real time, which is impossible through survey or interview.

• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.21 no.4
• /
• pp.222-226
• /
• 2011

This study was conducted to identify the characteristics of analytical errors shown in the Korean quality control program on bulk asbestos analyses using polarized light microscopy (PLM). 179 participating laboratories were required to analyze 4 samples respectively and asked to classify each test sample as asbestos-containing (positive) or non-asbestos-containing (negative). For positive samples, participants were also asked to identify the type and semiquantitate the contents of asbestos present. The test results showed 21 (4%) false negative errors among 562 samples, 9 (6%) false positive errors among 154 samples and 53 (9%) asbestos identification errors among 562 samples. Most of false negative and positive errors were observed in a few types of samples. Higher frequencies of asbestos identification errors were shown in samples containing two or more types of asbestos and samples containing anthophyllite, tremolite or actinolite asbestos. For semiquantitative analyses, the ratios of mean to nominal weight contents were 2.1 for chrysotile and 2.9 for amphiboles. A tendency of over-estimation was observed in semiquantitative analyses using the visual estimation technique and higher in case of analyzing samples containing amphiboles than chrysotile. Coefficients of variation (CVs) of semiquantitative analytical results were 0.44~0.83 and 0.5~1.14 for samples containing chrysotile and amphibole asbestos, respectively.

• The Korean Journal of Cytopathology
• /
• v.8 no.1
• /
• pp.11-19
• /
• 1997

Clinical lymphadenopathies are subjected to fine needle aspiration cytology(FNAC) for diagnosing not only benign lesions but also malignant ones, as the first diagnostic procedure. While the diagnostic reliability in metastatic carcinoma is high, it is difficult to differentiate malignant lymphoma from reactive conditions. We evaluated the diagnostic reliability of FNAC in lymphadenopathy, and discuss the diagnostic limitation and its place in clinical practice in this study, Over 8 years from January 1988, FNAC of 1,216 lymphadenopathies were analyzed and among them 170 cases were compared with histopathology. The results are as follows. 1. Of ail the cases, 890 cases(73.2%) were diagnosed cytologically as benign, 312 cases(25.7%) as malignant, and 14 cases(1.1%) as unsatisfactory material. Reactive hyperplasia was diagnosed in 585 cases(65.7%) of the benign lesions, and among the malignant diseases, metastatic carcinoma was diagnosed in 248 cases(79.5%), and malignant lymphoma in 62 cases(19.9%). 2. The overall diagnostic accuracy was 89.2%, and no false positive case and 9 false negative results were observed among 170 cases which were proven by histopathology. Six cases of sampling error of false negative diagnoses included 3 of metastatic carcinomas and 3 of malignant lymphomas. The causes were difference between aspiration and biopsy site, poor fixation, or scanty cellularity with bloody smear. All 3 cases of misinterpretation error were malignant lympliomas, one of mixed type on biopsy which was diagnosed as reactive hyperplasia cytologically. In summary, FNAC technique is thought to be useful in the initial diagnosis of lymphadenopathies as well as in the follow-up of patients with known malignancy. Although the results of malignant lymphoma was less accurate than other malignant lesions, the application of strict cytologic criteria or lymphoid marker studies of aspiration material will reduce the false negative rate.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.7
• /
• pp.49-55
• /
• 2016

In digital forensic images, there is a serious problem that is distributed with various image types. For the problem solution, this paper proposes a classification algorithm of the forensic image types. The proposed algorithm extracts the 21-dim. feature vector with the contrast and energy from GLCM (Gray Level Co-occurrence Matrix), and the entropy of each image type. The classification test of the forensic images is performed with an exhaustive combination of the image types. Through the experiments, TP (True Positive) and FN (False Negative) is detected respectively. While it is confirmed that performed class evaluation of the proposed algorithm is rated as 'Excellent(A)' because of the AUROC (Area Under Receiver Operating Characteristic Curve) is 0.9980 by the sensitivity and the 1-specificity. Also, the minimum average decision error is 0.1349. Also, at the minimum average decision error is 0.0179, the whole forensic image types which are involved then, our classification effectiveness is high.