• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.363-366
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• 2016

In this paper, we propose a method for detecting pedestrian, problem-solving situations that occur in a cross. When a pedestrian crossing and other, there occurs a problem of detecting the other pedestrians for detecting a specific pedestrian in the image. The proposed method for solving the problem is as follows. First, select a specific pedestrian detected by bounding box, and extracts the area as a template. Detecting a pedestrian from the image using the HOG, and designated as a candidate region. The final choice of the pedestrian detected by comparison with a candidate pedestrian with the specific pedestrian extracted for template. In comparison, using the Template matching, Histogram comparison and LBP.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.3
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• pp.284-290
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• 2012

Infrared thermography uses infrared energy radiated from any objects above absolute zero temperature, and the range of its application has been constantly broadened. As one of the active test techniques detecting radiant energy generated when energy is applied to an object, ultrasound infrared thermography is a method of detecting defects through hot spots occurring at a defect area when 15~100 kHz of ultrasound is excited to an object. This technique is effective in detecting a wide range affected by ultrasound and vibration in real time. Especially, it is really effective when a defect area is minute. Therefore, this study conducted thermography through lock-in signal processing when an actual defect exists inside the austenite STS304 seamless pipe, which simulates thermal fatigue cracks in a nuclear power plant pipe. With ultrasound excited, this study could detect defects on the rear of a pipe by using an aluminium reflector. Besides, by regulating the angle of the aluminium reflector, this study could detect both front and rear defects as a single infrared thermography image.

• Korean Management Science Review
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• v.13 no.2
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• pp.1-11
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• 1996

Two different methods to extract the license plate area of a vehicle have been used for automatic recognition purposes. One method is with a color vision system and the other is with an edge detecting operator. The system with color vision has some problems if the colors of license plate and vehicle's body are similar. The various plate colors in Korea also drops the system performance. The edge detecting operator also has a problem for a real time processing since it performs on all pixels of the scene. In this paper a possible method using gray level vision system and available pre-known information of license plates is suggested. The suggested procedure searches the lower boundary of the plate by counting high contrast points between one and near pixel from the bottom line of the scene. It finds the upper boundary from the bottom line by adding number plate height after finding the lower boundary. The left and right boundaries are found by similar processes.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.203-208
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• 1999

By tilting the reference mirror of Twynman-Green interferometer having a reference mirror and a moving mirror, firinge pattern composed of bright and dark parallel lines can be obtained and the fringe pattern is shifted according to the displacement of the mowing mirror. Several studies are executed for displacement measurement by detecting the intensity of the fringe with photo-diodes having small detecting area. In this study, to improve the sensitivity and robustness, the intensity of fringe is detected by using a large-area quadratic photo-diode masked with a grating panel having four kinds of binary grating having phase-difference of 0, {\pi}$/4, {\pi}$/2, 3 {\pi}$/4. The phase of the fringe is calculated with a simple 4-buckets algorithm. A experimental result shows that standard deviation of 5.653 nm is obtained comparing with a capacitive type gap sensor having nearly 1 nm accuracy.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.4
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• pp.227-232
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• 2018

In this paper, we propose a faster object detection and tracking method using Deep Learning, UAV(unmanned aerial vehicle), Kalman filter and YOLO(You Only Look Once)v3 algorithms. The performance of the object tracking system is decided by the performance and the accuracy of object detecting and tracking algorithms. So we applied to the YOLOv3 algorithm which is the best detection algorithm now at our proposed detecting system and also used the Kalman Filter algorithm that uses a variable detection area as the tracking system. In the experiment result, we could find the proposed system is an excellent result more than a fixed area detection system.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.5
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• pp.259-265
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• 2023

Pedestrian detection is used to effectively detect pedestrians in various situations based on deep learning. Pedestrian detection has difficulty detecting pedestrians due to problems such as camera performance, pedestrian description, height, and occlusion. Even in the same pedestrian, performance in detecting them can differ according to the height of the pedestrian. The height of general pedestrians encompasses various scales, such as those of infants, adolescents, and adults, so when the model is applied to one group, the extraction of data becomes inaccurate. Therefore, this study proposed a pedestrian detection method that fine-tunes the pedestrian area by Refining Layer and Feature Concatenation to consider various heights of pedestrians. Through this, the score and location value for the pedestrian area were finely adjusted. Experiments on four types of test data demonstrate that the proposed model achieves 2-5% higher average precision (AP) compared to Faster R-CNN and DRPN.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.6
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• pp.561-566
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• 2016

A subway platform is equipped with screen doors in oder to avoid accidents of passengers, where Area sensors are installed for detecting obstacles in the screen doors. However, there exist frequent operating errors in screen doors due to dusts, sunlight, snow, and bugs. It is required to develope a detection device which reduces errors and elaborates detection function. In this paper, we compared the detection rates of the Area sensor the 3D sensor using CCTV-based image data with installing sensors at the screen door in Munyang station Daegu, where 3D sensor is applied with the space division multiple detection algorithms. It is measured that the detection rate of 3D sensor and Area sensor is approximately 89.61% and 78.88%, respectively. The results confirmed that 3D senor has higher detection rate compared with Area sensor with the rate of 6.87~9.79%, and 3D sensor has benefit in the aspect of installation fee.

• The Korean Journal of Cytopathology
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• v.17 no.1
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• pp.1-17
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• 2006

Urine cytology is the most useful technique for detecting either primary or recurrent neoplasms in the urinary tract. Although urine cytology is the traditional method of detecting these neoplasms, its diagnostic accuracy has been underevaluated because of low sensitivity. The cytologic interpretation of urinary samples is not an easy task, even with some expertise in this area, for many reasons. In low-grade urothelial carcinoma, no reliable or reproducible diagnostic cytologic criteria can be provided because of the lack of obvious cytologic features of malignancy, which is one of the main factors lowering its diagnostic accuracy. Many diagnostic markers have been developed recently to enhance its diagnostic yield, but the results have not been satisfactory. However, urine cytology plays a role in detecting high-grade urothelial carcinoma or its precursor lesions. It still shows higher specificity than any of the newly developed urine markers. Understanding the nature of urine samples and the nature of neoplasms of the urinary tract, recognizing their cytologic features fully, and using cytologic findings under appropriate conditions in conjunction with a detailed clinical history would make urine cytology a very valuable diagnostic tool.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.193-203
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• 2009

Snow cover is one of the important parameters since it determines surface energy balance and its variation. To classify snow and cloud from satellite data is very important process when inferring land surface information. Generally, misclassified cloud and snow pixel can lead directly to error factor for retrieval of surface products from satellite data. Therefore, in this study, we perform algorithm for detecting snow cover area with remote sensing data. We just utilize visible reflectance, and infrared channels rather than using NDSI (Normalized Difference Snow Index) which is one of optimized methods to detect snow cover. Because COMS MI (Meteorological Imager) channels doesn't include near infra-red, which is used to produce NDSI. Detecting snow cover with visible channel is well performed over clear sky area, but it is difficult to discriminate snow cover from mixed cloudy pixels. To improve those detecting abilities, brightness temperature difference (BTD) between 11 and 3.7 is used for snow detection. BTD method shows improved results than using only visible channel.

• Journal of Environmental Impact Assessment
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• v.10 no.1
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• pp.1-8
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• 2001

Detection of red tides by satellite remote sensing can be done either by detecting enhanced level of chlorophyll pigment or by detecting changes in the spectral composition of pixels. Using chlorophyll concentration, however, is not effective currently due to the facts: 1) Chlorophyll-a is a universal pigment of phytoplankton, and 2) no accurate algorithm for chlorophyll in case 2 water is available yet. Here, red band algorithm, classification and PCA (Principal Component Analysis) techniques were applied for detecting patches of Cochlodinium polykrikoides red tides which occurred in Korean waters in 1995. This dinoflagellate species appears dark red due to the characteristic pigments absorbing lights in the blue and green wavelength most effectively. In the satellite image, the brightness of red tide pixels in all the three visible bands were low making the detection difficult. Red band algorithm is not good for detecting the red tide because of reflectance of suspended sediments. For supervised classification, selecting training area was difficult, while unsupervised classification was not effective in delineating the patches from surrounding pixels. On the other hand, PCA gave a good qualitative discrimination on the distribution compared with actual observation.