• Journal of the Korean Association of Geographic Information Studies
• /
• v.15 no.2
• /
• pp.103-112
• /
• 2012

We demonstrate the preliminary results of ship detection application using synthetic aperture radar (SAR) and automatic identification system (AIS) together. Multi-frequency and multi-temporal SAR images such as TerraSAR-X and Cosmo-SkyMed (X-band), and Radarsat-2 (C-band) are acquired over the West Sea in South Korea. In order to compare with SAR data, we also collected an AIS data. The SAR data are pre-processed considering by the characteristics of scattering mechanism as for sea surface. We proposed the "Adaptive Threshold Algorithm" for classification ship efficiently. The analyses using the combination of the SAR and AIS data with time series will be very useful to ship detection or tracing of the ship.

• Journal of Korea Multimedia Society
• /
• v.23 no.8
• /
• pp.953-964
• /
• 2020

Ever since media contents took over the world, the importance of typography has increased, and the influence of fonts has be n recognized. Nevertheless, the current Hangul font system is very poor and is provided passively, so it is practically impossible to understand and utilize all the shape characteristics of more than six thousand Hangul fonts. In this paper, the characteristics of Hangul font shapes were selected based on the Hangul structure of similar fonts. The stroke element detection training was performed by fine tuning Faster R-CNN Inception v2, one of the deep learning object detection models. We also propose a system that automatically extracts the stroke element characteristics from characters by introducing an automatic extraction algorithm. In comparison to the previous research which showed poor accuracy while using SVM(Support Vector Machine) and Sliding Window Algorithm, the proposed system in this paper has shown the result of 10 % accuracy to properly detect and extract stroke elements from various fonts. In conclusion, if the stroke element characteristics based on the Hangul structural information extracted through the system are used for similar classification, problems such as copyright will be solved in an era when typography's competitiveness becomes stronger, and an automated process will be provided to users for more convenience.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.7
• /
• pp.1429-1434
• /
• 2017

In automatic defect detection problem for spectacle lenses, it is important to extract lens area accurately. Many existing detection methods fail to do it due to insufficient minute noise removal. In this paper, we propose an automatic defect detection method using Bresenham algorithm and adaptive binarization strategy. After usual average binarization, we apply Bresenham algorithm that has the power in extracting ellipse shape from image. Then, adaptive binarization strategy is applied to the critical minute noise removal inside the lens area. After noise removal, We can also compute the influence factor of the defect based on the fuzzy logic with two membership functions such as the size of the defect and the distance of the defect from the center of the lens. In experiment, our method successfully extracts defects in 10 out of 12 example images that include CHEMI, MID, HL, HM type lenses.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.11
• /
• pp.2121-2127
• /
• 2017

In this paper, we propose a computer vision based automatic defect detection method from ceramic image for non-destructive testing. From region of interest of the image, we apply brightness enhancing stretching algorithm first. One of the strength of our method is that it is designed to detect defects of images obtained from various thicknesses, that is, 8, 10, 11, 16, and 22 mm. In other cases we apply histogram based binarization algorithm. However, for 8 mm case, it may have false positive cases due to weak brightness contrast between defect and noise. Thus, we apply modified fuzzy binarization algorithm for 8 mm case. From the experiment, we verify that the proposed method shows stronger result than our previous study that used Blob labelling for all five thickness cases as expected.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.8 no.10
• /
• pp.1443-1449
• /
• 2013

Automatic detection of vehicles in front is an integral component of many advanced driver-assistance system, such as collision mitigation, automatic cruise control, and automatic head-lamp dimming. Regardless day and night, tail-lights play an important role in vehicle detecting and status recognizing of driving in front. However, some drivers do not know the status of the tail-lights of vehicles. Thus, it is required for drivers to inform status of tail-lights automatically. In this paper, a recognition method of status of tail-lights based on video processing and recognition technology is proposed. Background estimation, optical flow and Euclidean distance is used to detect vehicles entering tollgate. Then saliency map is used to detect tail-lights and recognize their status in the Lab color coordinates. As results of experiments of using tollgate videos, it is shown that the proposed method can be used to inform status of tail-lights.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.12 no.2
• /
• pp.107-114
• /
• 2007

Islanding of PV systems occurs when the utility grid is removed but the PV systems continue to operate and provide power to local loads. Islanding is one of the serious problems in an electric power system connected with dispersed power sources. This can present safety hazards and the possibility of damage to other electric equipments. In the passive method, the voltage and frequency of PCC are measured and it determines islanding phenomena if their values excess the allowed limits. If the real and reactive power of RLC load and those of the PV system are closely matched, islanding phenomena can't be detected by the passive methods. Several active methods were proposed to detect islanding operation in the region where the passive method can not detect it. The most effective method is SFS method which was suggested by Sandia National Laboratory. In this paper, a new islanding detection method using automatic phase-shift is proposed and its validity is verified through the simulation and experimental results.

• Korean Journal of Remote Sensing
• /
• v.21 no.6
• /
• pp.505-516
• /
• 2005

The Communications, Oceanography and Meteorology Satellite(COMS) will be launched in 2008. Ground processing for COMS includes the process of automatic image navigation. Image navigation requires landmark detection by matching COMS images against landmark chips. For automatic image navigation, a matching must be performed automatically However, if matching results contain errors, the accuracy of Image navigation deteriorates. To overcome this problem, we propose use of a robust estimation technique called Random Sample Consensus (RANSAC) to automatically detect erroneous matching. We tested GOES-9 satellite images with 30 landmark chips that were extracted from the world shoreline database. After matching, mismatch results were detected automatically by RANSAC. All mismatches were detected correctly by RANSAC with a threshold value of 2.5 pixels.

• Imaging Science in Dentistry
• /
• v.51 no.3
• /
• pp.299-306
• /
• 2021

Purpose: This study aimed to propose a fully automatic landmark identification model based on a deep learning algorithm using real clinical data and to verify its accuracy considering inter-examiner variability. Materials and Methods: In total, 950 lateral cephalometric images from Yonsei Dental Hospital were used. Two calibrated examiners manually identified the 13 most important landmarks to set as references. The proposed deep learning model has a 2-step structure-a region of interest machine and a detection machine-each consisting of 8 convolution layers, 5 pooling layers, and 2 fully connected layers. The distance errors of detection between 2 examiners were used as a clinically acceptable range for performance evaluation. Results: The 13 landmarks were automatically detected using the proposed model. Inter-examiner agreement for all landmarks indicated excellent reliability based on the 95% confidence interval. The average clinically acceptable range for all 13 landmarks was 1.24 mm. The mean radial error between the reference values assigned by 1 expert and the proposed model was 1.84 mm, exhibiting a successful detection rate of 36.1%. The A-point, the incisal tip of the maxillary and mandibular incisors, and ANS showed lower mean radial error than the calibrated expert variability. Conclusion: This experiment demonstrated that the proposed deep learning model can perform fully automatic identification of cephalometric landmarks and achieve better results than examiners for some landmarks. It is meaningful to consider between-examiner variability for clinical applicability when evaluating the performance of deep learning methods in cephalometric landmark identification.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.2
• /
• pp.27-37
• /
• 2021

In this paper, we propose Automatic detection system of underground pipe which automatically detects underground pipe to help experts. Actual location of underground pipe does not match with blueprint due to various factors such as ground changes over time, construction discrepancies, etc. So, various accidents occur during excavation or just by ageing. Locating underground utilities is done through GPR exploration to prevent these accidents but there are shortage of experts, because GPR data is enormous and takes long time to analyze. In this paper, To analyze 3D GPR data automatically, we use 3D image segmentation, one of deep learning technique, and propose proper data generation algorithm. We also propose data augmentation technique and pre-processing module that are adequate to GPR data. In experiment results, we found the possibility for pipe analysis using image segmentation through our system recorded the performance of F1 score 40.4%.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.2
• /
• pp.355-360
• /
• 2023

Fire is a disaster that causes irreversible damage to many people due to personal injury and property damage. Various fire detection equipments are installed around us to detect and cope with it quickly. However, due to various problems such as artificial, environmental, and aging, fire detection equipment is activated even though it is not a actual fire, and there are many problems such as delaying the support to the necessary fire scene. In this paper, we analyze the non-fire alarm of the fire detection equipment and propose a system that enables the field staff to check the scene situation through the video as a way to prevent the mobilization due to the misinformation by checking the fire. The purpose of the present invention is to stably cope with a disaster by suggesting a customized automatic guidance system which induces a rapid evacuation by sending an evacuation guidance notification to a range of a fire occurrence neighboring area, and supports a rapid and accurate processing by a rapid dispatch of a firefighter, rather than a wide range of guidance such as an existing emergency disaster guidance letter when it is determined to be an actual fire through the confirmation procedure.