• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.275-284
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• 2008

With increases in recent security requirements, biometric technology such as fingerprints, faces and iris recognitions have been widely used in many applications including door access control, personal authentication for computers, internet banking, automatic teller machines and border-crossing controls. Finger vein recognition uses the unique patterns of finger veins in order to identify individuals at a high level of accuracy. This paper proposes new device and methods for touchless finger vein recognition. This research presents the following five advantages compared to previous works. First, by using a minimal guiding structure for the finger tip, side and the back of finger, we were able to obtain touchless finger vein images without causing much inconvenience to user. Second, by using a hot mirror, which was slanted at the angle of 45 degrees in front of the camera, we were able to reduce the depth of the capturing device. Consequently, it would be possible to use the device in many applications having size limitations such as mobile phones. Third, we used the holistic texture information of the finger veins based on a LBP (Local Binary Pattern) without needing to extract accurate finger vein regions. By using this method, we were able to reduce the effect of non-uniform illumination including shaded and highly saturated areas. Fourth, we enhanced recognition performance by excluding non-finger vein regions. Fifth, when matching the extracted finger vein code with the enrolled one, by using the bit-shift in both the horizontal and vertical directions, we could reduce the authentic variations caused by the translation and rotation of finger. Experimental results showed that the EER (Equal Error Rate) was 0.07423% and the total processing time was 91.4ms.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.7
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• pp.9-15
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• 2019

The purpose of present study is to assess the maximum width of the surface cracks using the histogram analysis of image processing techniques in concrete structures. For this purpose, the concrete crack image is acquired by the camera. The image is Grayscale coded and Binary coded. After Binary coded image is Dilate and Erode coded, the image is then recognized as separated objects by applying Labeling techniques. Over time, dust and stains may occur naturally on the surface of concrete. The crack image of concrete may include shadows and reflections by lighting depending on a surrounding conditions. In general, concrete cracks occur in a continuous pattern and noise of image appears in the form of shot noises. Bilateral Blurring and Adaptive Threshold apply to the Grayscale image to eliminate these effects. The remaining noises are removed by the object area ratio to the Labeled area. The maximum numbers of pixels and its positions in the crack objects without noises are calculated in x-direction and y-direction by Histogram analysis. The widths of the crack are estimated by trigonometric ratio at the positions of the pixels maximum numbers for the Labeled objects. Finally, the maximum crack width estimated by the proposed method is compared to the crack width measured with the crack gauge. The proposed method by the present study may increase the reliability for the estimation of maximum crack width using image processing techniques in concrete surface images.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.483-489
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• 2017

The purpose of this study was to propose useful suggestions for enhancing reliability to determine the resistance against surface wetting, KS K 0590, by an imaging process. We validated the standard spray test rating chart for determining quantification standard using JAVA script-based imaging process program. All of the acquired images were processed with the image software, Image J (NIH, Nethesda, MD, USA). The study results are as follows. We established the surface area measurement-based quantitative criteria for determining resistance to surface wetting. The standard spray test rating chart was converted into a numerical standard which leads easy-to-determine ratings. We also validated the procedure for imaging treatment by analyzing quantitative data. We introduced the fluorescence image for determining ratings by enabling threshold settings and binary image conversion as an optimal imaging process. It is expected that imaging-based determination for resistant to surface wetting will serve as an accurate and reliable method for KS K 0590.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2430-2434
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• 2005

Automatic FPC punching instrument for the improvement of working condition and cost saving is introduced in this paper. FPC(flexible printed circuit) is used to detect the contact position of K/B and button like a cellular phone. Depending on the quality of the printed ink and position of reference punching point to the FPC, the resistance and current are varied to the malfunctioning values. The size of reference punching point is 2mm and the above. Because the punching operation is done manually, the accuracy of the punching degree is varied with operator's condition. Recently, The punching accuracy has deteriorated severely to the 2mm punching reference hall so that assembly of the K/B has hardly done. To improve this manual punching operation to the FPC, automatic FPC punching system is introduced. Precise mechanical parts like a 5-step stepping motor and ball screw mechanism are designed and tested and low cost PC camera is used for the sake of cost down instead of using high quality vision systems for the FA. 3D Mechanical design tool(Pro/E) is used to manage the exact tolerance circumstances and avoid design failures. Simulation is performed to make the complete vision based punching machine before assembly, and this procedure led to the manufacturing cost saving. As the image processing algorithms, dilation, erosion, and threshold calculation is applied to obtain an exact center position from the FPC print marks. These image processing algorithms made the original images having various noises have clean binary pixels which is easy to calculate the center position of print marks. Moment and Least square method are used to calculate the center position of objects. In this development circumstance, Moment method was superior to the Least square one at the calculation of speed and against noise. Main control panel is programmed by Visual C++ and graphical Active X for the whole management of vision based automatic punching machine. Operating modes like manual, calibration, and automatic mode are added to the main control panel for the compensation of bad FPC print conditions and mechanical tolerance occurring in the case of punch and die reassembly. Test algorithms and programs showed good results to the designed automatic punching system and led to the increase of productivity and huge cost down to law material like FPC by avoiding bad quality.

• Publications of The Korean Astronomical Society
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• v.30 no.3
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• pp.821-832
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• 2015

As a part of the short-period variability survey (SPVS) at Bohyunsan Optical Astronomy Observatory, we obtained time-series BV CCD images in the region of the open cluster NGC 1039 (M34). The observations were performed for 22 nights from July 29, 2008 to September 26, 2010. We also made LOAO observations for 10 days from September 18, 2009 to October 30, 2010 to confirm the small variabilties of ${\delta}$ Scuti-type variable stars. In this paper we presented the observational properties of 28 variable stars found in the region. They are seven ${\delta}$ Scuti-type variable stars, two ${\gamma}$ Doradus-type variable stars, four-teen eclipsing binary stars and five semi-long periodic or slow irregular variables, respectively. Only three variables were listed in the GCVS and the rest are newly discovered ones. We have performed multiple-frequency analysis to determine pulsation frequencies of the ${\delta}$ Scuti-type and ${\gamma}$ Doradus-type variable stars, using the discrete Fourier transform and linear least-square fitting methods. We also have derived the periods and amplitudes of 12 eclipsing binaries from the phase fitting method, and presented the light curves of all variable stars.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.25-35
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• 2014

This paper proposes a method of boosting-based classification for the purpose of real-time face detection. The proposed method uses depth images to ensure strong performance of face detection in response to changes in lighting and face size, and uses the depth difference feature to conduct learning and recognition through the I-MCTBoost classifier. I-MCTBoost performs recognition by connecting the strong classifiers that are constituted from weak classifiers. The learning process for the weak classifiers is as follows: first, depth difference features are generated, and eight of these features are combined to form the weak classifier, and each feature is expressed as a binary bit. Strong classifiers undergo learning through the process of repeatedly selecting a specified number of weak classifiers, and become capable of strong classification through a learning process in which the weight of the learning samples are renewed and learning data is added. This paper explains depth difference features and proposes a learning method for the weak classifiers and strong classifiers of I-MCTBoost. Lastly, the paper presents comparisons of the proposed classifiers and the classifiers using conventional MCT through qualitative and quantitative analyses to establish the feasibility and efficiency of the proposed classifiers.

• Investigative Magnetic Resonance Imaging
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• v.19 no.2
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• pp.76-87
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• 2015

Purpose: There is an ongoing search for a stent material that produces a reduced susceptibility artifact. This study evaluated the effect of manganese (Mn) content on the MRI susceptibility artifact of ferrous-manganese (Fe-Mn) alloys, and investigated the correlation between MRI findings and measurements of Fe-Mn microstructure on X-ray diffraction (XRD). Materials and Methods: Fe-Mn binary alloys were prepared with Mn contents varying from 10% to 35% by weight (i.e., 10%, 15%, 20%, 25%, 30%, and 35%; designated as Fe-10Mn, Fe-15Mn, Fe-20Mn, Fe-25Mn, Fe-30Mn, and Fe-35Mn, respectively), and their microstructure was evaluated using XRD. Three-dimensional spoiled gradient echo sequences of cylindrical specimens were obtained in parallel and perpendicular to the static magnetic field (B0). In addition, T1-weighted spin echo, T2-weighted fast spin echo, and $T2^*$weighted gradient echo images were obtained. The size of the low-intensity area on MRI was measured for each of the Fe-Mn binary alloys prepared. Results: Three phases of ${\alpha}^{\prime}$-martensite, ${\gamma}$-austenite, and ${\varepsilon}$-martensite were seen on XRD, and their composition changed from ${\alpha}^{\prime}$-martensite to ${\gamma}$-austenite and/or ${\varepsilon}$-martensite, with increasing Mn content. The Fe-10Mn and Fe-15Mn specimens comprised ${\alpha}^{\prime}$-martensite, the Fe-20Mn and Fe-25Mn specimens comprised ${\gamma}+{\varepsilon}$ phases, and the Fe-30Mn and Fe-35Mn specimens exhibited a single ${\gamma}$ phase. The size of the low-intensity areas of Fe-Mn on MRI decreased relative to its microstructure on XRD with increasing Mn content. Conclusion: Based on these findings, proper conditioning of the Mn content in Fe-Mn alloys will improve its visibility on MR angiography, and a Mn content of more than 25% is recommended to reduce the magnetic susceptibility artifacts on MRI. A reduced artifact of Fe-Mn alloys on MRI is closely related to the paramagnetic constitution of ${\gamma}$-austenite and/or ${\varepsilon}$-martensite.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.789-795
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• 2020

Many deaths have been reported due to the worldwide pandemic of COVID-19. In order to prevent the further spread of COVID-19, it is necessary to quickly and accurately read images of suspected patients and take appropriate measures. To this end, this paper introduces a deep learning-based COVID-19 chest X-ray reading technique that can assist in image reading by providing medical staff whether a patient is infected. First of all, in order to learn the reading model, a sufficient dataset must be secured, but the currently provided COVID-19 open dataset does not have enough image data to ensure the accuracy of learning. Therefore, we solved the image data number imbalance problem that degrades AI learning performance by using a Stacked Generative Adversarial Network(StackGAN++). Next, the DenseNet-based classification model was trained using the augmented data set to develop the reading model. This classification model is a model for binary classification of normal chest X-ray and COVID-19 chest X-ray, and the performance of the model was evaluated using part of the actual image data as test data. Finally, the reliability of the model was secured by presenting the basis for judging the presence or absence of disease in the input image using Grad-CAM, one of the explainable artificial intelligence called XAI.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1331-1340
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• 2020

Object detection in maritime means that the captain detects floating objects that has a risk of colliding with the ship using the computer automatically and as accurately as human eyes. In conventional ships, the presence and distance of objects are determined through radar waves. However, it cannot identify the shape and type. In contrast, with the development of AI, cameras help accurately identify obstacles on the sea route with excellent performance in detecting or recognizing objects. The computer must calculate high-volume pixels to analyze digital images. However, the CPU is specialized for sequential processing; the processing speed is very slow, and smooth service support or security is not guaranteed. Accordingly, this study developed maritime object detection software and implemented it with FPGA to accelerate the processing of large-scale computations. Additionally, the system implementation was improved through embedded boards and FPGA interface, achieving 30 times faster performance than the existing algorithm and a three-times faster entire system.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.29-37
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• 2024

In this paper, we propose an efficient image processing system to detect and track the movement of specific objects such as patients. The proposed system extracts the outline area of an object from a binarized difference image by applying a thinning algorithm that enables more precise detection compared to previous algorithms and is advantageous for mixed-mode design. The binarization and thinning steps, which require a lot of computation, are designed based on RTL (Register Transfer Level) and replaced with optimized hardware blocks through logic circuit synthesis. The designed binarization and thinning block was synthesized into a logic circuit using the standard 180n CMOS library and its operation was verified through simulation. To compare software-based performance, performance analysis of binary and thinning operations was also performed by applying sample images with 640 × 360 resolution in a 32-bit FPGA embedded system environment. As a result of verification, it was confirmed that the mixed-mode design can improve the processing speed by 93.8% in the binary and thinning stages compared to the previous software-only processing speed. The proposed mixed-mode system for object recognition is expected to be able to efficiently monitor patient movements even in an edge computing environment where artificial intelligence networks are not applied.