• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.105-108
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• 2020

This paper presents a real-time low-resolution face recognition method that uses a super-resolution technique. Conventional face recognition methods are limited by low accuracy resulting from the distance between the camera and objects. Although super-resolution methods have been developed to resolve this issue, they are not suitable for integrated face recognition systems. The proposed method recognizes faces with low resolution using key frame selection, super resolution, face detection, and recognition on real-time processing. Experiments involving several databases indicated that the proposed algorithm is superior to conventional methods in terms of face recognition accuracy.

• Journal of KIISE
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• v.44 no.7
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• pp.726-732
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• 2017

In practical distributed sensing and prediction applications over wireless sensor networks (WSN), environmental sensing activities are highly dynamic because of noisy sensory information from moving source signals. The recent distributed online convex optimization frameworks have been developed as promising approaches for solving approximately stochastic learning problems over network of sensors in a distributed manner. Negligence of mobility consequence in the original distributed saddle point algorithm (DSPA) could strongly affect the convergence rate and stability of learning results. In this paper, we propose an integrated sliding windows mechanism in order to stabilize predictions and achieve better convergence rates in cooperative detection of a moving source signal scenario.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.447-448
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• 2018

라벨인식과 같은 광학 문자 인식은 영상처리를 활용한 컴퓨터 비전의 대표적인 연구분야이다. 본 연구에서는 딥러닝 기반의 라벨인식 시스템을 고안하였다, 생산 라인에 적용되는 라벨인식 시스템은 인식 속도가 중요하기 때문에 기존의 R-CNN기반의 딥러닝 신경망보다 월등히 빠른 오브젝트 검출 시스템 YOLO를 활용하여 문자를 학습 및 인식 시스템을 개발하였다. 본 시스템은 기존 시스템에 근접하는 문자인식 정확도를 제공하고 자동으로 문자영역을 검출 가능하며, 라벨의 인쇄불량을 판독하도록 하였다. 또한 개발, 배포, 적용이 한번에 가능한 프레임워크를 통하여 생산현장에서 발생하는 다양한 이미지 처리에 활용될 전망이다.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.472-483
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• 2016

This paper proposes a blocking artifacts detection algorithm in frequency domain for MC-FRUC (Motion Compensated Frame Rate Up-Conversion). Conventional MC-FRUC algorithms occur blocking artifacts near interpolated block boundaries since motion compensation is performed from block-based motion vector. For efficiently decreasing blocking artifacts, this paper analyses frequency characteristics of the interpolated frame and reduces blocking artifacts on block boundaries. In experimental results the proposed method shows better subjective quality than some conventional FRUC method and also increases the PSNR(Peak Signal to Noise Ratio) value on average 0.45 dB compared with BDMC(Bi-Directional Motion Compensation).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1697-1702
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• 2017

This paper proposes a forward vehicle movement estimation algorithm for the image-based forward collision warning. The road region in the acquired image is designated as a region of interest (ROI) and a distance look up table (LUT) is made in advance. The distance LUT shows horizontal and vertical real distances from a reference pixel as a test vehicle position to any pixel as a position of a vehicle on the ROI. The proposed algorithm detects vehicles in the ROI, assigns labels to them, and saves their distance information using the distance LUT. And then the proposed algorithm estimates the vehicle movements such as approach distance, side-approaching and front-approaching velocities using distance changes between frames. In forward vehicle movement estimation test using road driving videos, the proposed algorithm makes the valid estimation of average 98.7%, 95.9%, 94.3% in the vehicle movements, respectively.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.9-18
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• 2019

Android Application Package (APK) is vulnerable to repackaging attacks. Therefore, obfuscation technology was applied inside the Android APK file to cope with repackaging attack. However, as more advanced reverse engineering techniques continue to be developed, fake Android APK files to be released. A new approach is needed to solve this problem. A blockchain is a continuously growing list of records, called blocks, which are linked and secured using cryptography. Each block typically contains a cryptographic hash of theprevious block, a timestamp and transaction data. Once recorded, the data inany given block cannot be altered retroactively without the alteration of all subsequent blocks. Therefore, it is possible to check whether or not theAndroid Mobile APK is forged by applying the blockchain technology. In this paper, we construct a discrimination DApp (Decentralized Application) against forgery Android Mobile APK by recording and maintaining the legitimate APK in the consortium blockchain framework like Hyperledger Fabric by Composer. With proposed DApp, we can prevent the forgery and modification of the appfrom being installed on the user's Smartphone, and normal and legitimate apps will be widely used.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.195-200
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• 2021

With the development of transportation and mobiles demand for tourism travel is increasing and related industries are also developing significantly. The combination of augmented reality and tourism contents one of the areas of digital media technology, is also actively being studied, and artificial intelligence is already combined with the tourism industry in various directions, enriching tourists' travel experiences. In this paper, we propose a system that scans miniature models produced by reducing tourist areas, finds the relevant tourist sites based on models learned using deep learning in advance, and provides relevant information and 3D models as AR services. Because model learning and object detection are carried out using YOLOv3 neural networks, one of various deep learning neural networks, object detection can be performed at a fast rate to provide real-time service.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.171-179
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• 2022

Self-Modifying-Code is a code that changes the code by itself during execution time. This technique is particularly abused by malicious code to bypass static analysis. Therefor, in order to effectively detect such malicious codes, it is important to identify self-modifying-codes. In the meantime, Self-modify-codes have been analyzed using dynamic analysis methods, but this is time-consuming and costly. If static analysis can detect self-modifying-code it will be of great help to malicious code analysis. In this paper, we propose a static analysis method to detect self-modified code for binary executable programs converted to LLVM IR and apply this method by making a self-modifying-code benchmark. As a result of the experiment in this paper, the designed static analysis method was effective for the standardized LLVM IR program that was compiled and converted to the benchmark program. However, there was a limitation in that it was difficult to detect the self-modifying-code for the unstructured LLVM IR program in which the binary was lifted and transformed. To overcome this, we need an effective way to lift the binary code.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.3
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• pp.101-108
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• 2020

Since the performance of deep learning techniques has recently been proven in the field of image processing, there are many attempts to perform classification, analysis, and detection of images using such techniques in various fields. Among them, the expectation of medical image analysis software, which can serve as a medical diagnostic assistant, is increasing. In this study, we are attention to the capsule endoscope image, which has a large data set and takes a long time to judge. The purpose of this paper is to distinguish the gastrointestinal landmarks and to estimate the gastrointestinal transition location that are common to all patients in the judging of capsule endoscopy and take a lot of time. To do this, we designed CNN-based Classifier that can identify gastrointestinal landmarks, and used it to estimate the gastrointestinal transition location by filtering the results. Then, we estimate gastrointestinal transition location about seven of eight patients entered the suspected gastrointestinal transition area. In the case of change from the stomach to the small intestine(pylorus), and change from the small intestine to the large intestine(ileocecal valve), we can check all eight patients were found to be in the suspected gastrointestinal transition area. we can found suspected gastrointestinal transition area in the range of 100 frames, and if the reader plays images at 10 frames per second, the gastrointestinal transition could be found in 10 seconds.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.539-548
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• 2001

In this paper, we propose that fast tracking algorithm for moving object is separated from background, using partial boundary line information. After detecting boundary line from input image, we track moving object by using the algorithm which takes boundary line information as feature of moving object. we extract moving vector on the imput image which has environmental variation, using high-performance BMA, and we extract moving object on the basis of moving vector. Next, we extract boundary line on the moving object as an initial feature-vector generating step for the moving object. Among those boundary lines, we consider a part of the boundary line in every direction as feature vector. And then, as a step for the moving object, we extract moving vector from feature vector generated under the information of the boundary line of the moving object on the previous frame, and we perform tracking moving object from the current frame. As a result, we show that the proposed algorithm using feature vector generated by each directional boundary line is simple tracking operation cost compared with the previous active contour tracking algorithm that changes processing time by boundary line size of moving object. The simulation for proposed algorithm shows that BMA operation is reduced about 39% in real image and tracking error is less than 2 pixel when the size of feature vector is [$10{\times}5$] using the information of each direction boundary line. Also the proposed algorithm just needs 200 times of search operation bout processing cost is varies by the size of boundary line on the previous algorithm.