• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.1-11
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• 2023

In this paper, instead of using recurrent neural network, we compare a classification performance of time series imaging algorithms using convolution neural network. There are traditional algorithms that imaging time series data (e.g. GAF(Gramian Angular Field), MTF(Markov Transition Field), RP(Recurrence Plot)) in TSC(Time Series Classification) community. Furthermore, we compare STFT(Short Time Fourier Transform) algorithm that can acquire spectrogram that visualize feature of voice data. We experiment CNN's performance by adjusting hyper parameters of imaging algorithms. When evaluate with GunPoint dataset in UCR archive, STFT(Short-Time Fourier transform) has higher accuracy than other algorithms. GAF has 98~99% accuracy either, but there is a disadvantage that size of image is massive.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.171-187
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• 2023

When a person's face is recognized through a recording device such as a low-pixel surveillance camera, it is difficult to capture the face due to low image quality. In situations where it is difficult to recognize a person's face, problems such as not being able to identify a criminal suspect or a missing person may occur. Existing studies on face recognition used refined datasets, so the performance could not be measured in various environments. Therefore, to solve the problem of poor face recognition performance in low-quality images, this paper proposes a method to generate high-quality images by performing image quality improvement on low-quality facial images considering various environments, and then improve the performance of facial feature point detection. To confirm the practical applicability of the proposed architecture, an experiment was conducted by selecting a data set in which people appear relatively small in the entire image. In addition, by choosing a facial image dataset considering the mask-wearing situation, the possibility of expanding to real problems was explored. As a result of measuring the performance of the feature point detection model by improving the image quality of the face image, it was confirmed that the face detection after improvement was enhanced by an average of 3.47 times in the case of images without a mask and 9.92 times in the case of wearing a mask. It was confirmed that the RMSE for facial feature points decreased by an average of 8.49 times when wearing a mask and by an average of 2.02 times when not wearing a mask. Therefore, it was possible to verify the applicability of the proposed method by increasing the recognition rate for facial images captured in low quality through image quality improvement.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.65-73
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• 2023

In this paper, we propose a data augmentation method based on CNN(Convolutional Neural Network) learning for efficiently obtaining concrete crack image datasets. Real concrete crack images are not only difficult to obtain due to their unstructured shape and complex patterns, but also may be exposed to dangerous situations when acquiring data. In this paper, we solve the problem of collecting datasets exposed to such situations efficiently in terms of cost and time by using vector and thickness-based data augmentation techniques. To demonstrate the effectiveness of the proposed method, experiments were conducted in various scenes using U-Net-based crack detection, and the performance was improved in all scenes when measured by IoU accuracy. When the concrete crack data was not augmented, the percentage of incorrect predictions was about 25%, but when the data was augmented by our method, the percentage of incorrect predictions was reduced to 3%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.117-125
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• 2019

Deep Learning, which is being used in various fields recently, is being threatened with Adversarial Attack. In this paper, we experimentally verify that the classification accuracy is lowered by adversarial samples generated by malicious attackers in image classification models. We used MNIST dataset and measured the detection accuracy by injecting adversarial samples into the Autoencoder classification model and the CNN (Convolution neural network) classification model, which are created using the Tensorflow library and the Pytorch library. Adversarial samples were generated by transforming MNIST test dataset with JSMA(Jacobian-based Saliency Map Attack) and FGSM(Fast Gradient Sign Method). When injected into the classification model, detection accuracy decreased by at least 21.82% up to 39.08%.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.49-59
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• 2019

Generative adversarial networks(GANs) have received great attention in the machine learning field for their capacity to model high-dimensional and complex data distribution implicitly and generate new data samples from the model distribution. This paper investigates the model training methodology, architecture, and various applications of generative adversarial networks. Experimental evaluation is also conducted for generating synthetic image dataset for defense using two types of GANs. The first one is for military image generation utilizing the deep convolutional generative adversarial networks(DCGAN). The other is for visible-to-infrared image translation utilizing the cycle-consistent generative adversarial networks(CycleGAN). Each model can yield a great diversity of high-fidelity synthetic images compared to training ones. This result opens up the possibility of using inexpensive synthetic images for training neural networks while avoiding the enormous expense of collecting large amounts of hand-annotated real dataset.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.5
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• pp.476-486
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• 2022

Synthetic dynamic infrared image generation from the given virtual environment is being the primary goal to simulate the output of the infra-red(IR) camera installed on a vehicle to evaluate the control algorithm for various search & reconnaissance missions. Due to the difficulty to obtain actual IR data in complex environments, Artificial intelligence(AI) has been used recently in the field of image data generation. In this paper, CycleGAN technique is applied to obtain a more realistic synthetic IR image. We added the Structural Similarity Index Measure(SSIM) loss function to the L1 loss function to generate a more realistic synthetic IR image when the CycleGAN image is generated. From the simulation, it is applicable to the guided-missile flight simulation tests by using the synthetic infrared image generated by the proposed technique.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.551-552
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• 2021

In this paper, we present a multi-class image scene classification method based on transformation learning. ImageNet classifies multiple classes of natural scene images by relying on pre-trained network models on large image datasets. In the experiment, we obtained excellent results by classifying the optimized ResNet model on Kaggle's Intel Image Classification data set.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1187-1190
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• 2022

최근 홀로그램(Hologram)을 광학현미경(light microscopy)에 적용하여 시료의 두께를 측정하는 방식의 연구가 활발하게 진행되고 있다. 빛의 간섭패턴(Interference pattern)을 사용하여 시료의 두께를 측정할 수 있는 이유는 시료의 두께에 따라 빛의 위상(phase)이 달라지기 때문이다. 빛의 간섭패턴을 저장하는 홀로그램에서 위상만을 분리한 후 위상을 unwrapping 하면 물체의 두께를 측정할 수 있다. JPEG은 기존의 연구방식인 시료를 통과한 홀로그램 이미지를 직접 압축하는 object 압축방식을 사용한다. 하지만 본 논문에서는 object 압축방식과 달리 홀로그램 이미지를 직접 압축하지 않고 홀로그램 이미지에서 시료의 위상(phase)만을 분리하여 위상 도메인에서 압축하는 방식을 취하였다. 이를 통해 object 압축방식에 비해 위상 도메인 압축방식에서 모든 데이터셋에 대하여 평균적으로 0.0003~0.0142 radian의 성능향상을 이룰 수 있었다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.72-75
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• 2022

야외 환경을 카메라로 촬영한 일반 영상에서 텍스트 이미지를 검출하고 인식하는 기술은 로봇 비전, 시각 보조 등의 기반이 되는 기술로 활용될 수 있어 매우 중요한 기술이다. 하지만 저해상도의 텍스트 이미지의 경우 텍스트 이미지에 포함된 노이즈나블러 등이 더 두드러지기 때문에 텍스트 내용을 인식하는 것이 어렵다. 이에 본 논문은 일반 영상에서의 저해상도 한글 및 영어 텍스트에 대한 이미지 초해상화를 통해 텍스트 인식 정확도를 개선하였다. 트랜스포머에 기반한 모델로 한글 및 영어 텍스트에 대한 이미지 초해상화를 수행하였으며, 영어 및 한글 데이터셋에 대해 제안한 초해상화 방법을 적용했을 때 그렇지 않을 때보다 텍스트 인식 성능이 개선되는 것을 확인하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.85-87
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• 2022

최근 CCTV 출입 기록, 휴대폰 보안, 스마트 매장 등에서 얼굴 인식을 통해 개인을 식별하는 기술이 널리 사용되고 있다. 카메라의 각도, 조명, 사람의 움직임 등 얼굴 인식에 많은 외부 환경이 영향을 미치고 있지만 그중에서도 실제 영상에서 얼굴이 차지하는 영역이 작아 저해상도 얼굴 인식에 어려움을 겪고 있다. 이러한 문제점을 해결하고자 본 논문에서는 이미지 해상도가 얼굴 인식에 끼치는 영향을 알아보고 이미지 초해상화를 통해 얼굴 인식 성능을 개선하고자 한다. 쌍선형, 양3차 회선 보간법과 딥러닝 기반의 이미지 초해상화 모델인 RCAN을 이용하여 업스케일링한 데이터셋에 대해 학습한 ArcFace를 통해 얼굴 검증 평가를 진행하였다. 고해상도 이미지는 얼굴 인식 성능을 향상시키며, RCAN을 사용한 이미지 초해상화가 보간법을 사용한 방법보다 더 좋은 성능을 보였다.