• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.6
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• pp.38-44
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• 2019

A Convolution Neural Network(CNN) model was utilized to detect surface cracks in asphalt concrete pavements. The CNN used for this study consists of five layers with 3×3 convolution filter and 2×2 pooling kernel. Pavement surface crack images collected by automated road surveying equipment was used for the training and testing of the CNN. The performance of the CNN was evaluated using the accuracy, precision, recall, missing rate, and over rate of the surface crack detection. The CNN trained with the largest amount of data shows more than 96.6% of the accuracy, precision, and recall as well as less than 3.4% of the missing rate and the over rate.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.11a
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• pp.66-69
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• 2017

본 논문에서는 깊은 합성 곱 신경망 (Deep Convolutional Neural Network) 를 이용해서 SAR (Synthetic Aperture Radar) 영상의 반전 잡음 (speckle noise) 성분을 제거하는 기법을 제안하고자 한다. Deep Convolutional Neural Network는 이미지의 데이터 특성에 적합한 딥 러닝 방법이고, 이는 SAR 위성영상의 반전 잡음 제거에 사용해도 효과적이다. 반전 잡음 필터 모델 추정을 위한 학습은 임의로 반전 잡음을 합성한 트레이닝 이미지들과 원본 트레이닝 이미지들을 이용한 회귀모델을 통해 진행된다. 학습을 통해 얻은 반전 잡음 필터는 기존 알고리즘에 비해 우수한 외곽선 보존 성능을 나타냄을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.340-342
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• 2021

흔히 DeepFake로 불리는 GAN 기술은 소스 영상과 타겟 이미지를 합성하여 타겟 이미지 내의 사람이 소스 영상에서 나타나도록 합성하는 기술이다. 이러한 GAN 기반 영상 합성 기술은 2018년을 기점으로 급격한 성장세를 보이며 다양한 산업에 접목되어지고 있으나 학습 모델을 얻는 데 걸리는 시간이 너무 오래 소요되고, 감정 표현을 인지하는 데 어려움이 있었다. 본 논문에서는 상기 두가지 문제를 해결하기 위해 Facial Action Coding System(FACS) 및 음성 합성 기술[4]을 적용한 가상 아바타 생성 방법에 대해 제안하고자 한다.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.279-285
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• 2022

In this paper, as a liberal arts course for non-majors, we developed a basic practice case for convolutional neural networks, which is essential for designing a basic convolutional neural network course curriculum. The developed practice case focuses on understanding the working principle of the convolutional neural network and uses a spreadsheet to check the entire visualized process. The developed practice case consisted of generating supervised learning method image training data, implementing the input layer, convolution layer (convolutional layer), pooling layer, and output layer sequentially, and testing the performance of the convolutional neural network on new data. By extending the practice cases developed in this paper, the number of images to be recognized can be expanded, or basic practice cases can be made to create a convolutional neural network that increases the compression rate for high-quality images. Therefore, it can be said that the utility of this convolutional neural network basic practice case is high.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.51-57
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• 2021

In this paper, we propose a method to find the DoF(Depth of field) that is blurred in an image by focusing and out-focusing the camera through a efficient convolutional neural network. Our approach uses the RGB channel-based cross-correlation filter to efficiently classify the DoF region from the image and build data for learning in the convolutional neural network. A data pair of the training data is established between the image and the DoF weighted map. Data used for learning uses DoF weight maps extracted by cross-correlation filters, and uses the result of applying the smoothing process to increase the convergence rate in the network learning stage. The DoF weighted image obtained as the test result stably finds the DoF region in the input image. As a result, the proposed method can be used in various places such as NPR(Non-photorealistic rendering) rendering and object detection by using the DoF area as the user's ROI(Region of interest).

• Archives of design research
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• v.11 no.2
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• pp.59-68
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• 1998

A work as human's creatures are transformed to bits and multimedia. In the modem society which is governed by simulation, infornlation is symbolized and becoming easily reached to anyone. Thus, collections of image and composition can show a new world beyond the ethical viewpoint. The problem with worries about the differnce between the original and a copy and disappearence of the meaning in postmodern era is the problem of how to set the intellectual properties and copyrights.In this situation, sharing of information according to the theoretical concepts and process of digitalization of photos in the era of information-intensive society was presented. The ethics of intellectual properties and copyrights and in postmordern era were driven. After reviewing the intellectual properties in the era of national information infrastructure of twenty-first century and by identifying the problems, the directions of what we have to do as image makers, and the intellectual properties and copyright.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.3
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• pp.513-520
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• 2020

Recently, the development of a vision inspection system using machine learning has become more active. This study seeks to develop a defect inspection model using machine learning. Defect detection problems for images correspond to classification problems, which are the method of supervised learning in machine learning. In this study, defect detection models are developed based on algorithms that automatically extract features and algorithms that do not extract features. One-dimensional CNN and two-dimensional CNN are used as algorithms for automatic extraction of features, and MLP and SVM are used as algorithms for non-extracting features. A defect detection model is developed based on four models and their accuracy and AUC compare based on AUC. Although image classification is common in the development of models using CNN, high accuracy and AUC is achieved when developing SVM models by converting pixels from images into RGB values in this study.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.37-43
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• 2021

In this paper, we study how objects from front and back cameras can be recognized in real time in a mobile environment to segment regions of object pixels and synthesize them through image processing. To this work, we applied DeepLabV3 machine learning model to dual cameras provided by Apple's iOS. We also propose methods using Core Image and Core Graphics libraries from Apple for image synthesis and postprocessing. Furthermore, we improved CPU usage than previous works and compared the throughput rates and results of Depth and DeepLabV3. Finally, We also developed a camera application using these two methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.69-70
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• 2019

본 논문에서는 토지 정보를 분류하는 연구를 수행하기 위한 이미지 데이터 셋을 개발하는데 필요한 반자동 annotation 도구를 제안한다. 논문에서 제안하는 도구는 합성개구레이더 영상을 입력으로 하고, 물/경작지/숲/건물을 구분하는 시스템을 개발하기 위해서 만들어진 것이나, 다른 목적을 가지는 토지 관련 이미지 분석 시스템의 개발에 사용될 수 있다. 제안하는 도구는 합성개구레이더 영상이 GPS 정보와 같이 입력되었을 때, GPS 정보에 기반하여 토지지목정보를 불러오고, 이를 재정리하여 1차 레이블링 결과를 자동적으로 생성한다. 국가에서 관리하는 토지지목정보는 개발하고자 하는 시스템의 분류 기준에 많은 부분 도움이 되긴 하지만, 일부분 차이점이 있기 때문에 이를 다시 수동으로 수정하는 도구을 동작하여 annotation이 완료된 이미지 데이터를 구축한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.293-294
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• 2017

본 논문에서는 CNN 기반 서명인식에서 시간정보를 이용하여 위조서명을 보다 정확하게 판별하는 방법을 제안한다. 시간정보를 이용하는 첫 번째 방법은 서명하는 전체 시간을 동일한 개수의 등 간격으로 나누어 각각의 이미지를 얻고 이를 합성하여 이용하는 방법이다. 두 번째 방법은 동일한 개수의 등 간격으로 나누어진 각각의 이미지를 CNN-LSTM 으로 판별하는 방법이다. 동일한 개수의 등 간격으로 나누어진 이미지들에는 서명의 속도에 따른 모양의 차이가 발생하기 때문에 비록 최종 서명의 모양이 원본과 매우 유사하다고 하더라도 속도가 다른 경우 위조임을 판별할 수 있다. 두 명의 서명에 대하여 실험을 한 결과 최종 서명이 매우 유사하더라도 속도가 다른 경우 위조로 판별할 수 있음을 보였다. 다만 이미지 합성 과정에 만들어진 새로운 정보로 인하여 진짜 서명을 가짜로 판별할 수 있는 가능성도 늘어날 수 있음을 확인하였다.