• 한국표면공학회지
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• 제56권6호
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• pp.353-370
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• 2023

Corrosion detection and analysis is a very important topic in reducing costs and preventing disasters. Recently, image processing techniques have been widely applied to corrosion identification and analysis. In this work, we briefly introduces traditional image processing techniques and machine learning algorithms applied to detect or analyze corrosion in various fields. Recently, machine learning, especially CNN-based algorithms, have been widely applied to corrosion detection. Additionally, research on applying machine learning to region segmentation is very actively underway. The corrosion is reddish and brown in color and has a very irregular shape, so a combination of techniques that consider color and texture, various mathematical techniques, and machine learning algorithms are used to detect and analyze corrosion. We present examples of the application of traditional image processing techniques and machine learning to corrosion detection and analysis.

• 한국위성정보통신학회논문지
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• 제12권3호
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• pp.69-73
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• 2017

본 논문에서는 기계학습 기반의 실시간 이미지 인식 알고리즘을 개발하고 개발한 알고리즘의 성능을 테스트 하였다. 실시간 이미지 인식 알고리즘은 기계 학습된 이미지 데이터를 바탕으로 실시간으로 입력되는 이미지를 인식한다. 개발한 실시간 이미지 인식 알고리즘의 성능을 테스트하기 위해 자율주행 자동차 분야에 적용해보았고 이를 통해 개발한 실시간 이미지 인식 알고리즘의 성능을 확인해보았다.

• 센서학회지
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• 제30권2호
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• pp.76-81
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• 2021

In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.

• Journal of Platform Technology
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• 제8권1호
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• pp.16-23
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• 2020

상표권 보호를 위한 상표 이미지의 진위 결정에 심층 신경망인 합성곱 신경망을 이용할 수 있다. 이를 위해, 상표로 등록되어 있는 한 장의 상표 이미지를 반복적으로 학습하는 것은 기계학습의 성능을 감소시키는 원인이 된다. 따라서, 이러한 응용에서 학습 데이터는 다양한 방법으로 생성된다. 하지만 대상 이미지가 회전되어 있으면 원본이라 하더라도 인식하지 못하거나 위조 상표로 분류되기도 한다. 본 논문에서는 회전한 상표 이미지의 진위 결정을 위한 기계학습 데이터의 확장 방법을 제안한다. 본 논문에서 제안하는 학습 데이터 확장 방법은 기울어진 이미지를 생성하고 이를 학습 데이터로 사용하는 것이다. 본 논문에서 제안하는 학습 데이터 확장 방법의 유효성 검증을 위해 대학의 로고를 대상으로 학습 데이터를 생성하였으며, 이를 활용하여 합성곱 신경망을 학습시킨 후 검증용 데이터를 이용하여 정확도를 평가하였다. 정확도 평가 결과에 따르면 본 논문에서 제안한 방법으로 생성한 학습 데이터를 활용하면 회전한 상표를 대상으로 한 진위 여부 결정에 합성곱 신경망을 활용할 수 있다.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.364-373
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• 2022

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

• 대한임베디드공학회논문지
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• 제17권1호
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• pp.41-49
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• 2022

This paper proposes a novel lane detection algorithm based on inverse perspective transformation and machine learning in lightweight embedded system. The inverse perspective transformation method is presented for obtaining a bird's-eye view of the scene from a perspective image to remove perspective effects. This method requires only the internal and external parameters of the camera without a homography matrix with 8 degrees of freedom (DoF) that maps the points in one image to the corresponding points in the other image. To improve the accuracy and speed of lane detection in complex road environments, machine learning algorithm that has passed the first classifier is used. Before using machine learning, we apply a meaningful first classifier to the lane detection to improve the detection speed. The first classifier is applied in the bird's-eye view image to determine lane regions. A lane region passed the first classifier is detected more accurately through machine learning. The system has been tested through the driving video of the vehicle in embedded system. The experimental results show that the proposed method works well in various road environments and meet the real-time requirements. As a result, its lane detection speed is about 3.85 times faster than edge-based lane detection, and its detection accuracy is better than edge-based lane detection.

• IEIE Transactions on Smart Processing and Computing
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• 제3권6호
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• pp.366-371
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• 2014

This paper reports a machine learning approach for image object detection. Object detection and localization in a wild image, such as a STL-10 image dataset, is very difficult to implement using the traditional computer vision method. A convolutional neural network is a good approach for such wild image object detection. This paper presents an object detection application using a convolutional neural network with pretrained feature vector. This is a very simple and well organized hierarchical object abstraction model.

• 대한임베디드공학회논문지
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• 제16권1호
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• pp.17-27
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• 2021

In this paper, we develop a galaxy image classification method based on hand-crafted features and machine learning techniques. Additionally, we provide an empirical analysis to reveal which combination of the techniques is effective for galaxy image classification. To achieve this, we developed a framework which consists of four modules such as preprocessing, feature extraction, feature post-processing, and classification. Finally, we found that the best technique for galaxy image classification is a method to use a median filter, ORB vector features and a voting classifier based on RBF SVM, random forest and logistic regression. The final method is efficient so we believe that it is applicable to embedded environments.

• Communications for Statistical Applications and Methods
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• 제26권6호
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• pp.591-610
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• 2019

Deep Learning is one of the machine learning methods to find features from a huge data using non-linear transformation. It is now commonly used for supervised learning in many fields. In particular, Convolutional Neural Network (CNN) is the best technique for the image classification since 2012. For users who consider deep learning models for real-world applications, Keras is a popular API for neural networks written in Python and also can be used in R. We try examine the parameter estimation procedures of Deep Neural Network and structures of CNN models from basics to advanced techniques. We also try to figure out some crucial steps in CNN that can improve image classification performance in the CIFAR10 dataset using Keras. We found that several stacks of convolutional layers and batch normalization could improve prediction performance. We also compared image classification performances with other machine learning methods, including K-Nearest Neighbors (K-NN), Random Forest, and XGBoost, in both MNIST and CIFAR10 dataset.

• 정보보호학회논문지
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• 제32권2호
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• pp.381-390
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• 2022

기계학습 이미지 인식 기술의 발전에 따라 이를 악성코드 검출에 적용하는 방법이 연구되고 있다. 그 대표적인 접근법으로 악성코드 파일을 이미지로 변환하고 이를 CNN과 같은 딥러닝 네트워크에 학습시켜 악성코드 검출과 분류를 수행하는 연구가 진행되어 의미 있는 결과가 발표되고 있다. 본 연구에서는 기계학습을 사용한 악성코드 검출에 효과적인 이미지 생성방법을 제시하고자 한다. 이를 위하여 이미지 생성의 여러 선택 요소에 따른 악성코드 검출의 성능을 실험하고 분석하였으며, 그 결과를 반영하여 명령어 흐름의 특성을 좀 더 명확하게 나타낼 수 있는 선형적 이미지 생성방법을 제시하고 이 방법이 악성코드 검출의 정밀도를 높일 수 있음을 실험을 통하여 보였다.