• 한국멀티미디어학회논문지
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• 제23권7호
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• pp.803-811
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• 2020

This paper proposes an image classification algorithm that transforms the number of convolution layers in the residual block of ResNet, CNN's representative method. The proposed method modified the structure of 34/50 layer of ResNet structure. First, we analyzed the performance of small and many convolution layers for the structure consisting of only shortcut and 3 × 3 convolution layers for 34 and 50 layers. And then the performance was analyzed in the case of small and many cases of convolutional layers for the bottleneck structure of 50 layers. By applying the results, the best classification method in the residual block was applied to construct a 34-layer simple structure and a 50-layer bottleneck image classification model. To evaluate the performance of the proposed image classification model, the results were analyzed by applying to the cifar10 dataset. The proposed 34-layer simple structure and 50-layer bottleneck showed improved performance over the ResNet-110 and Densnet-40 models.

• 한국멀티미디어학회논문지
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• 제23권9호
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• pp.1129-1138
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• 2020

The performance of convolutional deep learning networks is generally determined according to parameters of target dataset, structure of network, convolution kernel, activation function, and optimization algorithm. In this paper, a genetic algorithm is used to select the appropriate deep learning model and parameters for Alzheimer's classification and to compare the learning results with preliminary experiment. We compare and analyze the Alzheimer's disease classification performance of VGG-16, GoogLeNet, and ResNet to select an effective network for detecting AD and MCI. The simulation results show that the network structure is ResNet, the activation function is ReLU, the optimization algorithm is Adam, and the convolution kernel has a 3-dilated convolution filter for the accuracy of dementia medical images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권6호
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• pp.3143-3165
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• 2017

We present a computerized framework for producing color pastel painting from the visual information extracted from a photograph. To express color pastel painting, we propose a multi-layered framework where each layer possesses pastel stroke patterns of different colors. The stroke patterns in the separate layers are merged by a rendering equation based on a participating media rendering scheme. To produce the stroke patterns in each layer, we review the physical properties of pastels and the mechanism of a convolution framework, which is the most widely used scheme to simulate stick-shaped media such as pencils. We devise the following computational models to extend the convolution framework to produce pastel strokes: a bold noise model, which mimics heavy and clustered deposition of pigment, and a thick convolution filter model, which produces various pastel stroke patterns. We also design a stochastic color coordination scheme to mimic pastel artists' color expression and to separate strokes in different layers. To demonstrate the soundness of approach, we conduct several experiments using the models and compare the results with existing works or real pastel paintings. We present the results for several pastel paintings to demonstrate the excellent performance of our framework.

• 한국멀티미디어학회논문지
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• 제20권2호
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• pp.126-133
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• 2017

The conventional hand-crafted features used to track objects have limitations in object representation. Convolutional neural networks, which show good performance results in various areas of computer vision, are emerging as new ways to break through the limitations of feature extraction. CNN extracts the features of the image through layers of multiple layers, and learns the kernel used for feature extraction by itself. In this paper, we use the feature map extracted from the convolution layer of the convolution neural network to create an outline model of the object and use it for tracking. We propose a method to adaptively update the outline model to cope with various environment change factors affecting the tracking performance. The proposed algorithm evaluated the validity test based on the 11 environmental change attributes of the CVPR2013 tracking benchmark and showed excellent results in six attributes.

• 스마트미디어저널
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• 제12권7호
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• pp.52-58
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• 2023

Alzheimer's disease is one type of dementia, the symptoms can be treated by detecting the disease at its early stages. Recently, many computer-aided diagnosis using magnetic resonance image(MRI) have shown a good results in the classification of AD. Taken these MRI images and feed to Free surfer software to extra the features. In consideration, using T1-weighted images and classifying using the convolution neural network (CNN) model are proposed. In this paper, taking the subjects from ADNI of subcortical and cortical features of 190 subjects. Consider the study to reduce the complexity of the model by using the single layer in the Res-Net, VGG, and Alex Net. Multi-class classification is used to classify four different stages, CN, EMCI, LMCI, AD. The following experiment shows for respective classification Res-Net, VGG, and Alex Net with the best accuracy with VGG at 96%, Res-Net, GoogLeNet and Alex Net at 91%, 93% and 89% respectively.

• 스마트미디어저널
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• 제13권6호
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• pp.90-97
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• 2024

This paper focuses on detecting Alzheimer's Disease (AD). The most usual form of dementia is Alzheimer's disease, which causes permanent cause memory cell damage. Alzheimer's disease, a neurodegenerative disease, increases slowly over time. For this matter, early detection of Alzheimer's disease is important. The purpose of this work is using Magnetic Resonance Imaging (MRI) to diagnose AD. A Convolution Neural Network (CNN) model, Reset, and VGG the pre-trained learning models are used. Performing analysis and validation of layers affects the effectiveness of the model. T1-weighted MRI images are taken for preprocessing from ADNI. The Dataset images are taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI). 3D MRI scans into 2D image slices shows the optimization method in the training process while achieving 96% and 94% accuracy in VGG 16 and ResNet 18 respectively. This study aims to classify AD from brain 3D MRI images and obtain better results.

• 전기전자학회논문지
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• 제23권4호
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• pp.1250-1256
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• 2019

3차원 프린터나 깊이 카메라 등을 이용하면 실세계의 3차원 메쉬 데이터를 손쉽게 생성할 수 있지만, 이렇게 생성된 데이터에는 필연적으로 불필요한 노이즈가 포함되어 있다. 따라서, 온전한 3차원 메쉬 데이터를 얻기 위해서는 메쉬 디노이징 작업이 필수적이다. 하지만 기존의 수학적인 디노이징 방법들은 전처리 작업이 필요하며 3차원 메쉬의 일부 중요한 특징들이 사라지는 문제점이 있다. 본 논문에서는 이러한 문제를 해결하기 위해 딥 러닝 기반의 3차원 메쉬 디노이징 기법을 소개한다. 구체적으로 본 논문에서는 인코더와 디코더로 구성된 컨볼루션 기반 오토인코더 모델을 제안한다. 메쉬 데이터에 적용하는 컨볼루션 연산은 메쉬 데이터를 구성하고 있는 각각의 정점과 그 주변의 정점들 간의 관계를 고려하여 디노이징을 수행하며, 컨볼루션이 완료되면 학습 속도 향상을 위해 샘플링 연산을 수행한다. 실험 결과, 본 논문에서 제안한 오토인코더 모델이 기존 방식보다 더 빠르고 더 높은 품질의 디노이징된 데이터를 생성함을 확인하였다.

• 한국인터넷방송통신학회논문지
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• 제20권2호
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• pp.39-44
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• 2020

악성코드는 탐지 프로그램을 피해 기기들에게 피해를 유발한다. 기존의 악성코드 탐지 기법으로 이러한 새로운 악성코드를 탐지하는데 어려움을 겪는 이유는 서명 기반의 탐지 기법을 사용하기 때문이다. 이 기법은 기존 악성코드들은 효과적으로 탐지하지만, 새로운 악성코드에 대해서는 탐지가 어렵다. 이러한 문제점을 인식하여, 휴리스틱 기법을 추가적으로 사용한다. 이 논문에서는 딥러닝을 활용하여 악성코드를 탐지하는 기술에 대해 소개하여 새로운 악성코드를 탐지하는 기술에 대해서 제안한다. 또한, 악성코드를 탐지한다는 것은, 기기에서 실행 가능한 파일의 개수는 무수히 많으므로, 지도학습 방식(Supervisor Learning)으로는 분명한 한계가 존재한다. 그렇기 때문에, 준지도 학습으로 알려진 SCAE(Stacked Convolution AutoEncoder)를 활용한다, 파일들의 바이트 정보들을 추출하여, 이미지화를 진행하고, 이 이미지들을 학습을 시켜, 학습 시키지 않은 10,869개의 악성코드, 3,442개의 비악성코드를 모델에 추론한 결과 정확도를 98.84%을 달성하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권3호
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• pp.1086-1103
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• 2020

The automatic identification and classification of image-based weld defects is a difficult task due to the complex texture of the X-ray images of the weld defect. Several depth learning methods for automatically identifying welds were proposed and tested. In this work, four different depth convolutional neural networks were evaluated and compared on the 1631 image set. The concavity, undercut, bar defects, circular defects, unfused defects and incomplete penetration in the weld image 6 different types of defects are classified. Another contribution of this paper is to train a CNN model "RayNet" for the dataset from scratch. In the experiment part, the parameters of convolution operation are compared and analyzed, in which the experimental part performs a comparative analysis of various parameters in the convolution operation, compares the size of the input image, gives the classification results for each defect, and finally shows the partial feature map during feature extraction with the classification accuracy reaching 96.5%, which is 6.6% higher than the classification accuracy of other existing fine-tuned models, and even improves the classification accuracy compared with the traditional image processing methods, and also proves that the model trained from scratch also has a good performance on small-scale data sets. Our proposed method can assist the evaluators in classifying pipeline welding defects.

• 한국멀티미디어학회논문지
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• 제16권5호
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• pp.637-646
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• 2013

본 연구는 다수의 나무로 구성된 실외 지형에 적합하고 다양하고 자연스러운 나무를 모델링하기 위하여 새로운 성장 규칙(약수 함수의 합성 곱 기반)의 모델링 방법을 제안한다. 기본적으로 나무를 구성하는 가지와 잎의 효율적 관리와 자연스러운 가지 증식을 위하여 기존의 성장 볼륨기반 알고리즘을 활용한다. 이 논문의 주요 특징은 각 성장 단계에서 가지와 잎의 성장과 운명을 자연스럽게 표현하는 약수 함수 합성 곱 이론을 도입하는 것이다. 이를 기반으로 일반화된 생성 함수를 갖는 여러 약수 함수와 성장 규칙의 변형을 통해 사용자의 제어를 최소화하여 다양한 나무를 모델링하는 방법을 제안한다. 이 모델링 방법은 가지와 잎을 동시에 고려하는 특징이 있으며, 다수의 나무들로 구성된 실외 지형 구축에 보다 효과적이라는 이점이 있다. 제안한 방법을 통해 자연스럽고 다양한 나무 모델 생성과 이를 활용하여 넓은 자연 지형 구축 가능성과 다수의 나무를 구성하는 과정에서의 효율성을 실험을 통해 증명한다.