• Title, Summary, Keyword: Convolutional Autoencoder

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Classification of Alzheimer's Disease with Stacked Convolutional Autoencoder

  • Baydargil, Husnu Baris;Park, Jang Sik;Kang, Do Young
    • Journal of Korea Multimedia Society
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    • v.23 no.2
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    • pp.216-226
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    • 2020
  • In this paper, a stacked convolutional autoencoder model is proposed in order to classify Alzheimer's disease with high accuracy in PET/CT images. The proposed model makes use of the latent space representation - which is also called the bottleneck, of the encoder-decoder architecture: The input image is sent through the pipeline and the encoder part, using stacked convolutional filters, extracts the most useful information. This information is in the bottleneck, which then uses Softmax classification operation to classify between Alzheimer's disease, Mild Cognitive Impairment, and Normal Control. Using the data from Dong-A University, the model performs classification in detecting Alzheimer's disease up to 98.54% accuracy.

Agglomerative Hierarchical Clustering Analysis with Deep Convolutional Autoencoders (합성곱 오토인코더 기반의 응집형 계층적 군집 분석)

  • Park, Nojin;Ko, Hanseok
    • Journal of Korea Multimedia Society
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    • v.23 no.1
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    • pp.1-7
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    • 2020
  • Clustering methods essentially take a two-step approach; extracting feature vectors for dimensionality reduction and then employing clustering algorithm on the extracted feature vectors. However, for clustering images, the traditional clustering methods such as stacked auto-encoder based k-means are not effective since they tend to ignore the local information. In this paper, we propose a method first to effectively reduce data dimensionality using convolutional auto-encoder to capture and reflect the local information and then to accurately cluster similar data samples by using a hierarchical clustering approach. The experimental results confirm that the clustering results are improved by using the proposed model in terms of clustering accuracy and normalized mutual information.

A Novel Road Segmentation Technique from Orthophotos Using Deep Convolutional Autoencoders

  • Sameen, Maher Ibrahim;Pradhan, Biswajeet
    • Korean Journal of Remote Sensing
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    • v.33 no.4
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    • pp.423-436
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    • 2017
  • This paper presents a deep learning-based road segmentation framework from very high-resolution orthophotos. The proposed method uses Deep Convolutional Autoencoders for end-to-end mapping of orthophotos to road segmentations. In addition, a set of post-processing steps were applied to make the model outputs GIS-ready data that could be useful for various applications. The optimization of the model's parameters is explained which was conducted via grid search method. The model was trained and implemented in Keras, a high-level deep learning framework run on top of Tensorflow. The results show that the proposed model with the best-obtained hyperparameters could segment road objects from orthophotos at an average accuracy of 88.5%. The results of optimization revealed that the best optimization algorithm and activation function for the studied task are Stochastic Gradient Descent (SGD) and Exponential Linear Unit (ELU), respectively. In addition, the best numbers of convolutional filters were found to be 8 for the first and second layers and 128 for the third and fourth layers of the proposed network architecture. Moreover, the analysis on the time complexity of the model showed that the model could be trained in 4 hours and 50 minutes on 1024 high-resolution images of size $106{\times}106pixels$, and segment road objects from similar size and resolution images in around 14 minutes. The results show that the deep learning models such as Convolutional Autoencoders could be a best alternative to traditional machine learning models for road segmentation from aerial photographs.

Generation of Fresnelet region using CAE (CAE를 이용한 Fresnelet 영역의 생성)

  • Lee, Jae-Eun;Kim, Dong-Wook;Seo, Young-Ho
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • pp.205-206
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    • 2018
  • 본 논문에서는 디지털 홀로그램 영상을 Fresnelet 변환을 하여 상관도를 확인할 수 있는 데이터로 바꾸고, 컨볼루션 오토인코더(Convolutional Autoencoder, CAE)를 이용해 압축하고 생성하는 방법을 제안한다. 컨볼루션 계층과 채널 수가 다른 2개의 네트워크로 실험한다. CAE의 인코더를 수행해 영상을 압축하고 디코더를 통해 복원한다. 원본 영상의 Fresnelet 영역과 2개의 네트워크를 진행하여 생성된 Fresnelet 영역을 다시 역 Fresnelet하여 압축률에 따른 PSNR을 비교, 분석한다.

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Pyramidal Deep Neural Networks for the Accurate Segmentation and Counting of Cells in Microscopy Data

  • Vununu, Caleb;Kang, Kyung-Won;Lee, Suk-Hwan;Kwon, Ki-Ryong
    • Journal of Korea Multimedia Society
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    • v.22 no.3
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    • pp.335-348
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    • 2019
  • Cell segmentation and counting represent one of the most important tasks required in order to provide an exhaustive understanding of biological images. Conventional features suffer the lack of spatial consistency by causing the joining of the cells and, thus, complicating the cell counting task. We propose, in this work, a cascade of networks that take as inputs different versions of the original image. After constructing a Gaussian pyramid representation of the microscopy data, the inputs of different size and spatial resolution are given to a cascade of deep convolutional autoencoders whose task is to reconstruct the segmentation mask. The coarse masks obtained from the different networks are summed up in order to provide the final mask. The principal and main contribution of this work is to propose a novel method for the cell counting. Unlike the majority of the methods that use the obtained segmentation mask as the prior information for counting, we propose to utilize the hidden latent representations, often called the high-level features, as the inputs of a neural network based regressor. While the segmentation part of our method performs as good as the conventional deep learning methods, the proposed cell counting approach outperforms the state-of-the-art methods.

Bias-correction of Dual Polarization Radar rainfall using Convolutional Autoencoder

  • Jung, Sungho;Le, Xuan Hien;Oh, Sungryul;Kim, Jeongyup;Lee, GiHa
    • Proceedings of the Korea Water Resources Association Conference
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    • pp.166-166
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    • 2020
  • Recently, As the frequency of localized heavy rains increases, the use of high-resolution radar data is increasing. The produced radar rainfall has still gaps of spatial and temporal compared to gauge observation rainfall, and in many studies, various statistical techniques are performed for correct rainfall. In this study, the precipitation correction of the S-band Dual Polarization radar in use in the flood forecast was performed using the ConvAE algorithm, one of the Convolutional Neural Network. The ConvAE model was trained based on radar data sets having a 10-min temporal resolution: radar rainfall data, gauge rainfall data for 790minutes(July 2017 in Cheongju flood event). As a result of the validation of corrected radar rainfall were reduced gaps compared to gauge rainfall and the spatial correction was also performed. Therefore, it is judged that the corrected radar rainfall using ConvAE will increase the reliability of the gridded rainfall data used in various physically-based distributed hydrodynamic models.

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2D Game Image Color Synthesis System Using Convolutional Neural Network (컨볼루션 인공신경망을 이용한 2차원 게임 이미지 색상 합성 시스템)

  • Hong, Seung Jin;Kang, Shin Jin;Cho, Sung Hyun
    • Journal of Korea Game Society
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    • v.18 no.2
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    • pp.89-98
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    • 2018
  • The recent Neural Network technique has shown good performance in content generation such as image generation in addition to the conventional classification problem and clustering problem solving. In this study, we propose an image generation method using artificial neural network as a next generation content creation technique. The proposed artificial neural network model receives two images and combines them into a new image by taking color from one image and shape from the other image. This model is made up of Convolutional Neural Network, which has two encoders for extracting color and shape from images, and a decoder for taking all the values of each encoder and generating a combination image. The result of this work can be applied to various 2D image generation and modification works in game development process at low cost.

Combining multi-task autoencoder with Wasserstein generative adversarial networks for improving speech recognition performance (음성인식 성능 개선을 위한 다중작업 오토인코더와 와설스타인식 생성적 적대 신경망의 결합)

  • Kao, Chao Yuan;Ko, Hanseok
    • The Journal of the Acoustical Society of Korea
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    • v.38 no.6
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    • pp.670-677
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    • 2019
  • As the presence of background noise in acoustic signal degrades the performance of speech or acoustic event recognition, it is still challenging to extract noise-robust acoustic features from noisy signal. In this paper, we propose a combined structure of Wasserstein Generative Adversarial Network (WGAN) and MultiTask AutoEncoder (MTAE) as deep learning architecture that integrates the strength of MTAE and WGAN respectively such that it estimates not only noise but also speech features from noisy acoustic source. The proposed MTAE-WGAN structure is used to estimate speech signal and the residual noise by employing a gradient penalty and a weight initialization method for Leaky Rectified Linear Unit (LReLU) and Parametric ReLU (PReLU). The proposed MTAE-WGAN structure with the adopted gradient penalty loss function enhances the speech features and subsequently achieve substantial Phoneme Error Rate (PER) improvements over the stand-alone Deep Denoising Autoencoder (DDAE), MTAE, Redundant Convolutional Encoder-Decoder (R-CED) and Recurrent MTAE (RMTAE) models for robust speech recognition.

A Deep Learning-based Streetscapes Safety Score Prediction Model using Environmental Context from Big Data (빅데이터로부터 추출된 주변 환경 컨텍스트를 반영한 딥러닝 기반 거리 안전도 점수 예측 모델)

  • Lee, Gi-In;Kang, Hang-Bong
    • Journal of Korea Multimedia Society
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    • v.20 no.8
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    • pp.1282-1290
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    • 2017
  • Since the mitigation of fear of crime significantly enhances the consumptions in a city, studies focusing on urban safety analysis have received much attention as means of revitalizing the local economy. In addition, with the development of computer vision and machine learning technologies, efficient and automated analysis methods have been developed. Previous studies have used global features to predict the safety of cities, yet this method has limited ability in accurately predicting abstract information such as safety assessments. Therefore we used a Convolutional Context Neural Network (CCNN) that considered "context" as a decision criterion to accurately predict safety of cities. CCNN model is constructed by combining a stacked auto encoder with a fully connected network to find the context and use it in the CNN model to predict the score. We analyzed the RMSE and correlation of SVR, Alexnet, and Sharing models to compare with the performance of CCNN model. Our results indicate that our model has much better RMSE and Pearson/Spearman correlation coefficient.