• Title, Summary, Keyword: 컨볼루션 신경망

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CNN (Convolutional Neural Network) based in-loop filter in HEVC (컨볼루션 신경망을 이용한 고효율 비디오 부호화에서의 인-루프 필터)

  • Park, Woonsung;Kim, Munchurl
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • pp.369-372
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    • 2016
  • 본 논문에서는 고효율 비디오 부호화에서 채택하고 있는 인-루프 필터 중 SAO (sample adaptive offset)를 컨볼루션 신경망으로 대체하여 부호화 효율을 향상시키는 방법을 제안한다. SAO 는 양자화 에러를 줄이기 위해 인코더에서 디코더로 적절한 오프셋 값을 전송한다. 제안하는 컨볼루션 신경망을 사용한 인-루프 필터는 인코더와 디코더가 같은 컨볼루션 신경망을 사용하여, 추가적인 비트를 디코더로 전송할 필요 없이 양자화 에러를 줄일 수 있다. 컨볼루션 신경망의 구조는 두 가지를 각각 사용하였고, 각 컨볼루션 신경망의 구조에 대해서 입력 영상과 원래 영상의 평균제곱오차에 따라 다른 모델을 적용하였다. 따라서 제안하는 방법을 HEVC에 적용하여 기존의 방법보다 더 적은 bit 로 더 좋은 화질의 영상을 얻어서 BD-rate 의 gain 을 얻을 수 있을 뿐만 아니라, 주관적인 화질의 비교에서도 더 좋은 결과를 보인다.

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The application of convolutional neural networks for automatic detection of underwater object in side scan sonar images (사이드 스캔 소나 영상에서 수중물체 자동 탐지를 위한 컨볼루션 신경망 기법 적용)

  • Kim, Jungmoon;Choi, Jee Woong;Kwon, Hyuckjong;Oh, Raegeun;Son, Su-Uk
    • The Journal of the Acoustical Society of Korea
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    • v.37 no.2
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    • pp.118-128
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    • 2018
  • In this paper, we have studied how to search an underwater object by learning the image generated by the side scan sonar in the convolution neural network. In the method of human side analysis of the side scan image or the image, the convolution neural network algorithm can enhance the efficiency of the analysis. The image data of the side scan sonar used in the experiment is the public data of NSWC (Naval Surface Warfare Center) and consists of four kinds of synthetic underwater objects. The convolutional neural network algorithm is based on Faster R-CNN (Region based Convolutional Neural Networks) learning based on region of interest and the details of the neural network are self-organized to fit the data we have. The results of the study were compared with a precision-recall curve, and we investigated the applicability of underwater object detection in convolution neural networks by examining the effect of change of region of interest assigned to sonar image data on detection performance.

An Intelligent Fire Learning and Detection System Using Convolutional Neural Networks (컨볼루션 신경망을 이용한 지능형 화재 학습 및 탐지 시스템)

  • Cheoi, Kyungjoo;Jeon, Minseong
    • KIPS Transactions on Software and Data Engineering
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    • v.5 no.11
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    • pp.607-614
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    • 2016
  • In this paper, we propose an intelligent fire learning and detection system using convolutional neural networks (CNN). Through the convolutional layer of the CNN, various features of flame and smoke images are automatically extracted, and these extracted features are learned to classify them into flame or smoke or no fire. In order to detect fire in the image, candidate fire regions are first extracted from the image and extracted candidate regions are passed through CNN. Experimental results on various image shows that our system has better performances over previous work.

Image Filtering Method for an Effective Inverse Tone - mapping (효과적인 역 톤 매핑을 위한 영상 필터링 기법)

  • Kang, Rahoon;Park, Bumjun;Jeong, Jechang
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • pp.55-58
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    • 2018
  • 본 논문에서는 가이디드 영상 필터를 (guided image filter) 이용하여 컨볼루션 신경망 (convolutional neural network) 을 이용한 역 톤 매핑 (inver tone - mapping; iTMO) 기법의 결과를 향상 시킬 수 있는 알고리듬을 제안한다. 기존 low dynamic range (LDR ) 영상을 high dynamic range (HDR ) 디스플레이에서 표현할 수 있는 역 톤 매핑 기법이 과거부터 계속 제안되어 왔다. 최근에 컨볼루션 신경망을 이용하여 단일 LDR 영상만으로 넓은 동적 범위 (dynamic range) 를 가진 HDR 영상으로 변환하는 알고리듬이 많이 연구되었다. 기존의 알고리듬 중 포화 영역 (saturated region) 으로 인해 잃어버린 화소 정보를 학습된 컨볼루션 신경망을 이용해서 복원하는 알고리듬은 그 효과가 좋지만 포화 영역이 아닌 부분의 잡음을 제거하지 못하며 포화 영역의 디테일을 복원하지 못한다. 제안한 알고리듬은 입력 영상에 가중치 기반 가이디드 영상 필터를 사용해서 비포화 영역의 잡음을 제거하고 포화 영역의 디테일을 복원시킨 다음 컨볼루션 신경망에 인가하여 결과 영상의 품질을 개선하였다. 제안하는 알고리듬은 실험을 통해서 기존의 알고리듬에 비해 높은 정량적 화질 평가 지수를 나타내었고, 기존의 알고리듬에 비해 세부 사항을 효과적으로 복원할 수 있음을 확인할 수 있었다.

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Drone Image Classification based on Convolutional Neural Networks (컨볼루션 신경망을 기반으로 한 드론 영상 분류)

  • Joo, Young-Do
    • The Journal of The Institute of Internet, Broadcasting and Communication
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    • v.17 no.5
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    • pp.97-102
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    • 2017
  • Recently deep learning techniques such as convolutional neural networks (CNN) have been introduced to classify high-resolution remote sensing data. In this paper, we investigated the possibility of applying CNN to crop classification of farmland images captured by drones. The farming area was divided into seven classes: rice field, sweet potato, red pepper, corn, sesame leaf, fruit tree, and vinyl greenhouse. We performed image pre-processing and normalization to apply CNN, and the accuracy of image classification was more than 98%. With the output of this study, it is expected that the transition from the existing image classification methods to the deep learning based image classification methods will be facilitated in a fast manner, and the possibility of success can be confirmed.

Mushroom Image Recognition using Convolutional Neural Network and Transfer Learning (컨볼루션 신경망과 전이 학습을 이용한 버섯 영상 인식)

  • Kang, Euncheol;Han, Yeongtae;Oh, Il-Seok
    • KIISE Transactions on Computing Practices
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    • v.24 no.1
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    • pp.53-57
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    • 2018
  • A poisoning accident is often caused by a situation in which people eat poisonous mushrooms because they cannot distinguish between edible mushrooms and poisonous mushrooms. In this paper, we propose an automatic mushroom recognition system by using the convolutional neural network. We collected 1478 mushroom images of 38 species using image crawling, and used the dataset for learning the convolutional neural network. A comparison experiment using AlexNet, VGGNet, and GoogLeNet was performed using the collected datasets, and a comparison experiment using a class number expansion and a fine-tuning technique for transfer learning were performed. As a result of our experiment, we achieve 82.63% top-1 accuracy and 96.84% top-5 accuracy on test set of our dataset.

Learning and Transferring Deep Neural Network Models for Image Caption Generation (이미지 캡션 생성을 위한 심층 신경망 모델 학습과 전이)

  • Kim, Dong-Ha;Kim, Incheol
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.617-620
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    • 2016
  • 본 논문에서는 이미지 캡션 생성과 모델 전이에 효과적인 심층 신경망 모델을 제시한다. 본 모델은 멀티 모달 순환 신경망 모델의 하나로서, 이미지로부터 시각 정보를 추출하는 컨볼루션 신경망 층, 각 단어를 저차원의 특징으로 변환하는 임베딩 층, 캡션 문장 구조를 학습하는 순환 신경망 층, 시각 정보와 언어 정보를 결합하는 멀티 모달 층 등 총 5 개의 계층들로 구성된다. 특히 본 모델에서는 시퀀스 패턴 학습과 모델 전이에 우수한 LSTM 유닛을 이용하여 순환 신경망 층을 구성하고, 컨볼루션 신경망 층의 출력을 임베딩 층뿐만 아니라 멀티 모달 층에도 연결함으로써, 캡션 문장 생성을 위한 매 단계마다 이미지의 시각 정보를 이용할 수 있는 연결 구조를 가진다. Flickr8k, Flickr30k, MSCOCO 등의 공개 데이터 집합들을 이용한 다양한 비교 실험을 통해, 캡션의 정확도와 모델 전이의 효과 면에서 본 논문에서 제시한 멀티 모달 순환 신경망 모델의 우수성을 입증하였다.

EEG-based Motor Imagery Classification Using Convolutional Neural Network (컨볼루션 신경망을 이용한 동작 상상 뇌파 분류)

  • Lee, David;Park, Sang-Hoon;Lee, Hee-Jae;Lee, Sang-Goog
    • 한국정보기술학회논문지
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    • v.15 no.6
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    • pp.103-110
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    • 2017
  • Brain-computer interface (BCI) is a technology that can be used as augmentative and alternative communication (AAC) for people such as the elderly or the disabled who are restricted or impaired in physical function. In order for BCI to be used as AAC, it is important to select appropriate feature extraction and classification methods because the electroencephalogram (EEG) signal is non-linear and non-stationary. This study proposes a feature extraction and classification method of motor imagery EEG using convolutional neural network (CNN). The CNN, most commonly used in the field of images, uses a large number of training data to avoid the problem of overfitting. If the amount of training data is small, the CNN cause overfitting problems. Therefore, in this study, the CNN suitable with small amount of training data was designed for motor imagery based BCI, and then the motion imaginary EEG was learned and classified. The performance of the proposed method is shown to be about 3.8~4.5% in terms of average accuracy through comparison with existing machine learning methods.

A Transfer Learning Method for Solving Imbalance Data of Abusive Sentence Classification (욕설문장 분류의 불균형 데이터 해결을 위한 전이학습 방법)

  • Seo, Suin;Cho, Sung-Bae
    • Journal of KIISE
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    • v.44 no.12
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    • pp.1275-1281
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    • 2017
  • The supervised learning approach is suitable for classification of insulting sentences, but pre-decided training sentences are necessary. Since a Character-level Convolution Neural Network is robust for each character, so is appropriate for classifying abusive sentences, however, has a drawback that demanding a lot of training sentences. In this paper, we propose transfer learning method that reusing the trained filters in the real classification process after the filters get the characteristics of offensive words by generated abusive/normal pair of sentences. We got higher performances of the classifier by decreasing the effects of data shortage and class imbalance. We executed experiments and evaluations for three datasets and got higher F1-score of character-level CNN classifier when applying transfer learning in all datasets.

HyperConv: spatio-spectral classication of hyperspectral images with deep convolutional neural networks (심층 컨볼루션 신경망을 사용한 초분광 영상의 공간 분광학적 분류 기법)

  • Ko, Seyoon;Jun, Goo;Won, Joong-Ho
    • The Korean Journal of Applied Statistics
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    • v.29 no.5
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    • pp.859-872
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    • 2016
  • Land cover classification is an important tool for preventing natural disasters, collecting environmental information, and monitoring natural resources. Hyperspectral imaging is widely used for this task thanks to sufficient spectral information. However, the curse of dimensionality, spatiotemporal variability, and lack of labeled data make it difficult to classify the land cover correctly. We propose a novel classification framework for land cover classification of hyperspectral data based on convolutional neural networks. The proposed framework naturally incorporates full spectral features with the information from neighboring pixels and has advantages over existing methods that require additional feature extraction or pre-processing steps. Empirical evaluation results show that the proposed framework provides good generalization power with classification accuracies better than (or comparable to) the most advanced existing classifiers.