• Title, Summary, Keyword: CNN

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Object Detection based on Mask R-CNN from Infrared Camera (적외선 카메라 영상에서의 마스크 R-CNN기반 발열객체검출)

  • Song, Hyun Chul;Knag, Min-Sik;Kimg, Tae-Eun
    • Journal of Digital Contents Society
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    • v.19 no.6
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    • pp.1213-1218
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    • 2018
  • Recently introduced Mask R - CNN presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation mask of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask R - CNN is an algorithm that extends Faster R - CNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. The mask R - CNN is added to the high - speed R - CNN which training is easy and fast to execute. Also, it is easy to generalize the mask R - CNN to other tasks. In this research, we propose an infrared image detection algorithm based on R - CNN and detect heating elements which can not be distinguished by RGB images. As a result of the experiment, a heat-generating object which can not be discriminated from Mask R-CNN was detected normally.

A Study of Facial Organs Classification System Based on Fusion of CNN Features and Haar-CNN Features

  • Hao, Biao;Lim, Hye-Youn;Kang, Dae-Seong
    • The Journal of Korean Institute of Information Technology
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    • v.16 no.11
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    • pp.105-113
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    • 2018
  • In this paper, we proposed a method for effective classification of eye, nose, and mouth of human face. Most recent image classification uses Convolutional Neural Network(CNN). However, the features extracted by CNN are not sufficient and the classification effect is not too high. We proposed a new algorithm to improve the classification effect. The proposed method can be roughly divided into three parts. First, the Haar feature extraction algorithm is used to construct the eye, nose, and mouth dataset of face. The second, the model extracts CNN features of image using AlexNet. Finally, Haar-CNN features are extracted by performing convolution after Haar feature extraction. After that, CNN features and Haar-CNN features are fused and classify images using softmax. Recognition rate using mixed features could be increased about 4% than CNN feature. Experiments have demonstrated the performance of the proposed algorithm.

Research on the Main Memory Access Count According to the On-Chip Memory Size of an Artificial Neural Network (인공 신경망 가속기 온칩 메모리 크기에 따른 주메모리 접근 횟수 추정에 대한 연구)

  • Cho, Seok-Jae;Park, Sungkyung;Park, Chester Sungchung
    • Journal of IKEEE
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    • v.25 no.1
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    • pp.180-192
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    • 2021
  • One widely used algorithm for image recognition and pattern detection is the convolution neural network (CNN). To efficiently handle convolution operations, which account for the majority of computations in the CNN, we use hardware accelerators to improve the performance of CNN applications. In using these hardware accelerators, the CNN fetches data from the off-chip DRAM, as the massive computational volume of data makes it difficult to derive performance improvements only from memory inside the hardware accelerator. In other words, data communication between off-chip DRAM and memory inside the accelerator has a significant impact on the performance of CNN applications. In this paper, a simulator for the CNN is developed to analyze the main memory or DRAM access with respect to the size of the on-chip memory or global buffer inside the CNN accelerator. For AlexNet, one of the CNN architectures, when simulated with increasing the size of the global buffer, we found that the global buffer of size larger than 100kB has 0.8x as low a DRAM access count as the global buffer of size smaller than 100kB.

A Search Interval Limitation Technique for Improved Search Performance of CNN (연속 최근접 이웃(CNN) 탐색의 성능향상을 위한 탐색구간 제한기법)

  • Han, Seok;Oh, Duk-Shin;Kim, Jong-Wan
    • Journal of Internet Computing and Services
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    • v.9 no.3
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    • pp.1-8
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    • 2008
  • With growing interest in location-based service (LBS), there is increasing necessity for nearest neighbor (NN) search through query while the user is moving. NN search in such a dynamic environment has been performed through the repeated applicaton of the NN method to the search segment, but this increases search cost because of unnecessary redundant calculation. We propose slabbed continuous nearest neighbor (Slabbed_CNN) search, which is a new method that searches CNN in the search segment while moving, Slabbed_CNN reduces calculation costs and provides faster services than existing CNN by reducing the search area and calculation cost of the existing CNN method through reducing the search segment using slabs.

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Method that determining the Hyperparameter of CNN using HS algorithm (HS 알고리즘을 이용한 CNN의 Hyperparameter 결정 기법)

  • Lee, Woo-Young;Ko, Kwang-Eun;Geem, Zong-Woo;Sim, Kwee-Bo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.27 no.1
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    • pp.22-28
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    • 2017
  • The Convolutional Neural Network(CNN) can be divided into two stages: feature extraction and classification. The hyperparameters such as kernel size, number of channels, and stride in the feature extraction step affect the overall performance of CNN as well as determining the structure of CNN. In this paper, we propose a method to optimize the hyperparameter in CNN feature extraction stage using Parameter-Setting-Free Harmony Search (PSF-HS) algorithm. After setting the overall structure of CNN, hyperparameter was set as a variable and the hyperparameter was optimized by applying PSF-HS algorithm. The simulation was conducted using MATLAB, and CNN learned and tested using mnist data. We update the parameters for a total of 500 times, and it is confirmed that the structure with the highest accuracy among the CNN structures obtained by the proposed method classifies the mnist data with an accuracy of 99.28%.

The Impact of the PCA Dimensionality Reduction for CNN based Hyperspectral Image Classification (CNN 기반 초분광 영상 분류를 위한 PCA 차원축소의 영향 분석)

  • Kwak, Taehong;Song, Ahram;Kim, Yongil
    • Korean Journal of Remote Sensing
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    • v.35 no.6_1
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    • pp.959-971
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    • 2019
  • CNN (Convolutional Neural Network) is one representative deep learning algorithm, which can extract high-level spatial and spectral features, and has been applied for hyperspectral image classification. However, one significant drawback behind the application of CNNs in hyperspectral images is the high dimensionality of the data, which increases the training time and processing complexity. To address this problem, several CNN based hyperspectral image classification studies have exploited PCA (Principal Component Analysis) for dimensionality reduction. One limitation to this is that the spectral information of the original image can be lost through PCA. Although it is clear that the use of PCA affects the accuracy and the CNN training time, the impact of PCA for CNN based hyperspectral image classification has been understudied. The purpose of this study is to analyze the quantitative effect of PCA in CNN for hyperspectral image classification. The hyperspectral images were first transformed through PCA and applied into the CNN model by varying the size of the reduced dimensionality. In addition, 2D-CNN and 3D-CNN frameworks were applied to analyze the sensitivity of the PCA with respect to the convolution kernel in the model. Experimental results were evaluated based on classification accuracy, learning time, variance ratio, and training process. The size of the reduced dimensionality was the most efficient when the explained variance ratio recorded 99.7%~99.8%. Since the 3D kernel had higher classification accuracy in the original-CNN than the PCA-CNN in comparison to the 2D-CNN, the results revealed that the dimensionality reduction was relatively less effective in 3D kernel.

Design of a Multi-array CNN Model for Improving CTR Prediction (클릭률 예측 성능 향상을 위한 다중 배열 CNN 모형 설계)

  • Kim, Tae-Suk
    • The Journal of the Korea Contents Association
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    • v.20 no.3
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    • pp.267-274
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    • 2020
  • Click-through rate (CTR) prediction is an estimate of the probability that a user will click on a given item and plays an important role in determining strategies for maximizing online ad revenue. Recently, research has been performed to utilize CNN for CTR prediction. Since the CTR data does not have a meaningful order in terms of correlation, the CTR data may be arranged in any order. However, because CNN only learns local information limited by filter size, data arrays can have a significant impact on performance. In this paper, we propose a multi-array CNN model that generates a data array set that can extract all local feature information that CNN can collect, and learns features through individual CNN modules. Experimental results for large data sets show that the proposed model achieves a 22.6% synergy with RI in AUC compared to the existing CNN, and the proposed array generation method achieves 3.87% performance improvement over the random generation method.

A Design of a Cellular Neural Network for the Real Image Processing (실영상처리를 위한 셀룰러 신경망 설계)

  • Kim Seung-Soo;Jeon Heung-Woo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.10 no.2
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    • pp.283-290
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    • 2006
  • The cellular neural networks have the structure that consists of an array of the same cell which is a simple processing element, and each of the cells has local connectivity and space invariant template properties. So, it has a very suitable structure for the hardware implementation. But, it is impossible to have a one-to-one mapping between the CNN hardware processors and the pixels of the practical large image. In this paper, a $5{\times}5$ CNN hardware processor with pipeline input and output that can be applied to the time-multiplexing processing scheme, which processes the large image with a small CNN cell block, is designed. the operation of the implemented $5{\times}5$ CNN hardware processor is verified from the edge detection and the shadow detection experimentations.

Centroid Neural Network with Bhattacharyya Kernel (Bhattacharyya 커널을 적용한 Centroid Neural Network)

  • Lee, Song-Jae;Park, Dong-Chul
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.32 no.9C
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    • pp.861-866
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    • 2007
  • A clustering algorithm for Gaussian Probability Distribution Function (GPDF) data called Centroid Neural Network with a Bhattacharyya Kernel (BK-CNN) is proposed in this paper. The proposed BK-CNN is based on the unsupervised competitive Centroid Neural Network (CNN) and employs a kernel method for data projection. The kernel method adopted in the proposed BK-CNN is used to project data from the low dimensional input feature space into higher dimensional feature space so as the nonlinear problems associated with input space can be solved linearly in the feature space. In order to cluster the GPDF data, the Bhattacharyya kernel is used to measure the distance between two probability distributions for data projection. With the incorporation of the kernel method, the proposed BK-CNN is capable of dealing with nonlinear separation boundaries and can successfully allocate more code vector in the region that GPDF data are densely distributed. When applied to GPDF data in an image classification probleml, the experiment results show that the proposed BK-CNN algorithm gives 1.7%-4.3% improvements in average classification accuracy over other conventional algorithm such as k-means, Self-Organizing Map (SOM) and CNN algorithms with a Bhattacharyya distance, classed as Bk-Means, B-SOM, B-CNN algorithms.

Classification and Safety Score Evaluation of Street Images Using CNN (CNN을 이용한 거리 사진의 분류와 안전도 평가)

  • Bae, Kyu Ho;Yun, Jung Un;Park, In Kyu
    • Journal of Broadcast Engineering
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    • v.23 no.3
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    • pp.345-350
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    • 2018
  • CNN (convolution neural network) has become the most popular artificial intelligence technique and shows remarkable performance in image classification task. In this paper, we propose a CNN-based classification method for various street images as well as a method of evaluating the safety score for the street. The proposed method consists of learning four types of street images using CNN and classifying input street images using the learned CNN model followed by evaluating the safety score. During the learning process, four types of street images are collected and augmented, and then CNN learning is performed. It is shown that learned CNN model classifies input images correctly and the safety scores are evaluated quantitatively by combining the probabilities of different street types.