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http://dx.doi.org/10.6109/jicce.2018.16.3.160

Convolutional Neural Network Based Image Processing System  

Kim, Hankil (Department of Music & Sound Technology, Korea University of Media Arts)
Kim, Jinyoung (Department of Computer Engineering, Pai Chai University)
Jung, Hoekyung (Department of Computer Engineering, Pai Chai University)
Publication Information
Journal of information and communication convergence engineering / v.16, no.3, 2018 , pp. 160-165 More about this Journal
Abstract
This paper designed and developed the image processing system of integrating feature extraction and matching by using convolutional neural network (CNN), rather than relying on the simple method of processing feature extraction and matching separately in the image processing of conventional image recognition system. To implement it, the proposed system enables CNN to operate and analyze the performance of conventional image processing system. This system extracts the features of an image using CNN and then learns them by the neural network. The proposed system showed 84% accuracy of recognition. The proposed system is a model of recognizing learned images by deep learning. Therefore, it can run in batch and work easily under any platform (including embedded platform) that can read all kinds of files anytime. Also, it does not require the implementing of feature extraction algorithm and matching algorithm therefore it can save time and it is efficient. As a result, it can be widely used as an image recognition program.
Keywords
Artificial intelligence; Convolutional neural network (CNN); Deep learning; Pattern recognition;
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