TIIS (한국인터넷정보학회)
Current Result Document : 3 / 5
| 한글제목(Korean Title) |
DCNN Optimization Using Multi-Resolution Image Fusion |
| 영문제목(English Title) |
DCNN Optimization Using Multi-Resolution Image Fusion |
| 저자(Author) |
Junwei Li
Zhisong Pan
Si Shen
Guojiang Shen
Yang Shen
Duanyang Liu
Xi Yang
Xiangjie Kong
Abdullah A. Alshehri
Adam Lutz
Soundararajan Ezekiel
Larry Pearlstein
John Conlen
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| 원문수록처(Citation) |
VOL 14 NO. 11 PP. 4290 ~ 4309 (2020. 11) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In recent years, advancements in machine learning capabilities have allowed it to see widespread adoption for tasks such as object detection, image classification, and anomaly detection. However, despite their promise, a limitation lies in the fact that a network’s performance quality is based on the data which it receives. A well-trained network will still have poor performance if the subsequent data supplied to it contains artifacts, out of focus regions, or other visual distortions. Under normal circumstances, images of the same scene captured from differing points of focus, angles, or modalities must be separately analysed by the network, despite possibly containing overlapping information such as in the case of images of the same scene captured from different angles, or irrelevant information such as images captured from infrared sensors which can capture thermal information well but not topographical details. This factor can potentially add significantly to the computational time and resources required to utilize the network without providing any additional benefit. In this study, we plan to explore using image fusion techniques to assemble multiple images of the same scene into a single image that retains the most salient key features of the individual source images while discarding overlapping or irrelevant data that does not provide any benefit to the network. Utilizing this image fusion step before inputting a dataset into the network, the number of images would be significantly reduced with the potential to improve the classification performance accuracy by enhancing images while discarding irrelevant and overlapping regions.
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| 키워드(Keyword) |
Traffic classification
deep learning
convolution neural network
stack auto encoder
long short-term memory network
Traffic signal timing
reinforcement learning
actor-critic
proximal policy optimization
generalized advantage estimation
Image Fusion
Deep Convolutional Neural Networks
Wavelets
Image Classification
Heterogeneous DCNN Fusion
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| 파일첨부 |
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