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Title:
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A HUMAN-COMPUTER INTERACTION METHOD BASED ON U-NET CONVOLUTIONAL NEURAL NETWORK FOR TARGET MOLECULE OBSERVATION |
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Author(s):
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Wenbin Yin, Xinfeng Zhang, Jinpeng Fang, Xudong Zhou and Bin Li |
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ISBN:
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978-989-8704-40-5 |
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Editors:
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Piet Kommers and Mário Macedo |
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Year:
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2022 |
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Edition:
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Single |
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Keywords:
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Deep Learning, U-Net, Target Detection, Human-Computer Interaction |
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Type:
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Full Paper |
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First Page:
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228 |
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Last Page:
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235 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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In order to accurately identify various physiological activities and movements in living bodies, we propose a U-Net-based method to identify all similar micro molecules such as cells and proteins in organisms. We first transform the molecular image to be observed into the feature space using the U-Net convolution network, and then use the target feature to match across the whole image to detect all similar targets of interest in the image. Extensive experimental results show that the proposed method can rapidly detect similar molecules of interest through a simple human-computer interaction and attain a more accurate detection performance than other approaches. |
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