Estimation of structure of four-scene comics by convolutional neural networks
Estimation of structure of four-scene comics by convolutional neural networks
复制标题
利用卷积神经网络估计四场景漫画的结构
DOI：
10.1145/3011549.3011558
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
H. Isahara
中科院分区：
文献类型：
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作者：
Miki Ueno;N. Mori;Toshinori Suenaga;H. Isahara
漫画的计算解读是人工智能和图像识别领域正在研究的重要课题之一。为了解读漫画，需要承担许多具有挑战性的任务，例如识别灰度绘画图像中的物体、提取场景的情感信息以及通过考虑漫画的结构来定义连续场景的模型。在本文中，我们聚焦于四格漫画及其转换。四格漫画的结构源于中国诗词的四联，所以创作者会清晰地描绘出每个场景之间的语义距离。这对于表达漫画的趣味性和抒情性非常重要。为了检测场景的转换，构建了卷积神经网络（CNN）并进行了计算机实验。结果表明，CNN能够检测场景的转换，并且每个场景的特征差异很大。
The computational interpretation of comics is one of the important topics being studied in the field of artificial intelligence and image recognition. There are a lot of challenging tasks to undertake in order to interpret comics, i.e., recognize objects in gray-scaled drawing image, extract emotional information of scenes, and define models of continuous scenes by considering the structure of comics. In this paper, we focused on four scene comics and their transition. Four-scene comics have a structure which originated in four-part of Chinese-poetry so creators clearly draw the semantic distance between each scene. It is very important for expressing the interesting and lyrical aspects of comics. To detect the transition of scenes, convolutional neural networks(CNNs) are constructed and computer experiments were carried out. The results suggest that CNN is able to detect the transition of scenes and that the features of each scene are quite different.
使用深度学习评估咬肌来检查口腔癌患者的新预后预测方法
DOI：
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发表时间：
2021
期刊：
影响因子：
--
作者：
阪本勝也;平岡慎一郎;川村晃平;内田修爾;田中晋
通讯作者：
田中晋