Abstract
AI is being used in various fields, especially in image analysis. There are many ways to analyze images using AI. There are many cases of image analysis that recognize real objects, but it is difficult to find application cases for image analysis cases that are not common. Unusual objects, that is, drawing images, such as hand-drawn drawings, are rarely learned and have limitations in image detection. In order to overcome these limitations, this study aims to learn images by applying deep learning so that they can be applied to picture projection tests among unusual fields, and to detect characteristic images that can be used for psycho-logical interpretation. In order to detect atypical images that may have psycho-logical characteristics among pictorial images, we learned through image trans-formation and increase. As a result of testing whether it is possible to detect non-realistic images through deep learning, first, it is possible to detect images even in hand drawings that are not real photos, and second, it is possible to detect unpatented partial images in pictures that must be found important for psychological analysis. It was confirmed that. Predicting the state of mind due to the detection of characteristic partial images Based on this paper, it is expected that faster and higher-level psychological analysis will be possible by incorporating AI technology in picture psychological analysis.
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Lee, J.W., Lee, J.H., Kim, D.Y., Gim, G.Y. (2021). Study on Partial Image Detection for Drawing—Focus on Unstructured Images Included in the Main Image. In: Lee, R. (eds) Computer and Information Science 2021—Summer . ICIS 2021. Studies in Computational Intelligence, vol 985. Springer, Cham. https://doi.org/10.1007/978-3-030-79474-3_7
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