Abstract
For computers to understand human perception, metrics that can capture human perception well are important. However, there are few metrics that characterize the visual perception of humans towards images. Therefore, in this paper, we propose a novel concept and a metric of pointedness of an image, which describes how pointy an image is perceived. The algorithm is inspired by the Features from Accelerated Segment Test (FAST) algorithm for corner detection which looks on the number of continuous neighboring darker pixels surrounding each pixel. We assume that this number would be proportional to the perceived pointedness in the region around the pixel. We evaluated our method towards how well it could capture the human perception of images. To compare the method with similar metrics that describe shapes, we prepared silhouette images of both artificial shapes and natural objects. The results showed that the proposed method gave nearly equivalent perceptual performance to other metrics and also worked in a larger variety of images.
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Acknowledgements
This work was partly supported by MEXT grant-in-aid for Scientific Research (16H02846), Microsoft Research CORE-16 program, and a joint research project with NII, Japan.
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Matsuhira, C. et al. (2021). Pointedness of an Image: Measuring How Pointy an Image is Perceived. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2021 - Posters. HCII 2021. Communications in Computer and Information Science, vol 1419. Springer, Cham. https://doi.org/10.1007/978-3-030-78635-9_20
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DOI: https://doi.org/10.1007/978-3-030-78635-9_20
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