Sindel A, Maier A, Christlein V (2020)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2020
Publisher: IEEE Computer Society
Pages Range: 788-792
Event location: Online (Abu Dhabi, United Arab Emirates)
ISBN: 978-1-7281-6395-6
DOI: 10.1109/ICIP40778.2020.9191117
Artists or art workshops often reuse their motifs directly or in a slightly amended form. To allow a better comparison of these artworks, salient contours are extracted that reduce them to the most important lines or boundaries. For this task, we propose a generative adversarial network (GAN) based approach to learn the mapping from artwork images to contour drawings in a supervised manner. We introduce the combination of multiple regression task losses to encourage the learning of salient contours. For the evaluation, we created a dataset of high-resolution prints and paintings and corresponding annotated ground truth drawings. We show that our method visually and quantitatively outperforms competing methods in contour detection on prints and paintings.
APA:
Sindel, A., Maier, A., & Christlein, V. (2020). Art2Contour: Salient Contour Detection in Artworks Using Generative Adversarial Networks. In Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP 2020) (pp. 788-792). Online (Abu Dhabi, United Arab Emirates): IEEE Computer Society.
MLA:
Sindel, Aline, Andreas Maier, and Vincent Christlein. "Art2Contour: Salient Contour Detection in Artworks Using Generative Adversarial Networks." Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP 2020), Online (Abu Dhabi, United Arab Emirates) IEEE Computer Society, 2020. 788-792.
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