Abstract
Motion segmentation is a challenging problem that seeks to identify independent motions in two or several input images. This paper introduces the first algorithm for motion segmentation that relies on adiabatic quantum optimization of the objective function. The proposed method achieves on-par performance with the state of the art on problem instances which can be mapped to modern quantum annealers.
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Arrigoni, F., Menapace, W., Benkner, M. S., Ricci, E., & Golyanik, V. (2022). Quantum Motion Segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13689 LNCS, pp. 506–523). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-19818-2_29
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