%0 Conference Paper %1 10.1007/978-3-030-69525-5_29 %A Chin, Tat-Jun %A Suter, David %A Ch'ng, Shin-Fang %A Quach, James %B Computer Vision -- ACCV 2020 %C Cham %D 2021 %E Ishikawa, Hiroshi %E Liu, Cheng-Lin %E Pajdla, Tomas %E Shi, Jianbo %I Springer International Publishing %K leibnizailab myown %P 485--499 %R 10.1007/978-3-030-69525-5_29 %T Quantum Robust Fitting %X Many computer vision applications need to recover structure from imperfect measurements of the real world. The task is often solved by robustly fitting a geometric model onto noisy and outlier-contaminated data. However, recent theoretical analyses indicate that many commonly used formulations of robust fitting in computer vision are not amenable to tractable solution and approximation. In this paper, we explore the usage of quantum computers for robust fitting. To do so, we examine and establish the practical usefulness of a robust fitting formulation inspired by the analysis of monotone Boolean functions. We then investigate a quantum algorithm to solve the formulation and analyse the computational speed-up possible over the classical algorithm. Our work thus proposes one of the first quantum treatments of robust fitting for computer vision. %@ 978-3-030-69525-5

@inproceedings{10.1007/978-3-030-69525-5_29, abstract = {Many computer vision applications need to recover structure from imperfect measurements of the real world. The task is often solved by robustly fitting a geometric model onto noisy and outlier-contaminated data. However, recent theoretical analyses indicate that many commonly used formulations of robust fitting in computer vision are not amenable to tractable solution and approximation. In this paper, we explore the usage of quantum computers for robust fitting. To do so, we examine and establish the practical usefulness of a robust fitting formulation inspired by the analysis of monotone Boolean functions. We then investigate a quantum algorithm to solve the formulation and analyse the computational speed-up possible over the classical algorithm. Our work thus proposes one of the first quantum treatments of robust fitting for computer vision.}, added-at = {2022-03-14T06:20:47.000+0100}, address = {Cham}, author = {Chin, Tat-Jun and Suter, David and Ch'ng, Shin-Fang and Quach, James}, biburl = {https://www.bibsonomy.org/bibtex/26c19817c9d6066af1a0330dc769ee5d1/dsuter}, booktitle = {Computer Vision -- ACCV 2020}, doi = {10.1007/978-3-030-69525-5_29}, editor = {Ishikawa, Hiroshi and Liu, Cheng-Lin and Pajdla, Tomas and Shi, Jianbo}, interhash = {bb1d4398c0f758b7700106f6fddf8dc0}, intrahash = {6c19817c9d6066af1a0330dc769ee5d1}, isbn = {978-3-030-69525-5}, keywords = {leibnizailab myown}, pages = {485--499}, publisher = {Springer International Publishing}, timestamp = {2022-03-16T09:34:59.000+0100}, title = {Quantum Robust Fitting}, year = 2021 }