Quantum robust fitting
Document Type
Conference Proceeding
Publication Title
Computer Vision - ACCV 2020
Volume
12622 LNCS
First Page
485
Last Page
499
Publisher
Springer
School
School of Science
RAS ID
35653
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.
DOI
10.1007/978-3-030-69525-5_29
Access Rights
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Comments
Chin, T. J., Suter, D., Ch'ng, S. F., & Quach, J. (2021). Quantum robust fitting. In Computer Vision - ACCV 2020 (pp. 485-499). Springer, Cham. https://doi.org/10.1007/978-3-030-69525-5_29