Quantum Robust Fitting

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 Fourier analysis of 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.

arXiv commentAppears in: Asian Conference on Computer Vision 2020 (ACCV 2020)

References in corpus (5)

• Image recognition with an adiabatic quantum computer I. Mapping to quadratic unconstrained binary optimization
• Quasiconvex Programming
• Image classification using quantum inference on the D-Wave 2X
• Quantum algorithms for testing Boolean functions
• Monotone Boolean Functions, Feasibility/Infeasibility, LP-type problems and MaxCon

Cited by in corpus (1)

• Adiabatic Quantum Graph Matching with Permutation Matrix Constraints