Quantum Robust Fitting
This work addresses robust fitting for computer vision applications, offering a novel quantum approach that could improve efficiency in handling imperfect real-world measurements.
The paper tackles the problem of robust fitting in computer vision, which involves fitting geometric models to noisy and outlier-contaminated data, by proposing a quantum algorithm based on a Fourier-inspired formulation, achieving computational speed-up over classical methods.
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.