Okkeun Lee
Introduction
Contrast sensitivity function (CSF) is a subjective measurement of an ability of the visual system to detect a low contrast pattern stimuli. The stimuli considered usually are vertical sinusoidal Gabor patches of decreasing shades of black to grey. This use of sine wave gratings was first introduced in vision by Schade (1956) and was subsequently used by early investigators to measure basic visual sensitivity (Westheimer, 1960; DePalma and Lowry, 1962; Campbell and Robson, 1968). The resulting measurement is used to validate the representation of the eyes' visual performance as it complements the visual acuity. CSF is synonyms with an audiogram where a person’s highest detectable pitch is measured and well as the ability to hear all lower pitches.
The CSF measurements are usually acquired with small patches of sinusoidal grating designed to fall within few central degrees of the visual field. It is well known that the CSF decreases as one measures contrast sensitivity at increasingly peripheral locations in the visual field. The reasons for such decreased CSF is attributed to a number of neural factors. The human eye is structured such that the distribution of the cone mosaic falls off rapidly as a function of visual eccentricity, so that there are fewer sensors available to detect and encode the incoming stimuli. Towards the periphery the amount of retinal ganglion cells’ density falls as well. This structure of the cones is also a key factor in deciding the CSF. In particular, for this project, we make an effort to explore the role of inference engines in shaping the CSF.
Background
The human visual system’s CSF or the modulation transfer function (MTF) fundamentally characterises eye’s spatial frequency response and thus, one may think of it as a bandpass filter. This bandpass nature, however, is determined by a variety of factors such as the front-end optics, cone distribution geometry and the neural mechanism that is responsible for this interpretation which is typically inferred using the machine learning techniques such as the KNN and SVM.