Visual detection of spatial contrast patterns: evaluation of five simple models.

Andrew B. Watson

doi:10.1364/oe.6.000012

Abstract

The ModelFest Phase One dataset is a collection of luminance contrast thresholds for 43 two-dimensional monochromatic spatial patterns confined to an area of approximately two by two degrees. These data were collected by a collaboration among twelve laboratories, and were designed to provide a common database for calibration and testing of spatial vision models. Here I report fits of the ModelFest data with five models: Peak Contrast, Contrast Energy, Generalized Energy, a Gabor Channels model, and a Discrete Cosine Transform model. The Gabor Channels model provides the best fit, though the other, simpler models, with the exception of Peak Contrast, provide remarkably good fits as well. Though there are clear individual differences, regularities in the data suggest the possibility of constructing a standard observer for spatial vision.

Highlights

Society of America whose purpose was to showcase and evaluate computational models of early human vision
ModelFest participants have collected a set of data designed to both calibrate and test vision models[1,2]
It was envisioned that the data set would be large and varied enough to adequately serve both purposes, and that the complete data set would be collected by a number of different labs, to enhance both generality and accuracy

Summary

Introduction

Society of America whose purpose was to showcase and evaluate computational models of early human vision. ModelFest participants have collected a set of data designed to both calibrate and test vision models[1,2]. It was envisioned that the data set would be large and varied enough to adequately serve both purposes, and that the complete data set would be collected by a number of different labs, to enhance both generality and accuracy. The selected stimuli consist of 43 patterns, including. In this report we describe fits of some simple models to the data of eight ModelFest observers. These fits provide a benchmark against which subsequent model fits may be compared

Objectives

Results

Conclusion