MeganckSajdakWu

Predicting Human Performance Using ISETBIO. Ryan Meganck, Adam Sajdak, Stephen Wu.

Introduction

Scope

Modern displays have benefited heavily from the technological advances surrounding the manufacture and design of transistors. Display designers have been able to package an increasing amount of transistors in a given display to yield stunning images while also improving the energy efficiency of the displays. In a vacuum, the goal of these display designers would be to strive for an infinite number of pixels in a display, but in all pipelines, there is a bottleneck for performance. In this case, the pipeline consists not only of display, but the observer watching the display. The observer is limited by non-idealities in the eye as well as the image processing portion of the brain.

At very low display resolutions, it is expected that a human observer would be able to notice an increase resolution or pixel count. In this case, the display resolution is the limiting factor in the pipeline. Conversely at high resolutions, there is a point where the resolution of the display is no longer the limiting factor and the human observer can no longer resolve higher resolutions.

The purpose of this project is to determine the critical point for display performance in the visual pipeline. In other words, to find the critical resolution at various viewing distances where the observer is no longer able to discern two different images.

Vernier Acuity

The metric used in this project for predicting an observer's ability to resolve an image is Vernier Acuity. This metric describes an observer's ability to discern the alignment of two line segments. The two images below are examples of the two scenes used throughout the project.

[File:Aligned.png]

Methods

The general procedure used for both methods in this experiment is:

1. Generate two scenes (one aligned, one misaligned).
2. Obtain cone absorption data for these scenes.
3. Train the system using the cone absorption data.
4. Obtain a new set of cone absorptions for the scenes.
5. Use the system to predict the classification of the test cone absorptions.
6. Calculate error.

Data

Training and test scenes were generated in MATLAB with the aid of a tutorial script provided by course staff. A human eye and display are generated

The feature vector for the experiment is the vector of cone absorptions. The length of this vector varies with FOV, image size, viewing distance, and screen resolution (TODO), since not all cones in the eye are affected by the scene.

Support Vector Machine

One approach uses a support vector machine to classify test images. Given a training dataset, this is achieved by calculating the separating hyperplane which yields the largest functional margin between the two classes. Test inputs are then classified based on which side of the hyperplane they fall on.

The particular flavor of SVM chosen for this investigation is C-support vector classification (C-SVC) and the kernel trick was employed using a radial basis function (RBF) kernel to allow for interpretation of higher-dimensional features aside from the individual cone absorptions. This method required the tuning of two parameters:

• C-SVC cost parameter. This controls the number of misclassified examples allowed when processing the training data to maximize the margin. This allows the system to ignore outliers if they exist.
• RBF $\gamma$ parameter. This controls the scale of the kernel function.

Parameter tuning graphs.

Conclusions

Further Investigation

• Application of principal component analysis to feature vectors
• Modeling myopia and hyperopia
• Comparing displays
• Contrast
• anything else?

References

Paper paper paper

Appendix I

Source code Result images