MeganckSajdakWu

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

Introduction

Vernier acuity is?

Methods

Data

Test scenes were generated in MATLAB using [].

The feature vector for our experiment is constructed by reshaping the cone-absorption matrix to a single vector in row(column?)-major order form.

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 the C-SVC cost parameter and the RBF $\gamma$ parameter.

References

Paper paper paper

Appendix I

Source code Result images