Psych 284

This Psych 284 wiki page enables students to post code, comment on the organization of the project, and document aspects of their work.

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Introduction

The course project is to build a computational infrastructure for modeling visual circuits and behavior.

Crazy ideas

Project ideas:

(A) How does the image amplitude spectrum change across the image processing pipeline?

Generate images containing noise patterns with particular amplitude distributions in the Fourier domain. For example a series of noise patterns such that the amplitude of the spatial frequency components in pixel space can be described by a function 1/f^n, with n spanning a range of values, such as 0 (white noise) 1 (pink noise), 2 (highly blurred) and so on. Then we calculate the amplitude spectrum for a series of images as they are processed through the visual system, including:

• 1. the pixel image
• 2. the irradiance image(assuming a typical LCD display)
• 3. the optical image (after passing through the eye's optics)
• 4. the cone absorption image
• 5. retinal ganglion cell image

We would like to know, how doe the amplitude spectrum change across these different stages of processing?

(B) Image Classification

Take a series of standardized images of object categories (e.g., Caltech 101?), and transform each into a series of new images, so that we have (again)

• 1. the pixel image
• 2. the irradiance image(assuming a typical LCD display)
• 3. the optical image (after passing through the eye's optics)
• 4. the cone absorption image
• 5. retinal ganglion cell image

Then we can ask, how well can a standard image classification algorithm learn to distinguish image classes for each of the separate images. Does classification get easier or harder across these processing steps?