Psych221 Camera Design

Imagine that you work for a cell phone company and you have been assigned the task of selecting the components for a camera that will be built into the phone. Because you work for a large vendor, you have parts suppliers calling you every day encouraging you to purchase their devices. Typically, these suppliers offer one component and it is your job to assemble a large variety of components into a working system.

In this project you will be asked to develop a method to make these purchasing decisions. Specifically, you will have to decide on which lens, anti-aliasing and IR filter, sensor, and color filter array you would like to use for your system.

Background

Your choice of optical components, pixel and sensors, and color filters is limited by that annoying thing called reality - not everything can be built. Still, there are many components and many possible combinations, and the choice of one component can have an impact on the selection of other components. For your project, we offer a list that you can choose from to build your camera. These are described here; we also provide some code to make it easy for you to build your model system.

List of Components

Color filters

Relevant scripts: s_sensorSimulation.m

Test Scenes and Images

The test images should include targets that help you evaluate the quality of your system. There are four general types of targets that you should include. These are:

• Macbeth (Gretag) Color Checker
• Slanted Bar target
• Uniform field
• A high dynamic range multispectral image.

The first three scenes can be generated using the ISET function sceneCreate.

You can download a set of multispectral images

It is also possible for you to create additional scenes or obtain additional images, if you would like to understand the system you are designing better.

Relevant scripts:

Evaluation Methods

The way you decide to evaluate the assembled components is crucial. We will ask you to explain your choice. In choosing the methodology, you must include assessments of

• Color accuracy
• Signal-to-noise
• Dynamic range
• Spatial resolution (blur)

There are examples of ISET scripts that calculate each of these metrics. You may decide to test your system using other metrics as well - and perhaps the biggest challenge you will have is to understand the tradeoffs between these different metrics and to compromise between the different optimizations.

Relevant scripts:

Helpful and Friendly Advise

Joyce Farrell (mailto:joyce_farrell@stanford.edu)