We’ve written about some of these issues in earlier posts, but this is one big, complete reference manual – a kind of ‘everything you wanted to know about light, but were afraid to ask’ – and it’s from NIST. They call it a ‘Self-Study Manual’ and it’s a clearly written tutorial on optical radiometry.

And it’s a free download. Enjoy. The test is Tuesday.

link to NIST Self Study Manual on Optical Radiation

The official title is The Self-Study Manual on Optical Radiation Measurements, edited by Fred Nicodemus

To test some non-imaging illumination optics, we set up our digital camera, and wrestled with the RAW data files from the camera. Most cameras have some ability to ‘see’ infra-red, so we can also test the pattern from the remote control output, or for other purposes.

Here we test the light uniformity of an LED source using a digital camera and some Thor Labs mounts.

These graphs were generated by ImageJ from the RAW data files of the Nikon D1x camera.

Here’s a link to an application note that explains these well, written by C. Richard Duda of UDT (now part of OSI Inc.).  Apertures, intentional and otherwise, are discussed, along with typical test configurations.

Link to pdf of application note titled Radiometripdf of Radiometric and

Please tell us if the link gets broken!

It’s free, was developed at NIH, is open-source, it has a ton of features and plug-ins, and you can write scripting macros, etc etc. It was developed so that the scientific community would have an open standard to process images. (Without an open standard for image number crunching, there’s no good way to independently reproduce an experiment that makes heavy use of images and image processing.)

You can read about it here at Wikipedia:

link to wiki entry for ImageJ program

http://en.wikipedia.org/wiki/ImageJ

It’s available here:

http://rsbweb.nih.gov/ij/

link to Imagej main website

We were turned onto this image analysis program by a couple of our clients. We recommend it. Today the cool thing was to separate the RGB channels, and allow us to ‘see’ an IR LED without being confused by the camera’s ‘grey scale’ clipping algorithm. Very nice.

differ from the response of a CCD camera sensor.

Using the data of a particular Hammamatsu CCD camera as an example,

we compared how silicon ‘sees’ to the photopic eye response

and compared both to a Planck black-body curve of a light at a particular

color temperature.

We don’t know what those lumps are in that CCD response curve – maybe some

strange reflection interference??

If you know – tell us!

spectroscopydetectorb-011408

Here’s a Tech Note that shows how our eyes respond to the Planck Black-Body radiator.

For a lamp filament at a certain ‘color temperature’ there’s a curve of how our eyes

respond to the lamp. Pete put this into a MathCAD model, and there’s a pdf here

that shows off a few nice graphs.

visualresponseintegral-011308