“Everything looks worse in black and white”

My May edition of Evaluation Engineering is hot off the press, and as always I turned straight to the machine vision article. This is an interesting piece by Robert Howison of Dalsa about the value of color in machine vision. It provides a reasonable overview of the issues but, to be quite frank, I thought it needed more detail.

The main point Mr. Howison is trying to make is that color is often not necessary in a machine vision system, which I agree with, but I think more explanation would have been helpful.

First, we need to remember that the CCD or CMOS sensor does not sense wavelength; it just catches photons of light that pass through the lens of the system. So all its detecting is the quantity of light.

Second, a filter will let us trap photons of certain wavelengths, so preventing them from reaching our sensor. For example, if we use a green filter we are allowing photons with wavelengths in the region of 500 to 570 nm to pass through, while absorbing all other wavelengths. This is how we detect green light.

So, it doesn’t take too much imagination to see that combinations of colored light and colored filters can be used to help a monochrome camera detect light of a specific wavelength. Thus the complexity of color is often unnecessary. (I should add that color image processing can get really ‘hairy’, but any further discussion is beyond the scope of this blog.)

It’s possible that everything actually looks better in black and white, (which is what Paul Simon sometimes sings, as opposed to the title of this piece, which is what he wrote.)

Enjoy the article, but treat it as an appetizer.