A digital image can be treated as a data set to extract information. Each pixel's intensity value is a measure of how much energy was used to generate it within the band of spectrum used to produce the image.

The sum of the pixel intensities is a measure of the total energy used to create the whole image. Most digital imaging programs provide a histogram utility that can be used to examine the data set of an image.

The usefulness of these histogram utilities varies. Unfortunately, none of them seem to provide clipboard, copy and paste functionality to interface with a spreadsheet. Adobe Photoshop's histogram dialog is better than most. The illustration on the left is of the Photoshop v3 histogram and shows what data is available.

In some applications it is useful to obtain a single value which represents the sum of the energies used to create an image. The following equation is one way to accomplish this. When the exponent w is set to a value of 1, this equation will produce a result in the range of 0.00 to 100.00.

This is a linearly weighted percentage of possible energy. If every pixel in the measured area had a maximum intensity value of 255, the SensiMetric value would be 100. The exponent w can be used to weight the curve for a particular imaging application.

The denominator is the equivalent of 2.55 times total number of non-zero valued pixels. For some applications, 2.55 times total number of pixels might be a better choice (sum from i=0 to 255). This equation is just a suggestion to get you started thinking about what is possible. This concept of generating a numeric value need not always be applied to the entire image. For some applications it may be useful to break the image up into segments and calculate numeric values for each segment.

Some applications will need to compare values from different images shot over a period of time. Certain elements of the image may have changed position over this period of time. Partitioning the image into small sections allows these elements to be floated to obtain better image correlation. Software may need to be written for handling such tasks.

Computers create many possibilities for extracting information from images created with the methods of spectral selectivity. Each application for imaging using spectral selectivity will have its own characteristics and requirements. Careful evaluation of these will allow generation of useful methods.