Image comparison algorithm that ignores brightness

Question

I am looking for an algorithm that I can use to compare two images and determine if there is something significantly different between the two. By "significant", I mean, if you took two photos of a room and a large spider was clearly on the wall in one of them, you'd be able to detect it. I am not really interested in WHAT is detected or even where - just that there is something different. The algorithm would need to ignore brightness. If the room gets brighter or darker during the day, the algorithm should ignore it.

Even if you don't know of an algorithm, any hints in the right direction would help.

Thanks!

Answer 1

I'd try to perform a high-pass filtering of your 2d-data.

According to Fourier, every signal can be transformed to "frequency space" by analyzing which frequencies are in the signal. This also applies to 2d-signals, like images.

By the means of a "high-pass-filter", you remove all low-frequency parts, like constant offsets and slow gradients. If applied to an image it can serve as a simple "edge detection" algorithm. Looking at a sample might make it easier to understand:

I took an image of a spider on a wall from somewhere on the web (top-left). I then decreased the brightness of this image (lower-left). For both versions, I applied a high-pass filter using GIMP (This plugin). For both input images, the output looks very similar.

My recommendation: First apply a high-pass filter, then look at differences.

Possible problems

As requested, here are some problems that I can imagine.

• No sharp edges: if the object you want to detect doesn'T have sharp edges you probably will filter it out using HF-pass filtering. But what objects could that be? They must be huge, flat (to not produce shadows) and unstructured.

• Only color differs, not brightness: If the object only differs in term of its color, but the brightness is the same as the background, the grayscale-conversion might be a problem. But if you run into this problem, just analyse the R, G, B-data separately, then at least one channel should help detecting the object - otherwise, you can't see it anyway.

Edit As reply to ???, if you also adjust the levels of the high-pass filtered image (which of course is all around 0.5*256) by just normalizing it to the range 0, 256 again you get

Which probably isn't worse than your result. But, HP-filters are simple and, when using FFT, very fast.