I am stuck in a pixel matching algorithm for finding symbols in an image. I have two images of symbols that I intend to find in an image that has big resolution.
Instead of a pixel by pixel matching algorithm, is there a fast algorithm that gives the same result as that of pixel matching algorithm. The result should be similar to: (percentage of pixel matched) divide by (total pixels).
My problem is that I wish to find certain symbols in a 1 bit image. The symbol appear with exact similarity in the target image and 95% of total pixel match with the target block in the image. but it takes hours to do iterations. The image is 10k X 10k and the symbol size is 20 X 20, so it will 10 power of 10 calculations which is too much to handle. Is there any filter/NN combination or any other algorithm that can give same results as that of pixel matching in a few minutes?
The point here is that pixels are almost same in the but problem is that size is very large. I do not want complex features for noise handling or edges, fuzzy etc. just a simple algorithm to do pixel matching quickly and the result should be similar to: (percentage of pixel matched) divide by (total pixels)
object recognition is tricky in that any simple algorithm is generally going to be way too slow, as you’ve apparently realized.
Luckily, if you have a rather large collection of these images on hand that are already correctly labeled, then I have a very simply solution for you.
Simply make 3 layer feedforward network with one input unit per pixel, all of which connect to a much smaller hidden layer, and then those in turn connect to 1 output unit (representing which symbol is present in the image). Then just run the backpropagation algorithm on your dataset until the network learns to identify the symbols.
Unfortunately, this doesn’t scale very well, so you might have to look into convolutional NNs for better performance.
Additionally, if you don’t have any training data (i.e. labeled examples), then your best bet is probably to decompose your symbols into features and then sweep the image for those. If you can decompose them into lines, then a hough transform can do this quite rapidly.