I'm curious about suitability of Machine Learning techniques to classify grayscale images of polygons into categories defined by number of their sides, which will be kept small. Images will be "perfect" representations produced by an OpenGL rasterizer. Orientation, shape and scale of polygons will vary. Training data can be huge as it will be generated algorithmically on demand. In extreme case it can cover the whole domain, but the data size would get impractically large. Here is an image example, which belongs to category 4 (polygon with 4 sides):
Do you know about research addressing classification of synthetic simple geometric shapes? Do you have any advice about neural network architecture suitable for such task?
My goal is to compare performance of the most appropriate ML techniques with "old fashion" Computer Vision ones. In this simple case, just counting the lines produced by Hough transform or something a bit more sophisticated, can easily achieve zero error rate. AFAIK, neural networks will struggle even on these simple images. Black on white images are only the beginning: grayscale, color, line drawing, textures, etc. will follow, if NN can handle the easier cases, but my biased opinion is that they can't. The acceptable performance is under 0.1% error rate.