Should I do Material Clasification/ Recognition using histograms or some other higher Math tool, like Bayesian Networks?

I'm learning the basics of OpenCV, and I thought a good project would help me make the studying more fun. After thinking some ideas I came up with some material recognition project. Let's say, I got myself a conveyor and it's transporting material for production of some product ( this product don't really matter, tho). There are 3 materials, and the illumination conditions will vary, (using natural light at the morning through the afternoon, and a light-bulb at night). That would be the problem description.

I was thinking of using sand, wood and rocks, which are easy to get. and place them on a plastic surface. After taking a pic, I'll apply some histogram to get the color, and using this color I'll identify the material. But, since the lightning conditions will change over time, when i take this photograph and apply the histogram, the color will change and the material won't be recognized properly. And I thought, what if I were to use sand and dust, they have very s开发者_如何学Goimilar color, but different texture, is there something that can help me with that?

I just want some ideas, and maybe some expert in the field could guide me.

Quite an advanced idea for a starting project. The differences in lighting could be tackled by using the HSV or other color spaces, taking the Hue component. However the matter of "texture" can be handled in two ways:

1. Feature descriptors: If you deal with the grey level image, there are a set of feature descriptors called the Grey Level Co-occurrence Matrix (GLCM) that gives a measure of the textures of different regions in the image. This is present in Matlab, for OpenCV there is the following code: in C.

   So you could take several standard shots of the sand, wood and rocks and use them as training samples on a classifier - NN, SVM, OpenCV's Haar classifier, whatever. Then train it with negative samples. The feature vector for the classifier will be the GLCM output for each picture. Then run it on the actual pictures and see how accurate they are.

2. Texture Roughness: Came across this useful paper that shows a single-valued measure for the 'roughness' of a texture called the Eigen Transform. The calculations are quite simple, especially if you use OpenCV's SVD() for eigenvalue calculations. The result of the Eigen-transform gives a value corresponding to the roughness of that portion. This can be used to separate out required portions.