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I am a physics experimentalist. In our research, we have our experimental data in a 400400400 matrix(x, y, z axis of a 3d space), each entry is associated with a value("brightness"). We expect the brightest entries will form a closed path in the 3d space. But some portion of the path is always too dark to identify. Our current algorithm doesn't do a very good job to extract the path from the data. I am not familiar with machine learning, but I am wondering if it can be used on this case? For example, is it possible that I tell the program which data points are on the path, the program will know how to choose the data points for the path in the future. Thanks. |
