Greetings Morphmet members, Getting to the point: I have access to an osteological collection, where bones are being experimentally heated at a range of different temperatures and times. Starting January, I'll be 3D-scanning these previous and after to the event. As you can imagine, bones get severely deformed by suffering bone warping, shrinkage, fractures and colour alterations. I'm mostly interested in shape modifications such as the warping and that's why I'll be using GMM, but I'd like to be able to do something akin to "reverse engineering", in order to transform burnt bones into the respective original form. From what I understood TPS can do this quite easily. However, can it help me create a model to predict the original form of burnt bones that didn't make part of the original analysis (akin to the idea of a testing/validation set in machine learning)?
This is because there are already 10 burnt individuals in the collection, done before I talked to the main researcher about the potential of using GMM and 3D-Scanning, and since a lot of measurements and data where registered previously to the thermal alterations in these 10 individuals, I thought they would be ideal to test a model created through the sample of the next individuals that will become part of the experiment. I couldn't find any design experiments using GMM similar to this in the literature. So I'm a bit lost in how to proceed in the later steps, and I sincerely don't want things to go wrong. So if anyone could recommend statistical techniques I should use or test in order to create a predictive model to recreate original form I would be very grateful. P.S. Also, worth of mention: We have considerable data for each individual, with variables such as age-at-death, sex, bone weight, etc. -- MORPHMET may be accessed via its webpage at http://www.morphometrics.org To unsubscribe from this group and stop receiving emails from it, send an email to [email protected].
