In essence, Jackson & Jumper took the crude photo of the Shroud, determined location of 13 characteristic points on the face, determined their vertical coordinates on the model, determined the relative intensity of those points with VP-8 (much more primitive than ImageJ we use today), plotted linear regression, obtaining as a parameter max range of 3.7 cm, calculated correlation coefficient, determining that almost certainly there is at least some correlation, and then compared it with the profile for the whole front image of the Shroud. Simple technique, but remarkable achievement.

imageThat paragraph above is from the 29th slide in a self-paced, fully explained, fully comprehendible four-part presentation, 3D properties of the Shroud revised by regular participant O.K.

        Take the time to read this presentation carefully.
          Is this the definitive final word on the 3D properties of the shroud? No. For one thing it is unfinished, as O.K. tells us. O.K. promises to respond to our comments and questions and add to the presentation. So look for more.
          In my mind, this a very important presentation on the 3D properties of the shroud, perhaps the most important one so far.

        Am I convinced? Yes and no. I am convinced that the 3D means data is there. Smooth the image data, but not too little or too much, and you have, in essence, a height-field (my terminology, not O.K.’s).  Sure, it easy to imagine that this means that the data somehow represents body to cloth distance.  A carefully calculated correlation doesn’t make it so, however. It could be that there are other possible reasons even though I don’t have any strong ideas about what they might be.

        imageBy-the-way: I love the slide pictured to the right and the wording in the very next slide. It demonstrates that O.K. clearly understands the smoothing issue. Not many people do:

All right, I cheated. Here is the same image, but this time using ‘Thermal LUT’ instead of ‘Original colors’

[See  Part 1, slides 19 & 20 ]

As you can see, the image is blurred too much -too high smoothing applied, and a lot information is gone. We have a handy image in original colors, but actually disastrous 2D resolution.