To Tomato or not toomato – that is the question!! (apologies to Will).
There has been much constructive criticism of my video on the structural geology of North America ( https://www.youtube.com/watch?v=MtzmRbDw73w ) from the surface to the core. As an ‘interested spectator’ I noted a few points. And it set my overactive brain thinking. I then re-examined the Tomato photo and enhancement more closely.
The tomato was enhanced using the EagleEye process by a ‘first-time user’ and it was quite a credible job! This is the result, and shows my instructions are readable and executable.
The enhancer stated that because the tomato showed structure (which they believed it shouldn’t) that it proved that the EagleEye enhancement system is ‘pseudoscience’ and all the structures produced are ‘artifacts and spurious’. This was very clearly stated about 30 times in the ensuing discussion. I even briefly entertained the sneaking suspicion that the aim was to diminish my good reputation 😊.
After a couple of weeks of spirited discussion (not by me) the tomato enhancer stated that,
….. As for ‘demonstrable results’ – where? Here’s a challenge: …. “FIND ONE EXAMPLE OF BOB’S WORK WHERE THE EAGLEEYE METHOD IDENTIFIES KNOWN GEOLOGICAL STRUCTURES” …..
However, when I looked at the infamous Tomato enhancement, I observed a few interesting things. Those things were entirely consistent with my oft stated (with 110% certainty) that EagleEye enhances real structures that are not seen in blurry, fuzzy images. And that these structures can be proved to be real!. The examples below may be agricultural 😊 but here are THREE EXAMPLES IN THE ENHANCER’S OWN BACKYARD WHERE FEATURES ARE MUCH MORE CLEARLY DEFINED.
Examine and read about the following insets from the Tomato enhancement (not done by me), of the garden background and fingers, and see if you can see what I observed??
- Top LHS showing garden behind tomato. B. Garden shadows and objects now more clearly outlined and seen. If it was geology and I was familiar with the area or had access to plans I could do a lot of interesting speculation with 100% certainty that the structures were real, even if my speculation as to what they were was an educated guess!
- C, D. Lower RHS. Outline of thumbnail and garden behind is rougher but now clearer edges.
- E, F. The fingernail which is invisible in E is now visible and the finger muscle tension shown more clearly. This forms the basis of structural geology stress analysis.
CASE PROVED, all good fun and, for the moment, I’ll leave the proving of structures at that.
A note on my qualifications to understand what I’m doing (which has been one of the main criticisms of the enhancer and invoked spirited discussion in my defense (nice to be appreciated :)).
EagleEye uses a PowerPoint structure recognition algorithm and also edge effect and contrast algorithms to obtain the enhancement of the structures as is evidenced by the above examples. Show me where it doesn’t enhance and give better edge effects in the above, rather poor, examples??
Contrary to some comments I DO understand the broad scope of these treatments as they relate to current geophysical understanding of how images and data are manipulated. For the last 50 years I have worked with some of the best geophysicists who have invented all sorts of innovative geophysical methods. I was doing SP and IP for tin in 1970, EM for groundwater in the 1980s (one of first), helped with borehole radar in the 1990s (first) and microgravity in the 1990s. These are a few examples that spring to mind. I have also used (and understood the processes) of ERmapper and other geophysical packages to enhance data quite successfully for exploration targeting.
I have discussed these aspects of EagleEye with geoscientists who are interested and have used it for exploration. The fact that a ‘reasonable’ example of EagleEye enhancement has been produced in the Tomato exercise shows I have been successful in imparting the basics of my work (pat on the back Bob).
As ever constructive comments by all and sundry are most welcome.
Cheers
Bob
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