Automated Card Grading - Surface

[kevinlenane]

fingerprint.jpg

For anyone following along with my efforts to build a machine vision based grading, authentication and provenance company - we are currently crossing what i think is probably the last milestone before we can demo a real proof of concept. Grading corners, edges and centering is pretty straightforward - its the surface grades that wind up requiring the most training data and are the most challenging for a variety of reasons - mostly relating to how to create a perfect "mask" to compare all other "submitted"/scanned cards to.

We are pretty close to finishing a generic solution for this piece but we also checked another box in that in this process - by detecting that there is ample data to fingerprint a card image to mark and measure it for any alteration. When the alteration news really hit the forums - folks were utilizing a few obvious print marks and stains but the reality that is visible to pixel analysis is much bigger - once we figured out how to normalize what that perfect mask looked like - each and every card quite literally had a finger print. Anyway, this alone would be super useful to any grading effort - ive attached an image of an actual card and its departures from the "perfect version" - just thought folks here might find this extremely reliable feature interesting as guard against trimming and other alteration including any color changes which the machine sees as changes in pixel color.

[kevinlenane]

I should also note - that the actual surface grade is basically learned behavior and is a somewhat proportional measure of how far the card diverges from the perfect version and then also the extent of each diversion. And even a 10 or 100% will have significant diversions/anomalies.

[swarmee]

We touched on this a little in the CSG grading thread.

https://www.net54baseball.com/showpo...9&postcount=30

[toledo_mudhen]

Dam Kevin - I had been thinkin that I was a fairly innovative "IT" guy.

You're on a whole different level than the rest of us nerds - KUDOS bud!

[chriskim]

Come on! Pls don't bring AI to our hobby. We all love our beat up T cards, using AI grading them, they will all come back as 1s.

[kevinlenane]

swarmee -

The machine vision is capable of detecting the set, year and card and then can adjust its surface parameters and level of acceptance accordingly. This enables a more accurate and consistent score and also enables better fingerprinting though this isnt necessarily required for that unique identification of the cards given how unique each card is.

Oddly enough normalizing the perspective from the many possible/variable camera angles proved to be almost as difficult a solution vs. working out this surface adjustment.

[Gorditadogg]

Way cool. Looking forward to seeing you on Shark Tank.

Sent from my SM-G955U using Tapatalk

[tazdmb]

I want to be an Angel investor!

[Leon]

Very interesting technology. I believe there are several companies ramping this type (not exactly) of technology up at the moment. If I were a current TPG I wouldn't rest on my laurels....There could be a new mousetrap soon.

[kevinlenane]

perspective.jpg

Here is the perspective normalization I was referencing for those who are interested.... basically this ensures that we have the right angle on the card to evaluate appropriately. Otherwise minor changes in camera angle would produce dramatically unacceptable grades...

Leon who else is working on this? Would love to compare notes

[kevinlenane]

I also want to point out this is the first thread devoted entirely to Larry Bowa imagery.

[Goudey]

Originally Posted by kevinlenane

Attachment 417880

Here is the perspective normalization I was referencing for those who are interested.... basically this ensures that we have the right angle on the card to evaluate appropriately. Otherwise minor changes in camera angle would produce dramatically unacceptable grades...

Leon who else is working on this? Would love to compare notes

Hi Kevin, question for you: how does your normalization not affect the pixel vectors of the original scan? Given it probably affects the values you have for raw images ( assuming you're not reducing dimensions). Question 2: how do you deal with different size/complexity of scans to "grade"? seems like they could be all different size vectors. Super cool work