Automated Card Grading - Surface

[JollyElm]

There is another wrinkle to add to the mix. Would this system be able to detect the differences between a card that was actually printed on the lighter side (from a dearth of certain colors of ink at the factory) and one that has spent time in the sun and faded?

Here's an example of what I'm talking about...

1969topps567three.jpg

The top two look faded, but considering their coloring is identical (both were submitted by me and weren't left out in the sun or anything), they actually came out of the factory that way, so PSA graded them accordingly (Although, perhaps the 6 was graded accurately by one grader, and a different grader hammered the no creases/wrinkles 4 for being 'faded'?? Who knows.).

[swarmee]

Good question. There are all kinds of print defects or color printing issues that would need to be sussed out and appropriately weighted. I think your example error cards are not print variations, per se, since it looks like the omission of the black around "1969 Rookie Stars" is an intentional change to the black printing plate. All three cards have the black outline around the players and header, very clearly, and black is very tough to fade without damaging the rest of the colors.

[T205 GB]

Kevin it was great talking to you this morning. I think you are headed in the right direction and hope as things progress we are able to work together in, "making a better mousetrap," as Leon would say.

[GasHouseGang]

I think that was Ralph Waldo Emerson. Although I'm sure Leon has said it too.

[Case12]

Curious - are you using TensorFlow?

[kevinlenane]

To answer questions on the surface details - anything that the human eye can detect a machine can as well (w greater resolution on most new cameras) so any edge cases can be accounted for by simply accounting for them with real and synthetic data. It's worth noting that it is expected that there will quite a bit of this kind of give and take from user to company - feedback is going to be welcome and utilized so something like the nuances of Hocus focus or T card edge cuts would require some feedback and then review for accuracy/ethics before it gets accommodated in the grading. Generally you can expect the data to be extremely transparent and eventually explanatory.

[kevinlenane]

FWIW I haven't seen a surface crease yet that can't be detected at a pixel level - but that would corroborate with numbers from physical card a training data meaning the human can inspect it in anyway to provide the grades associated w the image. In this way - there are no gaps between eyes and lens. The only thing that might get missed in this dual training model is a stain or
mark that only exists on the edge of the card but doesn't show on the front or back when looking at the edges. I've only been able to produce this on extremely thick cards like the modern memorabilia/insert stuff - but in okay not worrying to much about that case right now...