51–60 of 171 posts
As a metrologist (and photographer), the difficulty with these techniques is that they can over-represent the information contained within an image; they present an image of what was "probably" there, rather than representing what was. These aren't so different from our own brains, which remember what we thought we saw, rather than the light that reached our retinas. These methods are already in extensive use (most s…
As a metrologist (and photographer), the difficulty with these techniques is that they can over-represent the information contained within an image; they present an image of what was "probably" there, rather than representing what was. These aren't so different from our own brains, which remember what we thought we saw, rather than the light that reached our retinas. These methods are already in extensive use (most s…
Never mind memories; there are parts of our eyes that aren’t responsive to light at all. We’re always hallucinating.
Earlier quoted context omitted.
This “guessing” is nice for the sake of artistry, but we’ve got to be careful when knowing what actually was there is important—like when photos are submitted as evidence in court cases, or when determining the identity of a person from a photo as part of an investigation.
I hope such photos are submitted as camera takes them. With our without this new feature, photoshopping a photo before presenting it to court must be illegal.
Earlier quoted context omitted.
Absolutely not. If there's not enough information available, there's not enough information, full stop. Plausible (i.e. "good looking" or "believable") results are not the same as actual data, which is why enhance wouldn't work on vehicle licence plates or faces for example. Sure, the result might be a plausible looking face or text, but it's still not a valid representation of what was originally captured. That's th…
No there certainly is a chance for ML to improve here. Let’s take the classic example of enhancing a blurry photo to get a license plate. Humans may not be able to see much in the blur, but an AI trained on many different highly down-res’d images could at least give you plausible outcomes using far less data than a human brain would be able to say anything with confidence. You wouldn’t hold it up as the absolute trut…
The difficulty will be making sure people treat it the same way, because it looks like a normal image.
As a metrologist (and photographer), the difficulty with these techniques is that they can over-represent the information contained within an image; they present an image of what was "probably" there, rather than representing what was. These aren't so different from our own brains, which remember what we thought we saw, rather than the light that reached our retinas. These methods are already in extensive use (most s…
> These aren't so different from our own brains, which remember what we thought we saw, rather than the light that reached our retinas. Never mind memories; there are parts of our eyes that aren’t responsive to light at all. We’re always hallucinating.
Earlier quoted context omitted.
CSI and the like were just ahead of their time edit: I was joking, but people pointing out that you still can't create something out of nothing etc might not be thinking big enough. I think this technology absolutely has the potential to help. police are literally still using artists impressions - photofits, to find perpetrators
I think the artist impression has a lot more value than a highly realistic generated face. If you see an artistic impression, you will see the facial features that were noticeable. Such as a mole, the shape of the nose, or the thickness of the eyebrows. Then you have a template that your brain uses to match those features with any face that you see. However, if I show you a highly realistic face, your brain will take…
That aside, you'd still probably need a ML PhD to have a chance of correctly interpreting the results, given the myriad potential issues with current systems.
I guess professional photos have always been touched up and this is just an automation of this process. But I've long felt a little odd about the use of machine learning in photography this way. How long before Google Photos recreates dark photos by just reassembling all of the items it believes are there from machine learning?
And for artistic purposes, why does it matter how the final result was arrived at? If we have powerful and easy techniques for realising an artistic vision, that doesn't seem like a bad thing?
I like how he realizes the impact for pro cameras but doesn't highlight the elephant in the room: phone cameras. This means that soon for many people digital cameras outside of their smartphone will become an even more niche product.
As a metrologist (and photographer), the difficulty with these techniques is that they can over-represent the information contained within an image; they present an image of what was "probably" there, rather than representing what was. These aren't so different from our own brains, which remember what we thought we saw, rather than the light that reached our retinas. These methods are already in extensive use (most s…
Technically, they were correct -- it wasn't her. It was an algorithm's best-guess reconstruction based on training data of other people's faces. Unfortunately, neither the original poster or anyone else in the thread seemed to grasp this concept.