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Author Topic: Major New Development in Computational Photography  (Read 619 times)

Chris Kern

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Major New Development in Computational Photography
« on: April 01, 2019, 12:08:01 am »

What may be the next major development in the use of artificial intelligence to improve photography is currently the subject of a well-funded research program by major players in the camera and software industries, according to a friend of mine who serves as a consultant to the project and who agreed to let me post a summary of the research as long as I don’t use his name or those of the participating companies.  He and his colleagues call it SWIM, which is an acronym for “Shoot What I Meant” (i.e., not necessarily what I actually saw in the viewfinder).

The essence of this technique is to train a neural network to optimize the images made by a camera in real-time based on advanced machine-learning techniques.  This involves feeding millions of pictures to the neural network until it figures out how to produce good ones.  Prototype cameras that employ this technology have already been made available for testing to selected photographers under strict nondisclosure requirements.  The software is computationally intensive and requires a lot of specialized supporting hardware.  While the eventual goal is to make these techniques available in a cellphone camera, currently a much larger enclosure is required: something the size of a current full-frame DSLR.

Accordingly, the researchers arranged with a manufacturer of high-end cameras to build a few prototypes, which look exactly like the company’s top-of-the-line “professional” reflex model but actually contain a mirrorless sensor, leaving room inside the body for the auxiliary data processing components and high-speed satellite uplink that are required to perform the image transformations.  As a security measure, the prototype emits the sounds of a mirror-slap and a mechanical shutter, and a haptics module even imparts a little shake while the camera is simulating the mechanical actions of a DSLR, although of course the software completely neutralizes any blur caused by camera motion—and even subject motion, for that matter.

A module for sports photography eliminates the need for the “spray and pray” technique commonly used when shooting fast-moving events and, consequently, reduces the time spent by the photographer or photo editor to cull a large number of only-slightly-different frames.  The AI software, which needed a different training set of images for each sport during the machine-learning phase, analyzes the action and anticipates the precise moment a play will reach a critical point, then triggers the shutter once to capture it.  According to my friend, a panel of seven experienced photo editors judged the system to be accurate 92.1 percent of the time for basketball, 89.5 percent of the time for international football, 84.0 percent of the time for American football, and 81.7 percent of the time for baseball.  (A cricket module was abandoned because the researchers concluded that the machine-learning effort would never terminate.)  In addition to anticipating the optimal moment to snap the shutter, the AI system uses selective focus to blur any extraneous players and automatically removes other distracting elements, in a manner which I gather is analogous to Photoshop’s content-aware fill.

The module for photojournalism is a derivative of the one for sports photography, but has been trained to anticipate the precise moment when a political figure or celebrity will strike the most awkward pose or display the most bizarre expression.  Hit rates, according to my friend, were even higher than for the sports photography examples, and would have approached 100 percent except for a consensus among the experienced photo editors that some of the images were “too disgusting for publication.”  (My friend declined to describe these outlier images, saying that was sensitive proprietary information.)  Conversely, the portrait module waits for the subject’s most flattering expression before triggering the shutter.  The portrait module also optionally employs an anatomical-improvement feature to discreetly modify the subject’s features according to a menu-selectable attractiveness parameter.

A landscape module is still under development, according to my friend.  In addition to making generic improvements to light, color, shadows, and scenic elements, it can be trained on images by famous photographers in order to emulate their style exactly.  For example, my friend used the prototype Ansel Adams module to shoot “Moonrise, New York, N.Y.”  He made the photo at high noon on a sunny, cloudless day from a vantage point in Secaucus, New Jersey, and the software automatically (1) changed the perspective of the Manhattan skyline to match that of Adams’ famous photo, (2) introduced a rising moon and cloud bank in the appropriate locations and proportions, and (3) adjusted the lighting with a tone curve that is indistinguishable from that of gelatin silver prints of his Moonrise picture that were made by Adams himself.  Further machine-learning along these lines has temporarily been suspended, however, pending a determination by the project’s legal consultants whether training a neural network to perfectly recreate the style a dead photographer constitutes identify theft.

Now I suspect some of you reading this post think the introduction of this new AI technology will remove all the challenge and therefore the satisfaction from the process of making great images.  I confess that was my initial reaction, too.  But upon further reflection, I realized that possibility must be balanced against the potential advantages of these cameras for the experienced photographer.  No need to get up before dawn to catch the perfect sunrise.  Or to stand around for many hours, hoping for the emergence of a dramatic storm that never materializes.  No more worrying about that irritating tourist who always seems to wander between your camera and the subject just before the decisive moment to capture a perfect street photograph: yes, the camera will automatically recreate the scene as though the interloper had not occluded your view.  And needless to say, your loved ones will be delighted with the way they look in your family snapshots after you crank up the attractiveness setting.  Artificial intelligence holds out the promise of finally eliminating the frustration many of us feel when a day of shooting doesn’t turn out the way we had hoped.

In any event, it’s coming whether we like it or not.  You can’t stop progress.

Two23

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Re: Major New Development in Computational Photography
« Reply #1 on: April 01, 2019, 12:41:10 am »

It makes me sad that anyone would deliberately want to take a bad photo  of someone, politician or not.


Kent in SD
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Slobodan Blagojevic

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Re: Major New Development in Computational Photography
« Reply #2 on: April 01, 2019, 01:07:44 am »

Already today, actually:

mcbroomf

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Re: Major New Development in Computational Photography
« Reply #3 on: April 01, 2019, 10:12:47 am »

Already today, actually:

Slobodan, you ruined that for me .. I glanced down and saw the graphic after reading only the 1st paragraph.  I guess it saved my keyboard from a mouthful of coffee though ...
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bwana

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Re: Major New Development in Computational Photography
« Reply #4 on: April 02, 2019, 10:21:16 am »

Actually, I would re-iterate what Thomson said about physics over a century ago. To paraphrase, there is nothing new in this world-someone has already done what you are doing' So the new cameras will just search the cumulative photographic output of the internet since its inception and give you the photograph you want (after it reads your brain).
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