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Of 36,157 CD covers I ran magic robotic vision code on, there were seven that were essentially invisible to the algorithm because they were too uninteresting (the algorithm is blind to color). L to R:
Gorgoroth: Live Bergen 1996 (view All Sizes to see that it's not all black).
Nusrat Fateh Ali Khan: Greatest Hits, Vol. 2.
J.S. Bach: Guitar Recital (Julian Bream Gtr)*
Spinal Tap: This Is Spinal Tap
Project Grudge: Between You & Reality
George Harrison: Brainwashed*
Staind: 14 Shades of Grey
* These two covers aren't really all black; My source for the CD images has a couple bogus entries.
On this task we explored the new possibilities to design a three dimensional object by combining algorithms and the 3d camera.
For further information go to:
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Visioni potenziate: creando immagini con l’AI.
Love is love.
L’amore visto in tante forme, anche non convenzionali, anche irriverenti, ma si parla sempre di amore. Questo è quello che vedo io.
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Enhanced vision: creating images with AI.
Love is love.
Love seen in many forms, even unconventional, even irreverent, but it is always about love. This is what I see.
By Kyle McDonald and Jonas Jongejan at Day For Night 2017 in Houston, Texas. Photo by Theo Civitello.
Visioni potenziate: creando immagini con l’AI.
Love is love.
L’amore visto in tante forme, anche non convenzionali, anche irriverenti, ma si parla sempre di amore. Questo è quello che vedo io.
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Enhanced vision: creating images with AI.
Love is love.
Love seen in many forms, even unconventional, even irreverent, but it is always about love. This is what I see.
combining multiple views from the kinect using a chessboard taped to the back of it as a 'diy 6dof sensor'.
i'm interested in combining multiple views as a method for overcoming the statistical noise associated with the kinect depth images.
I dreamed about a human being is is part of a project exploring the use of artificial intelligence as applied to photography by using online open source code and data.
More information at fransimo.info/?p=1100
ID:84a9c114232d0052a8a5a1c672e66cf0
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Tracking of a 4 spermatozoa with past positions.
In this example, a maximum of 6 spermatozoa can be tracked silmutaneously.
Three tracked spermatozoa connected by lines. The thickness of the lines depend on the distance between the tracked spermatozoa.
My computer vision surface is almost done. Frame is made from Qubelok aluminium system. It pops apart for travel.
Using frames of time rather than two cameras. Obviously doesn't work very well yet, could turn up some interesting results if I get it going though. Depth is represented by intensity in the top image, not a lot of it at the moment as you can see.
These grape harvesters represent the latest technology in berry picking. The tractors can strip a plant of fruit without doing damage to the vine.
A demonstration of the process of my end of year college project, which tracks movement through a space (in this case, St Enoch Square in Glasgow). Uses Processing for detecting movement, as well as the BlobScanner library for detecting large shapes (e.g. people) and the Mesh library for calculating a Delaunay triangulation of the resulting points. To see the full video and code, check my blog at velvetkevorkian.wordpress.com.
Cloud point animation build on Processing. The second cloud (in red) is dependent of the first one, it is constantly changing and trying to reach it.
For further information go to:
On this task we explored the new possibilities to design a three dimensional object by combining algorithms and the 3d camera.
For further information go to:
Visioni potenziate: creando immagini con l’AI.
Continuo a sperimentare per il mio piacere.
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Enhanced vision: creating images with AI.
I continue to experiment for my own pleasure.
An old project I came across while avoiding working on what I should be working on.
It's following the same basic steps as Mr Levin does here:
flickr.com/photos/golanlevin/1246224108/in/set-7215760097...
It uses the OpenCV Haar face detector to get a rough face area, then makes guesses on where eyes, nose and mouth should be (not shown), then does a Haar eye search for eyes in the eye region (split in left and right) and then does an ellipse fitting in those areas to find ellipse shapes.
It still needs quite a bit of work, currently the Haar eye trackers work better than ellipse fitting shown here. Actually most of the interesting stuff is not shown, but I had just started re-arranging the code and the debug drawing for those was not in place.
In the time I haven't been working on this I've figured out what a few of the things Golan mentions are though (and how they work), so I am looking forward to returning to this soon.
Example outputs of my new fiducial generator for reacTIVision using force directed layout and super customizable for adjusting overall complexity and size, it should be possible to create vast amounts of unique markers suiting many different usage contexts.
It also happens to generate very cute teddy bear lookalikes... :)