video stills

Democratizing security one citizen at a time. Built with lasercut hardboard, iPhone 4, and custom html5 app.

The persistence of the lines could be affected by altering the opacity and the radius of the contour lines could be changed by setting the thresholding point, which is the cutoff point or “line in the sand” by which we determine what white and black are. Imagine flooding a mountainous area with water: everything above the water line is considered “dry” and everything below, “wet.”  If you walk along the shoreline, or contour, add more water and then walk the shore line again, you will find a much smaller ring the second time.  If you were to plot the path you took for each of these rings, you would have a topographic map.

Continuing with this idea, I automated the process and drew all the contours on each image, leading to a series of images like this:

Next I added all the points to 3D space and set up some basic camera controls to allow exploring the mesh and ended up with a video like this, which felt a lot like those old-school flight simulators from the 80′s.  There’s something very amusing about flying over your face…

If you’re interested in a technical description of the code, read on. The main hurdle to getting this completed was implementing a fast “nearest neighbor” system. From the chroma-key footage (“green screen” as it’s often called), I threshold it (which means turn it to black and white). Next, I perform edge detection which leaves just the outline to the dancer. By altering the edge detector’s “kernel,” a mathematical term for a table of numbers, I can degrade the outline so it is a series of dots rather than one solid line. Next, I go through the image and make note of the locations of all the points. All that’s left to do is to connect them with a continuous line. However, if you simply connect the dots in the order they were found (top to bottom, left to right), you will get a zig-zag line that fills the area of the dancer. The next logical step is to take a starting point and ask for it’s nearest neighbor and connect the two. Then continue the line to the nearest neighbor after that, and so on. Unfortunately, this naive approach does not scale well and takes an enormous amount of time to compute. Enter the KD Tree, a method of storing the points for fast retrieval. I had started to write my own implementation last year and got distracted. Thankfully, I found a simple implementation for Java which worked perfectly. It’s beautiful to see a series of techniques converge to produce something entirely new.

The source code is provided in two files: dancers_outlined.pde and Tree.java for reference. Please be warned–it is very rough and not intended for public consumption, although I tried to comment as much as possible.