Doggleganger

Doggleganger  is an app  developed by the Pedigree Adoption Drive and NEC. It uses a simple face recognition system to match dogs in need of a home to the humans that use it.

The Artist is Present

Developed by pippinbar games, The Artist is Present is an old school Sierra-style recreation of the famous performance piece of the same name by Marina Abramovic. The user can relieve the experience including the incredibly long and frustrating line into the museum.

“Have the experience only a lucky few have ever had! Stare into Marina Abramovic’s eyes! Make of it what you will! Just like art!”

 Nando Costa | The New America

Nando Costa is currently producing a “crowd sourced” animation through kickstarter.com. Each frame of the finished production is being laser engraved on a separate block of wood, which those who would like to contribute can purchase to help fund the project.

import processing.opengl.*;

/*Alex Wolfe | Gauntlet*/

//Globals, adjust to edit nitpicky params
//Circles
int numCirc = 4;
int circMax = 100;
int circMin = 10;
//GridLines
int minRowSpace = 50;
int maxRowSpace = 100;
int minColSpace = 30;
int maxColSpace;
int minCols = 8;
int maxCols = 20;
int numrows, numcols;
int linewaver = 8;
int[] colSpace;
boolean[][] gridCheck;

//Lines
int minLines = 10;
int maxLines = 20;

RowLine[] grid;

void setup() {
  size(600, 600, P2D);
  background(255);
  smooth();
  strokeWeight(0.5);
  strokeCap(ROUND);
  noFill();
  stroke(0);

  //init colSpace grid
  int j =0;
  numcols = int(random(minCols, maxCols));
  maxColSpace = width/numcols;
  colSpace = new int[numcols];
  for (int i=0; i

class RowLine{
  PVector[] segPoints;
  int rowStart;
 // RowLine prev, next;
  
  public RowLine(int rowStart1){
    segPoints = new PVector[numcols];
    rowStart=rowStart1;
    
    if( (rowStart == 0) || (rowStart == height))
      initEdgeLine();
    else
      initLine();
  }
  
  void initLine(){
    int x,y;
    y= rowStart;
    for(int i=0; i
	

This page presents a quick link index to all of the interactive projects created in our semester course which made use of the new Microsoft Kinect depth-camera. These included a number of 7-week final projects, as well as sixteen small projects from a 2-week Kinect-specific unit.

Kinect-based final projects (May 2011):

• Kinect Fun House Mirror (Ben Gotow)
• Robotic Mimicry (Shawn Sims)
• Body Type (Mauricio Giraldo)
• Catching Flies Trampoline Game (Meg Richards)
• Never Turn Your Back on Mother Earth (Tim Sherman)
• Kinect Portal (Ward Penney)

Kinect-based small projects (February 2011):

• Comic Kinect (Maya Irvine, Ward Penney, Emily Schwartzman, and Mark Shuster)
• Mario Boo (John Horstman and Nisha Kurani)
• Magrathea (Tim Sherman and Paul Miller)
• We be Monsters (Caitlin Boyle and Asa Foster)
• Mix & Match Interactive Toy (Meg Richards)
• Kinect Flock (Alex Wolfe and Honray Lin)
• RoboScan (Shawn Sims)
• Neurospasta (Huaishu Peng and Charles Doomany)
• Will-o-the-Wisp (Le Wei and James Mulholland)
• Balloon Grab (Susan Lin and Chong Han Chua)
• Kinect Hand-Tracking Visualization (Ben Gotow)
• Kinect Tracer (Eric Brockmeyer and Jordan Parsons)
• Automatic Spotlight (Samia Ahmed)
• 2D♥3D (Mauricio Giraldo)
• Kinect VJ’ing (Riley Harmon)
• Interactive Projection Mapping (Marynel Vázquez and Madeline Gannon)

Abstract

Knitting is often dismissed as a way to pass time for the excessively hip, or the excessively old, and it’s potential for digital fabrication ignored. But really, at its most basic level, knitting is an extremely robust method of transforming one continuous line into a three dimensional flexible form, incorporating color pattern, and also texture and volume seamlessly.

For this project I wanted to explore knitting as a way to fabricate textiles based off of generative systems, choosing to focus on Reaction Diffusion.

Inspiration

Although digitally controlled generative patterns aren’t really in existence, hand knitters for ages have been applying mathematical principles in order to not only produce interesting color patterns, but also form and texture. 

(from left to right, machine knit generative fair-isle by Becky Stern, hand knit from Sandra Backlund's pool collection, and Cables with Whisky sweater by Lucy Neatbody)

My inspiration for this project largely came from Sandra Backlund, one of my absolute favorite knitwear designers. By hand, and without any particular pattern she creates beautiful volumes and textures. However, due to the tedious nature of the work, she is only able to produce a handful of pieces each season, each entirely unique and unreplicable. I started wondering if this process of creating knitwear could be digitalized and algorithmically generated, like the work I’d done before with flocking. Becky stern also has been doing some amazing work with hacking knitting machines to print out patterns in fair-isle, and I was delighted to find out that similar texture producing stitches could also be created in the same methodology. However, as anyone who has knitted before knows, just randomly generating stitches, crossing your fingers, and hoping for the best often produces horrible and ill fitting results. Fortunately, there is a large community of knitters who have been applying mathematics, such as fibonacci sequences, cellular automata, and mobious strips, to the craft for ages. Lucy Neatbody was actually one of the first to apply such methodology to texture, using probability to pseduo randomly cable a sweater.

Process

Since even the most intricate patterns can be reduced to a very simple set of rules, knitting lends itself well to combining with more traditional generative system algorithms. After experimenting with several, including Voronoi diagrams and Diffusion-limited aggragation, I decided to focus on Reaction Diffusion, using Grey Scott’s equations.

Reaction Diffusion is a chemical reaction that produces a large range of emergent patterns in nature, from fish scales, to leopard spots and zebra stripes, whose hosts are often killed so their pelts can be used for textiles. By using it as a basis for a pattern making application, I liked the potential for creating and then fabrication patterns of our own.

I began experimenting in processing, using Toxiclib’s excellent simUtils library, “growing” reaction diffusion to fit various patterns and forms, and then focusing on creating multiple layers of it in order to produce variety in pattern

However, Processing became frustratingly slow and unwieldy, especially when it came to seeding the diffusion off an underlying layer of the reaction. So following a tutorial by Robert Hodgin and rdex-fluxus on how to recreate Reaction Diffusion with shaders in Cinder I developed my final application.

Producing “swatches” of pattern by adjusting the parameters of the equation and user interaction, I then translated them into patterns for knitting by analyzing the colors of pixels in the corresponding row, and translating them into a series of cables and eyelets.

Since plans for using a computer controlled knitting machine fell through (which ended up being a much larger part of the project than I had ever intended), I then hand produced the above pattern on my own machine.

Doing it by hand instead of on a computer controlled machine took a significantly longer time (for something that was rather small), but it allowed me to incorporate certain textural stitches such as cables that would have been impossible otherwise. I really like the final product, and actually wherever there were 3×3 cables in the pattern produced the best results, but those are really difficult to produce on the machine (you have to knit those rows manually since the stitches are too tightly pulled). The 1×1 cables of which there was an abundance of in that middle section are much more subtle.

Conclusion

Although I am extremely pleased with the final swatch, ideally I wanted this project to operate on a much larger scale. Hopefully, one could have the application and machine set up so that once the user finishes creating their own swatch and entering their measurements, garments could be printed out immediately incorporating their unique texture in a few hours rather than the 28-30 it took to produce the swatch. Working with more advanced shaders in Cinder, and learning more than I ever thought possible about knitting machines, how they operate, (and also how to fix terrible broken ones purchased from craigslist!) were also excellent things to know I learned from this project. I’m very excited to pursue this project further, and am working on making a prototype dress using the algorithm, and modding a machine to just print this during the summer

I had a hard time coming up with a concrete concept for my project, so what I have so far is a bit of a hodge-podge of little exercises I did. I wanted to achieve the effect of finger painting with sound, with different paints representing different sounds. However, I’m having a really hard time using the maximilian library to make sounds that actually sound good and mix well together. So as a proof to myself that some reasonable music can be made, I implemented a little keyboard thing and stuck it in as well. I think the project would be immensely better to use with the wireless trackpad, since it’s bigger and you can hold it in your hand, but I haven’t gotten it to work with my program on my computer (although it might on another computer w/o a trackpad).

So what I did get done was this:

• Multi touch, so different sounds can play at the same time. But the finger tracker is kind of imperfect.
• Picking up different sounds by dipping your finger in a paint bucket.
• One octave keyboard

And what I desperately need to get done for Thursday:

• Nicer sounds
• Nicer looks
• Getting the magic trackpad working
• A paper(?) overlay on the trackpad so that its easier to see where to touch.

Special Thanks

Nisha Kurani

Ben Gotow

The previous checkpoint was a reality check, and I scrapped the computer vision project for a continuation of my twingring project.

A short list of things I have to do and where I am:
1. Put on the web
2. Fix bugs
3. Modulate sound to create different voices
4. Do dictionary swaps and replacements of text
5. Switch to real time API and increase filtering options
6. Design and multiple parties

Instead of doing a search, the new option will revolve around looking for hashtags or using a starting message id. With this, we can prototype a play with multiple actors as well as real time. This would enable twingring to act as a real life twitter play of some sort, which should be fun to watch.

On the user interface side, there’ll be some work required to improve the display of messages, the display of users, as well as a way to visualize who is talking and who isn’t. Some other work includes making it robust and possibly port it for iPad (probably not).

To check out the current progress, visit www.twingring.com

I have a Pure Data patch supplying pitch detection to a Java application, which will be drawing and animating fish in a fish tank based on the sounds people make with their voices. These red blobs are the precursors to the fish, where vertical width corresponds to pitch over a one-second recording. I plan to add colors, smooth contours, fins, and googly eyes.

Here is the patch:

I may end up ditching the cell phones. The point of the phone integration was so that many people could interact simultaneously, but now that I’m using Pure Data, which does real-time processing (not exactly what I wanted in the first place) it would be inconvenient to process more than one voice at a time.

Hello!
Thus far, I’ve been building the pieces of my project, and have a somewhat working, page-generator
Roadmap sketching:

Half one: generating pdfs! It’s happening! Currently generating single pages, this may be ideal, however, because I’m going to have to make an action in photoshop to automatically cut up spreads to be printed (hooray doublesided printing and perfect binding!).

Half two: building components of “visualizations” using my personal-schedule-data as a jumping off point to generate small viz that will be recombined on spreads to create the book. Right now, I only have two, and the mostly suck.

So right now, I can generate a rather simple, currently boring book, with a user specified page count

Now that the frame work functions, I need to really build all of the different visualization pieces, as well as rules for combining them, as well as start to deal with things such as variable pages size.

So there were speedbumps of many shapes and sizes. Large, small, technical and personal; production just kind of ground to a halt. This prompted us to basically bunker down and focus on our two largest challenges: a.) recording a file consisting of Kinect data to use during debugging, and b.) nailing down the angle tracking algorithm for our baseline skeleton-tracking-stick-figure-program. Caitlin took the reins on the data recorder, I worked on the maff.

The Kinect data recording bit is pretty straightforward and rather uninteresting, so I’ll just summarize: instead of having to get off our ass every time we press compile, we want it to just play a dummy so we can code a LOT more efficiently. We currently have a small snippet of data to access any time we need it.

The math bit is a hair more complicated, but can be summarized by saying that there are quite a few sets of quite a few points, which all need to run through an algorithm quite a few times. The goal is to create a baseline stick figure program, onto which we can build our puppets (or any other future programs for that matter), and we needed angle data for each of the joints on the skeleton, in three-dimensional space. The initial equation worked to calculate one of the angles at a time, but calculating all eight simultaneously became a data structure puzzle. My first instinct was to make arrays to hold these points, and multi-dimensional arrays to hold the sets of points, and then doing math with waaaay too many of [these] suckers. With Golan’s help explaining an object-oriented approach vs. the convoluted arrays that I had been using, we are now well on our way to finishing up the integration into the stick figure program.

In other news, this whole thing is somewhat changed by the fact that we did not get the Tough Art residency at the Pittsburgh Children’s Museum, meaning that we now do not have some floating due date in the future to worry about and thus just want to make this thing WORK. And well. More updates to follow.

Inspiration:

“Musical instruments come in a wide variety of shapes, sizes, and forms. Most have a long history, sometimes thousands of years, and their basic structure derives in part from the accidental discoveries of early musicians, in part from the properties of the physics of vibrating strings, columns of air, membranes, and reeds. Very little attention has been paid to the ergonomics of the instruments. As a result, they often require awkward body positions, such as the contortion of the left hand required to play the violin and related stringed instruments, and sometimes exert great strain: look at the bulging cheeks of brass players, or the calluses on fingers tips of string players.

I am convinced that if the instruments were introduced today and forced to undergo ergonomic review for health and safety, they would fail.

The piano, for example, is relatively straightforward to understand, but incredibly difficult to master. The learning time is measured in years. Note that there are two parts to learning an instrument. One is the physical mastering of the mechanics itself: how to hold the hands, posture, and breathing. Many instruments require demanding physical exertion or special blowing techniques. Some require different rhythms in each hand simultaneously, and some require use of both hands and feet simultaneously (harp, piano, organ, percussion).”

– Don Norman, Living With Complexity

Design Goal:

Create an experimental digital instrument – the design of the instrument will not be constrained by the acoustic properties of its physical form or by traditional design paradigms, but will be centered on providing an ergonomic and intuitive interface for musical expression.

Hardware/Software:

• Arduino + Processing + MIDI

Interaction:

• Possibly incorporate gesture recognition/ haptic feedback/ parallel screen-based feedback with physical interaction

• Synesthetic associations for notation and aiding performance; utilize multiple modalities

[?]

Are there any projects that you have seen that address a similar goal?

transit

Interactive 3D Drawing Generator

The motivation for this project is the same I had for Project 4 ((r)evolve).

Graffity Analysis v3.0

The idea is that a person holds a paper with a drawing made out of lines and this drawing is then tracked as the person moves the paper in 3D space. The drawing plus the motion generates a 3D model that can be visualized in the computer.

Personal Space Competition

Have you thought of how your personal space changes as your day goes along? This idea involves a small installation driven by transit data (maybe another type of data if new ideas come to mind).

The installation is composed by a constrained space (a box for example) and two balloons that compete for this space while inflating. Here’s an example of an inflating balloon controlled with an Arduino:

The amount of space they will take depends on the available space in a particular bus in Pit. Ideally, the project will run with online data (collected by the RERC-APT), but the iPhone app for collecting data was just pushed out to the App Store. Most probably this will run with data collected from a experiment done in the last months.

Is there any source of data about how personal space changes between cultures?

So because everyone is already familiar with the ins and outs of our project, we’re basically just telling people that a.) we have extensive plans for future updates, and b.) we need help from all of you with Processing chops to give this a read-through and throw in your two cents as to how to improve the mechanics.

For some reason the Prezi embed is failing on me, so here’s the permalink:

We Be Monsters Update

Incoming…

Presentation PDF: KinectHorse

Automatic Street Musician by Helmut Smits

Dialtone Symphony

lewei-final idea slides