I have been jumping too much lately from subject to subject and getting too much input just created a bottleneck problem in my head. Here is what I have talked in the class for my proposal:

quote:”-data is everywhere. information?”

scenario 1: user just goes in the room, he sees the screen full of data from various web sources that look meaningless to him. Once he is in the range of the sensing camera, he sees himself inside the screen as a silhouette covered with these information flow. According to his moves, the data becomes something more “meaningful” for him. I am looking for ways to get certain inputs from the user and/or how to create this “meaningful” information.

scenario 2: the information on the screen is related to physical character of the user. The sensing mechanism calculates height, the shape of the body, the colours of the user’s clothes, brands and spits information according to this. advertising? Is this really what user wants? What would be our reaction to a strange machine inspecting us and our privacy, while everyday we are being inspected with eyes of everyone that surrounded us.

scenario 3: the information sources could be constrained. classified(bloody news, happy news). certain mappings. Red shirt you wear brings wikipedia entry about red shirt etc. getting cues from the audience and spit information according to that. What kind of information, historical, geographical, biological, horoscope?

idea : we are surrounded with lots of data that is meaningless to us. Is there a way to make this data more meaningful for us? Do we want this data to be more meaningful? What kind of data is more attracting to us than others? Is there anyway to reveal certain patterns in user behaviors that gives cues about information they are looking for? Could these patterns improve the quality of the information sources over time?

This was last week, I still feel something is missing in my project that I’d would like to make users aware. Here is the steps that I am going to take in this project:
1 – Get screenshot of users image through a camera. Try to get cues related to user. Colors of what he/she wears, his/her height are the ones that look more appropriate as I am stressing not to get this information through his/her intentions. Why? By getting this information without his notice, I am suggesting the information is already there whether we want it to be there or not. But still this gives me so little and abstract information unfortunately.

2 – Get the average color value of the image. Go to flickr and mine random images that have similar average color values. Get their tags and display on the screen. This is a little bit tricky and is still not the final thought. What I am trying to question is, could be the information related to the owner with only getting so little data, data that is put out of his/her intentions. The problem I will likely come across is that I am afraid the users are not able to connect what they see on the screen with themselves and the work will be left conceptually. So I need a strong element that connects those two. I might escape this in processing with adding something that is following this users’ path.

3 – Starting random images in flickr doesn’t seem to be a good idea to me at this point. At least there has to be some connection, why flickr, why starting with random a as opposed to random b? Those questions are still waiting to be answered. Also I have come up with this project called Open Mind Commonsense. The page seems to be down, but there is an article in KurweizAI.net by Push Singh. This is really parallel what I am trying to achieve. I am after text, images etc which is already there related to us. check out this first paragraph from the article:

Why is it that our computers have no grasp of ordinary life? Wouldn’t it be great if your search engine knew enough about life so that it could conclude that when you typed in “a gift for my brother”, it knew that because he had just moved into his first apartment that he could probably use some new furniture? Or if your cell phone knew enough about emergencies that, even though you had silenced it in the movie theater, it could know to ring if your mother were to call from the hospital? Or if your personal digital assistant knew enough about people that it could know to cancel a hiking trip with a friend who had just broken a leg?

4- Wouldn’t it be great if we can search the text by its affective emotions and color code the text according to that?

Technically I started with baby steps and right now I can get average RGB – HSB values of an image without any problems. Here is my code:

[code]
PImage b;
void setup() {
b = loadImage("deneme.jpg");
int rsum = 0;
int gsum = 0;
int bsum = 0;

for (int i = 0; i color redk = (color) ((b.pixels[i] & 0xFF0000) >> 16);
// println("redk: " + redk);
color greenk = (color) ((b.pixels[i] & 0x00FF00) >> 8);
// println("greenk: " + greenk);
color bluek = (color) ( b.pixels[i] & 0x0000FF);
// println("bluek: " + bluek);

rsum += redk;
gsum += greenk;
bsum += bluek;
}
println(rsum/b.pixels.length);
println(gsum/b.pixels.length);
println(bsum/b.pixels.length);
float[] hsb = Color.RGBtoHSB(rsum, gsum, bsum, null);

// println(hsb[0]);
size(200,150);
image(b,0,0);
}
[/code]

So for my next step, I should figure out how to mine images in flickr and get their tags according to those images. I should still think how I can reveal sensible information and connect those with the users.

This is a workshop that Zachary Lieberman gave back in November in Barcelona.

a workshop examining image processing and computer vision via the processing environment

There are lots of handful and new info about video sensing, I also learned new terms.
One is image quantization. one explanation is here:

Many people don’t have full-color (24 bit per pixel) display hardware. Inexpensive display hardware stores 8 bits per pixel, so it can display at most 256 distinct colors at a time. To display a full-color image, the computer must choose an appropriate set of representative colors and map the image into these colors. This process is called “color quantization”. (This is something of a misnomer; “color selection” or “color reduction” would be a better term. But we’re stuck with the standard usage.)

Quasimondo has a processing example of this here. And there is something called clustering algorithms which I still haven’t figured out what it is. I am going to dig more of those stuff in the summer hopefully.

I have been keeping those in my browser for a while now. Better to keep them here instead of there. Those might be helpful for what I am thinking to do for my final of Programming from A to Z. I am thinking to use video sensing at this point, but we’ll see.

This comes from all the way from Japan: Computer Vision with Proce55ing. I haven’t checked out the applets in details, but the names seem quite explanatory. There are bunch of stuff here from temporal differencing to background subtraction from clustering to pixel analysis.

Dotimpac.to sample apps : Those examples are not necessarily related to CV but there are some samples that are using video. Also I am always curious what people are doing other side of the globe, so I am happy to find these. The links are mostly gathered from processing.jp and Takashi Maekawa!

KrazyDad’s page has some nice processing examples as well.

Here is my ugly and partly not working obstacle avoidance try. A little bit background for the applet: The creatures are normally wandering and flocking, also checking obstacles, when an obstacle is at a distance that is dangerous for the creature, the creature quits flocking and wandering and makes the avoidance. Craig Reynolds has a great paper dated 1999 that he explains what’s going on in details: Steering Behaviors For Autonomous Characters.

there are certain problems with this approach. First of all, since the boids are moving together, some of them quits flocking but the ones in the behind do not since they are yet not at the dangerous point for the obstacle, and so that they pass through the obstacle. also as Reynolds explained in his paper, when you subtract your boid’s force from the obstacle’s force if they are head to head to each other, the boid stops instead of steering.
One workaround could be checking the force from multiple points.

I must admit my obstacle avoidance algorithm is too simple and it should be improved more. While searching for boids I have come across to Michael Chang’s very nice application while he did in Reas’ Design for Interactive Media class. Actually he did a very good job writing this algorithm for the avoidance especially for the bounding box which avoids obstacles. I studied his code and it looks like he is using polar coordinates instead of what I am used to, cartesian coordinates. I must say the polar coordinates somewhat come handy and make much more sense and easier to understand at some points. To refresh our memories what is the difference between polar and cartesian coordinates, we can check out this link.

flocking systems are so entertaining. Craig Reynolds’ did lots of cool stuff with those back in 80’s. There are lots of papers to be read and lots of video to be watched. Probably one of the most impressive is the obstacle avoidance one. Watch the video here. He wrote a Siggraph paper called “Not bumping into things” only about this subject. I am going to try to implement this for our nature of code class.

Spending the last few days brainstorming about my final projects. Lately I am really interested in transforming data into “tangible bits” as Hiroshi Ishii coined the term. One example of this could be fraesmaschine by Ralf Baecker.

Nowhere is a landscape in the condition of development. the users of the german search-engine METAGER erode rivers, canyons and valleys by their search-movements. search-requests, existing only for a fraction of a second on the internet, get inscribed in a block of pu-foarm (75cm x 75cm x 10cm) by a 3d milling-machine. the continuous stream of queries defines the rhythm of the machine.

Another example could be Email Erosion.

Email Erosion automatically creates sculptures out of biodegradable, starch-based foam using spam and email as stimuli. Based on email, the Eroder may elect to rotate the foam, raise or lower the Sprayer or erode the foam with a spray of water.

What I like about those pieces are mainly their ability to create something totally different from the actual source, transform this data more than mapping the data itself to a concrete material at least.

According to the Lev Manovich’s paper [Data Visualisation as New Abstraction and Anti-Sublime, 2002 .doc format] one of the earliest mapping project which revived lots of attention was Natalie Jeremijenko’s “live wire”.

The movement of the dangling wire is proportional to the number of packets on the network. That is, the more traffic on the local area network, the higher the frequency of the “wiggles.” The transceiver plugs into the network, and the dynamic behavior of the wire become an intuitive peripheral representation of the network activity. In contrast to a screen based graph of ethernet activity this device is a shared social display of information.

In his paper, Malovich points out that data visualization arts should be carrying certain reasons in their work. One of them is arbitrary versus motivated choices in mapping. He questions the artist’s mapping selection since the computers allow us to easily map any data set into another set. He suggests to foreground the arbitrary nature of the chosen mapping as one way to deal with this problem. Another question he stresses is the conceptual elegance and poetry that is lacking in the works. He gives the modern art as the example showing us the ambiguity always present in our perception and experiencee, to show us what we normally don’t notice or don’t pay attention to. He expects same kind of approach in visualizations.

The more interesting and at the end maybe more important challenge is how to represent the personal subjective experience of a person living in a data society. If daily interaction with volumes of data and numerous messages is part of our new “data-subjectivity,” how can we represent this experience in new ways? How new media can represent the ambiguity, the otherness, the multi-dimensionality of our experience, going beyond already familiar and “normalized” modernist techniques of montage, surrealism, absurd, etc.? In short, rather than trying hard to pursue the anti-sublime ideal, data visualization artists should also not forget that art has the unique license to portray human subjectivity – including its fundamental new dimension of being “immersed in data.”

Funny when I was searching for live wire project in google, I have come up with Sculpting with Data class that Tom Igoe taught in Spring 2003. I am kind of surprised that class is not being offered last year or this year.

Tracing boids brought me great resources. First of all I have discovered the classes that Reas is teaching at UCLA with the help of Michael Chang one of his former students. This guy is really talented, he has done lots of great projects at his undergraduate and I am sure he is going to rock no less in the future. So Reas is teaching couple of classes, here is his page that has the listing of the classes.

Post regarding following Cellular Automata and Fractals week we had in Nature of Code.

Iterated function systems or IFS, are a kind of fractal that was conceived in its present form by John Hutchinson in 1981 and popularized by Michael Barnsley’s book Fractals Everywhere.

Iterated Function Systems
Classic Iterated Function Systems, a link on different fractals and explanation of those.

This educational domain page has a little bit more mathematical explanation of Directed-Graph Iterated Function Systems.
Another IFS link. That examples are more natural, just creating ferns and stuff. Finally some 3d implementations.
This site calls itself the best source about fractals on the net. Also Wolfram has a nice introduction about how CA works in 1d and 2d.

And at last but not the least this page is about diffusion-limited Aggretation. To be honest I don’t remember why I kept this page at the time. it should be related to Fractals probably.

Upon discovering receiver, I have come across to an article by Mark Pesce which is quite interesting. In article, he tells how our use of networks breaks down into three eras and we are on the verge of the third one which is understanding networks..

First, we built networks to help us find information. Google is a great example of this: pop in a few keywords and get back all the matches to your search.

Second, we are currently building networks to help us acquire knowledge. Knowledge is more than just information; knowledge is information plus context. Knowledge is information which has been sifted by human hands (or, more precisely, human minds), sorted, organized, and structured, so that the next person who comes along seeking knowledge can quickly and effectively absorb it.

And according to this technologist, philosopher and artist Mark Pesce, if knowledge is information plus context, understand is knowledge plus experience. Understanding is not data; it cannot be stored by a computer in any meaningful way. It resides within us, within the chain of experiences that makes us what we are. We can know a fact, but until we have lived through that fact, we have no understanding of it. Also one of the main properties of this approach is, the understanding should be dynamic, blogs as we see are first but static examples of those third era. According to the writer, this is the point where it comes to mobile.

This is rather an interesting point, I remember Josh Knowles was trying to build some kind of digital social network where evey person writes his/her expertise and interest fields. So that if you need any help at that time, you would know whom you are going to contact with.

In his concept of a mobile digital social network he tells more in details with examples. Liverecord is a mobile java application which allows you to submit your quality tips to a backend server. The server could be accessed through top tips from your friends while you share your quality with them. This could be a growing system pretty quick if you make it to read a tip you should write a tip approach.

So I was thinking personally that, the social networks sites that I am connected, I am getting away from describing myself in my profiles. Not that I don’t like, I don’t like that fact that if I start to list I feel like I am getting restricted somehow. It could be the first cautious approach towards those applications, and what I perceive in general is, how much you explain yourself, you are getting that much response.

So this third era is also supporting that view. It is how much you express yourself, so that people can take the most out of from you although I see some flaws and dangerous points at that. This is more likely the potential ultrapost-consumer approach at some point if people realize that their experiences are learnt. It all comes to teacher’s ethics maybe.

Anyway it was an interesting read. It would be nice to test it with an example application based on textmessaging.

Vodafone’s receiver magazine is a neutral space where pioneer thinkers challenge you to discuss exciting, future-oriented aspects of communications technologies. Started four years ago as a platform for exchange about how innovations in this sector affect societies worldwide, receiver is now established as one of the industry’s key idea generators.

word! I was reading an old (2002) but gold paper from Usman Hoquenamed invisible topographies. There are some good points about mobile phones.
Mobile Phones are not only communication tools, but also sensors of the invisible electromagnetic environment that surrounds us. A new term is Hertzian space. He is also talking about the emergence between architechture and art. He explains that new technologies encourage us to think designed spaces not of static silent structures that surrounds us but rather of fluid dynamic fields beyond the edge of our natural perceptions.

This link is a source listing Wireless Arts.

Couple of links that I have found through googling some names on the paper.
Varieties of Wireless Art

He mentions about a design theorist, Anthony Dunne, saying “material responses to immaterial electromagnetic fields can lead to new aesthetic possibilities for architecture.”

His project Sky Ear is exploring the hertzian habitat above Greenwich Park in London.

I like this paper because it is revealing how we can use mobile data in an artistic and informative way rather than trying to come up with new ideas of using it just inside of it.