The Universe of ( ) Images Part 2

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The Universe of ( ) Images Part 2
Name The Universe of ( ) Images Part 2
Location Froh! Cologne
Date 2018/12/07-2018/12/09
Time 10:00-17:00
PeopleOrganisations Hackers & Designers, Froh!
Type Meetup
Web Yes
Print No

Part 2 – Cologne!

GroupCologne.png photo by Fabian Weiss


The long-held idea of images as proof of reality vanished. Washed away by manipulative practices of image production our hyper-visual media streams have become highly subjective and emotional. Authenticity claims to be the new challenge while power structures shift and users become creators.

In this second part of the workshop we dived more into the paradox of the Universe of [ ] Images: our long-held idea of images as proof of reality vanished. Washed away by manipulative practices of image production our hyper-visual media streams have become highly subjective and emotional. Authenticity claims to be the new challenge while power structures shift and users become creators.

FabianWeiss UniverseOfImages Cologne2018 15 FAB2737.jpg Photo by Fabian Weiss


For two weeks a group of hackers, designers and photographers joined us to explore the shift of construction and perception of 'truth' in visual culture through hands on making, experiments and discourse. The outcomes, processes and methods of the works was presented in an open workshop and installation with video presentations by:

Participants of the workshop were: Leith Behkhedda, Miriam Chair, Roberta Esposito, Dennis Fechner, Raphaël Fischer-Dieskau, Isabel Garcia Argos, Sophie Golle, Katherina Gorodynska, Niels van Haaften, Jan Husstedt, Tiziana Krüger, Felix Rasehorn, Lisa Schirmacher, Io Alexa Sivertsen, Jurian Strik, Upendra Vaddadi, Lacey Verhalen, Klaus Neuburg, Juliette Lizotte, Anja Groten and Jeannette Weber.

This second weekend ended with a public presentation at Atelier 4 in Cologne: the event was open to all and free of charge!

FabianWeiss UniverseOfImages Cologne2018 1.jpg Photo by Fabian Weiss

Projects

The Collective Publication

While long-held ideas of images as proof of reality are increasingly challenged, this publication presents methods and processes of hands-on learning and unlearning about the production and reception of digital images.

The projects, tools and methods were developed in the context of a workshop, taking place in Amsterdam and Cologne in November and December 2018. The aim of the workshop was to collectively develop an understanding about (de)constructions, accumulation and manipulation of images and image production, – including the dominance of certain technologies relating to image making, power structures inherent in images, and omissions entailed in processes of image creation.

The publication should be approached as a collection of tools and ideas which invites the readers to use and appropriate whatever they find useful. The binding method and standard paper sizes, allows for compiling and reshuffling the content depending on the purpose, and even allows for the addition of new tools and methods of image making.

The readers are therefore addressed as users and makers at the same time and invited to join the conversation about questions such as: how can the tools we build and use shape how we publish and consume media? How can we trust our perceptions when concepts such as truthful visual representation have vanished? Can ideas of ‘subjectivity’ and ‘partial perspectives’ replace notion of objectivity and rationality? …

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Dennis Fechner

From [ ] to [ ]

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Process

  1. Open Image.
  2. Scale to 7cm, 72dpi.
  3. Scale to 700cm, 72 dpi.
  4. Repeat Step 2 and Step 3.


Katherina Gorodynska

Non flash mitsuku.png

ELIZA Eliza is a mock Rogerian psychotherapist. The original program was described by Joseph Weizenbaum in 1966 MITSUKU Mitsuku is an artificial intelligence that you can talk to like a real person at www.mitsuku.com Hi, I am the worlds most humanlike conversational AI REPLICA Whether you are feeling overwhelmed, alone, or just need to chat, Replica is here for you. ZO Your AI friend, here to talk whenever you need it Discover new things about yourself with every conversation ROSE Rose is a yuppie who has an unorthodox family and quirky attitudes to life. You will find her secretive on some subjects as her work has made her aware how under surveillance we all are. ALEXA Alexa calls itself female in character.

Would you rather your virtual assistant be male, female, or gender neutral? Male 9 % Gender neutral 7 % I do not have a preference 24 % Other 1 % Female 58 %

https://www.androidauthority.com/virtual-assistant-voice-poll-886294/

Analyzing Image....

What image do you have of me while we are chatting?

Why do you need an image?

Can you draw me?

How do I look like? How does my interface look like?

If swimming is such a good exercise, why are whales fat?

Lisa Schirmacher

Algorythmic Faliure An Installation of a Computer-Human-View

Lisafinaall hd-1.jpg

"I visualize how abstract data in computers actually are and how they make up things and maybe produce failures. Every program has its own rules of how to deal with data and therefore every digital tool influences the outcoming result and generating new data along the way. To create this experience and make it most perceptible I transferred this data in a very primal space: the 3 dimensional „reality“. The case of the portrait is for me the most interesting and I am also curious about how identity and personality is destroyed through reducing real images to pixels and through defining rules to read this pictures. In every picture you only see what you want to see."

Process

  1. Take a normal photo of a person.
  2. Convert in black and white with Photoshop.
  3. Take the highpass filter.
  4. Increase the contrast by using levels in Photoshop.
  5. Pixelate your image.
  6. Increase contrast by turning down brightness and increasing contrast.
  7. Use the website http://cvl-demos.cs.nott.ac.uk/vrn/ to convert your 2D Image into a 3D Image. Blend out the background.
  8. Repeat step 6 to see how the algorithm reacts.
  9. You can export Wavefont OBJ File to look at your Image in Cinema 4D. You have to delete the .txt at the end of the file first. Now you can make a video or something else. Have fun!


Miriam Chair

Are you able to recognize faces?

"I was fascinated by humans that are (still) able to process faces better then computer softwares. In 2009 it was discovered that there a people that can remember faces and recognize them after years and with changes of their appearance, even if they only saw them a few times. They are called ‘Super Recognizers.’ These people are not only interesting for scientist but also used in criminal cases. Different to most softwares that need a lot of input photos to recognize someone and are unable to look over changes of hair/beard/hat etc. Super Recognizers can identify persons on surveillance cameras and in real life just after seeing them on a photo. With a photobooth application I wanted to engage visitors to make a photo of themself or someone else and in a second step gave them a minute to look at it and draw from there memory"

Photobooth in Processing

  1. Install Processing and Import the Video Library
  2. Activate the webcam of your computer with the processing application

https://processing.org/reference/libraries/video/Capture.html

3. To make a snapshot by clicking the mouse ad this to void setup():

  if mousePressed(); {
  video.read();

//to save the image add:

  saveFrame(“Folder/ #### .jpg”)
  }

4. There are different filters to stylize the image under the line void draw(); https://processing.org/reference/filter_.html

Niels van Haaften

Niels.png

The Viola-Jones Algorithm is the first object detection framework for object detection in real time proposed by Paul Viola and Michael Jones in 2001. The algorithm can be trained to detect a variety of objects, some of which are traffic signs, cars and most famously faces.By detecting various objects and people within a space and the ability to track their movement the Viola-Jones algorithm and all of its offspring have become a player within our 3D world.

Step 1: From an array to a 2D imageA picture is basically one big array (a kind of list) filled with all the RGB values of the picture. So every three pixels represent the color of one pixel. In order for the algorithm to be able to read it as a 2D image, it first has to be rebuild as an 2d image. There is a specific section of looped code in order to do that. Within the loop the object detection takes place.


 _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
| | | | | | | | | | | | | |X| 
 ¯ ¯ ¯ ¯ ¯ ¯ ¯ ¯ ¯ ¯ ¯ ¯ ¯ ¯  
  0 1 2 3 4 5 
  _ _ _ _ _ _
0|_|_|_|_|_|_| x + y * width
1|_|_|_|_|_|_| ↓
2|_|_|_|_|_|_| 2 + 2 * 6
3|_|_|_|_|_|_| ↓
4|_|_|_|_|_|_| 14


  for (int x = 0; x < video.width; x++){ 
    for (int y = 0; y < video.height; y++){ 
        int loc = x + y * video.width; 
    }
  }

With loc being the location of the pixel in the array of the video file. And x and y being it’s location within the 2d image (based on coding within the Java framework Processing).

Step 2: Looking at contrast within the imageThe algorithm detects objects by looking at the Haar features of an object. Haar features are specific strongly contrasting areas within the image of an object. So with the front of a car for example, the bottom area is always a lot darker than the hood of the car.

Screen Shot 2018-12-19 at 12.51.43.png

Because Haar features are only a weak learner or classifier (its detection quality is slightly better than random guessing) a large number of Haar-like features are necessary to describe an object with sufficient accuracy. And in the case of a car for example you wil need differents sets of Haar features for the font, the sides and the back of the car.

Screen Shot 2018-12-19 at 12.51.57s.png

Because of the large number of Haar-like features that are necessary to detect an object the detection proces is split up in different cascades. A cascade is a smaller set of Haar-like features of which the first is used to quickly detect if there is a chance of a car being in that part of the picture. The second is to detect it with even more certainty and so on…

Screen Shot 2018-12-19 at 12.52.08.png

Step 3: Training the algorithmIn order for the viola jones algorithm to be able to know what an object looks like the algorithm first needs to learn what it looks like. This proces takes place by feeding it a lot of positive images (images of faces) and negative images (images without faces) to train the classifier by extracting the haar-like features from those images.

Jurian Strik

The dark side of user friendly interfaces.

Jurian.png
Jurian2.png

Raphaël Fischer-Dieskau

Michelle Brown

Raphael.png
Raphael2.png

Process

  1. Gathering faces from characters
  2. Faceswapping the two faces with free software or with Hay's Face Tool
  3. Morphing the two faceswaps into one face
  4. Using a CNN algorithm to interpret the 2D face in a 3D mesh
  5. Make the 3D mesh 3D printable
  6. 3D print the face
  7. Use text to speech software for text
  8. Edit recordings
Raphael3.png

Roberta Esposito

Travelling through the internet

Google research by images can take you very far. Starting from Cologne, I wanted to travel the internet jumping from one picture to another to see how far the research could take me. We’re used to sending postcards from our holidays to family and friends, but nowadays we are getting more used to sending selfies through messaging apps. That’s why I collected “selfie postcards” during my trip.

Roberta.png

Process

1st TRIAL

  1. Took a selfie in Cologne
  2. Uploaded it on Google research by images to see the most similar result
  3. I picked the first result and uploaded it again in the research bar and saved again the first image I got
  4. I did it several times, until I got stuck because I always got the same image as the first result


2nd TRIAL 3. I picked the first three results and uploaded each one again in the research bar 4. I saved the first three results that I got for each image I searched File:Roberta Tree 5. This reseach is still in progress [...]

Sophie Golle

WHAT DO PICTURES SOUND LIKE?

  1. Convert a picture to an audio file with Photosounder, MetaSynth or Sonic Photo (for Windows only)
  2. Save the audio file (not possible with free demo of Photosounder and MetaSynth, but you can record the sound with Audacity and save it)
  3. Use the spectogramm (programme: Sonic Visualiser) to make the picture, hidden in the generated audio file, visible


Download:


Tutorials:


More Information: What do pictures SOUND like?:

Hide Secret Messages in Audio:


DELETE / SMOOTH / KING - PROTOTYPE OF AN IDEA Manipulative practices of image production are not only used by the media. Celebrities and advertising industries are making use of it as well as people in power. Common practice include deleting political enemies from photos, airbrushing the face of an aged leader to revive a youthful appearance or the so-called Bedeutungsperspektive, which is a perspective in pre-renaissance painting that sizes persons and objects according to importance. The idea of the project is three voice commands (“Delete”, “Smooth”, “King”) activating three different ways of political image processing.

Sophie.png


More Information: The Politics of Design: A (Not So) Global Manual for Visual Communication


Isabel Garcia Argos

G–Earth Tones

Isabel.png

G–Earth Tones is born from a fascination with the diversity, colors and shapes of our planet. The project has a special interest in the aesthetics of images gene-rated by the application Google Earth and how new technologies allow us to travel through a screen. Where is the limit between representation of reality and unrealistic images generated by a computer ? The final project is composed by a website, a book and postcards that make you travel to the most remote corners of the world. The website presents a selective color palette and each color shows different Google Earth views having the same tones. Cliking on the images brings you directly to the Google application. In this way, the user can travel around different places depending on their favorite color. The book is a complation of the 300 landscapes and each picture comes with the respective tone, re-creating the full color palette. Finally the postcards are here as a complement of the project bringing back the idea of travel. The postcards only include the image and a QR code that allows the addressee to know the place thanks to Google Earth.

Process:

  1. Collect of places, links & QR codes
  2. Create the tone palette (15)
  3. Use of html/CSS/Javascript
  4. Generate invidual 300 tones
  5. Design of the book & postcards

Tiziana Krüger

32 Weapons

Wikipedia Cross-lingual Image Analysis makes the images of all language versions of a Wikipedia article comparable.

Tiziana2-2.jpg

Upendra Vaddadi

»Edible Images« is a series of zines, produced as a result of an ongoing experiment in generative publishing; and marks the starting point of such an experiment. The idea of edible images is rooted in the critique of the aesthetic economy that food operates within today. For, our food ecologies today have come a long way from the original organic landscapes and now stand as a universe of edible images, one that is housed within the convenient space of our neighborhood supermarket. With this process, our visual grammar in relation to food has also changed drastically; from being a tactical understanding of food as it naturally grows in organic landscapes, to a comprehension of food based on the two-dimensional images that claim to represent it.

Neo-Liberalization of food, as with everything else, has led to the expropriation and subsequent extinction of many indigenous bodies of knowledge, that once stood as empirical truths. As a result, we now find ourselves not only navigating a universe of edible images, but also a maze of incoherent nutritional truths.

We are therefore, in dire need of critically filtering our notions about food and generating more public discourse than already exists. Publishing, of course, since its inception has been instrumental in making things ‘public’. But how should publications occur today in our post-truth world? Can we still depend on knowledge bodies propagated by authors and authorities? Is there a way to overcome these ego-fueled autocratic structures? Can the “binary” machine be called upon as savior? Further, can this ‘binary’ machine that is often attributed with ‘objectivity’, be employed as an effective ‘auto-critical’ mechanism; one that perpetrates ‘critical’ bodies of knowledge, thus simplifying the process of humans navigating a post-truth world?

The experiment in generative publishing thus attempts to investigate the above, by posing the machine as an ‘auto-critic’. In the spirit of agonism, the machine as an auto-critic attempts to leverage discourses on the internet to generate zines based on a certain topic. By juxtaposing data from distinctly different sources, the zine as a ‘body of knowledge’ acts more as an ironical assemblage, one that attempts to provoke.

As, the »Edible images« series acts a starting point of this experiment, the zines have not been generated automatically or seamlessly by the posited auto-critic. Instead, they have been generated through a process that is partially human, partially machine, nevertheless critical.

Based on a keyword, data in the form of text and image was retrieved from different corners of the internet using methods of APIs, as well as manual scraping of open databases and discursive spaces. The retrieved data was then used to create a local databank, which was used to generate the series of zines, each issue of which corresponds to the particular keyword searched for.

To know more about how the zines were made and the tools that were employed, PTO.

Process

1. RETRIEVING DATA FROM THE INTERNET

The ‘News API’ was used to get a JSON data-list of the most recent news articles published on the internet, based on a keyword search. ‘Wikimedia Commons’, ’Open-Food Facts’ and ‘Reddit’ were manually scraped for text and images using the same keyword. (In spite of the fact that each of them offers an API)

Upendra a4 dokuss.jpg

2. CREATING LOCAL ‘DATABASE’

Data retrieved from step 1 was compiled into a local folder on the computer.

3. ‘GENERATING’ ZINES

Harnessing the powers of ‘basil.js’, data was pulled from the local database to ‘design’ the zines with code, instead of the traditional InDesign GUI.

Upendra a4 doku.jpg

LINKS TO TOOLS & RESOURCES:

Lacey Verhalen

CALMING LAKE AND MOUNTAINS / ANXIETY

Lacey.png

Process:

1. Image to ASCII:


2. ASCII Browser Preview to video file

  • Make a screenrecording
  • Tool: Quicktime, Kap, etc.


3. Download input file


4. Video Layering

  • Layer the ASCII and input video
  • Tool: After Effects


5. Download text to embed in the image


6. Complex-ify

  • Add text to the image manually
  • Tool: Aftereffects,Photoshop
LaceyGIF.gif

Documentation


Look back at the first part of the workshop: 23–25 November in Amsterdam and the kick-off lectures!

Some code bits:

If the pad does not show up visit here: https://etherpad.hackersanddesigners.nl/p/universecologne


The project The Universe of [ ] Images is funded by Fonds Soziokultur and Fonds voor Cultuurparticipatie

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