1) Video lectures – 15 hours of video in c. 10 minute blocks on: flat part recognition, deformable part recognition, range data and stereo data 3D part recognition, detecting & tracking objects in video, and behaviour recognition. There are also about 8 hours of introductory image processing videos. 2) CVonline – organising about 2000 related topics in imaging & vision, including some elementary neurophysiology and psychophysics. Most content is in wikipedia now, but the index is independent. 3) CVonline supplements: list of online and hardcopy books list of datasets for research and student projects list of useful software packages list of over 300 different image analysis application areas 4) Online education resources of the Int. Assoc. for Pattern Recognition 5) HIPR2 – Image Processing Teaching Materials with JAVA 6) CVDICT: Dictionary of Computer Vision and Image Processing
Including PDF slides, links to supplementary reading, a drill question for each video The site contains a set of video lectures on a subset of computer vision. It is intended for viewers who have an understanding of the nature of images and some understanding of how they can be processed. The course is more like Computer Vision 102, introducing a range of standard and acccepted methods, rather than the latest research advances.
Because of the improvements in the content available in Wikipedia, it is now possible to find content for more than 50% of CVonline’s 2000 topics. CVonline groups together the topics into a sensible topic hierarchy, but tries to exploit the advancing quality and breadth of wikipedia’s content.
HIPR2 is a free www-based set of tutorial materials for the 50 most commonly used image processing operators. It contains tutorial text, sample results and JAVA demonstrations of individual operators and collections.
This are the free view terms A..G from the the first version of the Dictionary, published by John Wiley and Sons. (Note there there a second edition currently on sale).
3D ANIMATION WORKSXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXxXXXXXXXXXXXXXXXXXXXXXXXX
PNG is an image format that has a history of development beginning in 1995, and it is still a popular, long living format. Generally, it is known for its features such as lossless compression and the ability to handle transparent pixels.
However, we do not look at image formats from a general point of view, but rather think of ways to glitch them. When we look at PNG from the point of view of glitch, what kind of peculiarity does it have?
Checksum
We should first look into the checksum system of the CRC32 algorithm. It is used to confirm corrupted images, and when it detects corruption in an image file, normal viewer applications refuse to display it. Therefore, it is impossible to generate glitches using simple methods such as rewriting part of the binary data using text editors or binary editors (you will completely fail). In other words, the PNG format is difficult to glitch.
We need to create glitches accordingly to the PNG specification in order to avoid this failure. This means that we must rewrite the data after decoding CRC32, re-calculate it and attach it to the edited data.
State
Next we want to look at the transcode process of PNG. The chart shown below is a simplified explanation of how PNG encoding flows.
Figure 1) PNG encoding flow
Each of the four states that are shown above can be glitch targets. However, glitching the the first “Raw Data” is the same as glitching BMP, so it technically isn’t a PNG glitch (at the end, it is the same as PNG with the None filter applied. I will explain this in the next section). The final “Formatted PNG” glitch will not work because of the checksum system I mentioned above.
This means that PNG glitches can be made when the “Filtered Data” or “Compressed Data” is manipulated. I will explain about filters in the following subsection. When “Filtered Data” is glitched, it shows a distinctive effect; patterns that look like flower petals scatter around the image. The difference between the filters become clear when the “Filtered Data” is glitched. On the other hand, “Compressed Data” glitches are flavored by their own compression algorithm, which is Deflate compression. It shows an effect similar to a snow noise image.
There are elements else besides the transcoding process that could also influence the appearance of glitches such as transparent pixels and interlaces.
Five filters
The factor that characterizes the appearance of glitches the most is the process called filter. The filter converts the uncompressed pixel data of each scanline using a certain algorithm in order to improve the compression efficiency. There are five types of filters that include four algorithms called Sub, Up, Average and Paeth, and also None (which means no filter applied). PNG images are usually compressed after the most suitable filter is applied to each scanline, and therefore all five filters are combined when PNG images are made.
These five filters usually only contribute to the compression efficiency, so the output result is always the same no matter which filter is applied. However, a clear difference appears in the output result when the filtered data is damaged. It is difficult to recognize the difference of the filters when an image is optimized and has all five filters combined, but the difference becomes obvious when an image is glitched when the same, single filter is applied to each scanline.
I will show the difference of the effect that each filter has later on, but when we look close into the results, we will understand which filter is causing which part of the beauty of PNG glitches (yes, they are beautiful) to occur.
I will show the actual glitch results in the next section.
Glitching: In practice
Figure 2) Original PNG imageFigure 3) Glitched PNG image
I have shown two PNG images above: one is an image before it has been glitched, and one is an image that has been glitched.
This is a Filtered Data glitch, which I explained in the previous section.
The original PNG has optimized filters applied to each scanline, and all of the five filters have been combined. The glitch reveals how the five filters were balanced when they were the combined.
Difference between filters
Lets look into the difference between each filter type.
Figure 4) Glitched PNG, filtered with NoneFigure 5) Magnified view of fig. 4
The image above has applied “None (no filter)”, meaning that it is a raw data glitch. Each pixel stands alone in this state and do not have any relationship with the others, so a single re-wrote byte does not have a wide range influence.
Figure 6) Glitched PNG, filtered with SubFigure 7) Magnified view of fig. 6
This is a glitched image that has the filter “Sub” applied to each scanline. When the Sub algorythm is applied, the target pixel rewrites itself by refering to the pixel that is right next to it. This is why the glitch pattern avalanches towards the right side.
Figure 8) Glitched PNG, filtered with UpFigure 9) Magnified view of fig. 8
This is the filter “Up”. This filter is similar to Sub, but its reference direction is the top and bottom.
Figure 10) Glitched PNG, filtered with AverageFigure 11) Magnified view of fig. 10
The filter “Average” refers to a diagonal direction. It shows a meteor like tail that starts from the damaged pixel. The soft gradation effect is also one of the peculiarities of this filter. The result of a PNG glitch when the Average filter is applied is a glitch that lacks glitchiness, and is also the most delicate portion of PNG glitching.
Figure 12) Glitched PNG, filtered with PaethFigure 13) Magnified view of fig. 12
The filter “Paeth” has the most complicated algorithm when compared with the others. It also has the most complicated glitch effect. The glitch will affect a wide range of areas even with the least byte re-writing. The keynote effect of PNG glitch is caused by this filter; the figure shown in the original image is maintained, but is intensely destroyed at the same time.
Glitch after compression
Figure 14) Glitched PNG, after compressedFigure 15) Magnified view of fig. 14
This is a glitch of the state that I referred to as Compressed Data in the previous section. A snowstorm effect appears, and it is difficult to recognize the original figure in the image. It infrequently remains to show effects of the filters. The image is often completely destroyed.
Transparence
Lets look into what happens when an image that includes transparent pixels is glitched.
Figure 16) Original PNG image with alpha pixelsFigure 17) Glitched PNG, with alpha pixels
The transparency comes as an effect. Especially the filter “Average” seems to blend transparent pixels gradually. A 100% gathering of transparent pixels is handled in the same way as a solid colored section. You can tell that the filter “Up” is often applied to solid colored sections.
(There is a possibility that newer general-purpose image formats switch their compression scheme of each part depending on if the image is a solid colored section, or else a complicated image such as photographs. The use of images that include solid colored sections for testing glitches is an effective method. One example is a WebP. )
Interlace
Figure 18) Glitched PNG, with interlaceFigure 19) Magnified view of fig. 18
PNG interlaces are divided into seven passes, using the Adam7 algorithm based on 8×8 pixels. We are able to visualy observe that algorithm when an interlaced PNG is glitched. We can also confirm a stitched effect, and that its angle has become narrow towards the Average filter (see appendix B).
Conclusion
PNG is a very simple format compared to JPEG or other new image formats. The filter algorithms are like toys, and its compression method is the same as oldschool Zip compression. However, this simple image format shows a surprisingly wide range of glitch variations. We would perhaps only need one example to explain a JPEG glitch, but we need many different types of samples in order to explain what a PNG glitch is.
PNG was developed as an alternative format of GIF. However, when it comes to glitching, GIF is a format that is too poor to be compared with PNG. PNG has prepared surprisingly rich results that have been concealed by the checksum barrier for a long time.
Appendix A: PNGlitch library
The author released a tiny script for PNG glitch in 2010. Back then, it only removed the CRC32 and added it back again after the internal data was glitched.
Since then, the author has continued to rewrite the script and make improved versions of it for the purpose of using it in his own work, but he decided to make a library that adopts his know-how in 2014. The Ruby library PNGlitch came out as the result.
Every glitch image that appears in this article is made by using this library.
Appendix A explains how to use the PNGlitch library.
(The user must have a certain level of knowledge of the Ruby language in order to understand the code snippet samples.)
How to use this library: The Simple Way
png = PNGlitch.open '/path/to/your/image.png'
png.glitch do |data|
data.gsub /\d/, 'x'
end
png.save '/path/to/broken/image.png'
png.close
The code above can also be written in a different way, like the one below.
PNGlitch.open('/path/to/your/image.png') do |png|
png.glitch do |data|
data.gsub /\d/, 'x'
end
png.save '/path/to/broken/image.png'
end
The glitch method handles compressed and decompressed data as a single string instance. It is handy, but on the other hand the memory usage amount can become enormous. When the memory usage is an issue, the user can write a code that uses IO instead of String like the one below.
PNGlitch.open('/path/to/your/image.png') do |png|
buf = 2 ** 18
png.glitch_as_io do |io|
until io.eof? do
d = io.read(buf)
io.pos -= d.size
io.print(d.gsub(/\d/, 'x'))
end
end
png.save '/path/to/broken/image.png'
end
PNGlitch also provides a method to manipulate each scanline.
PNGlitch.open('/path/to/your/image.png') do |png|
png.each_scanline do |scanline|
scanline.gsub! /\d/, 'x'
end
png.save '/path/to/broken/image.png'
end
The first example that uses the glitch method sometimes destroys bytes that express the filter type, so it might output a file that cannot be opened by certain viewer applications. The each_scanline method is much safer, and the memory usage is also low. It is a thorough method, but it takes more time than the glitch method.
How to use this library: Complex Manipulatin
Scanline data is made out of pixel data and the filter type value.
The user can also rewrite pixel data using Scanline#replace_data.
png.each_scanline do |scanline|
data = scanline.data
scanline.replace_data(data.gsub(/\d/, 'x'))
end
The user can also use Scanline#gsub! and do treatments like String#gsub!.
png.each_scanline do |scanline|
scanline.gsub! /\d/, 'x'
end
The user can confirm the filter type of the PNG file by running the command below. Internally, the filter types None, Sub, Up, Average and Paeth are all expressed by numeric values between 0 and 4.
puts png.filter_types
The user can also check each filter type using each_scanline.
png.each_scanline do |scanline|
puts scanline.filter_type
scanline.change_filter 3
end
The sample above has had each filter type changed to 3 (Average). change_filter properly applies the new filter type. This treatment will not cause glitches to occur because the filter is re-calculated and the PNG will be properly formatted. This also means that the resulting image will appear as the same to our eyes.
However, the difference of each filter has a large influence on the glitches.
PNGlitch.open(infile) do |png|
png.each_scanline do |scanline|
scanline.change_filter 3
end
png.glitch do |data|
data.gsub /\d/, 'x'
end
png.save outfile1
end
PNGlitch.open(infile) do |png|
png.each_scanline do |scanline|
scanline.change_filter 4
end
png.glitch do |data|
data.gsub /\d/, ‘x’
end
png.save outfile2
end
The output results of the two samples above are completely different. The difference is in the filters.
The code examples that I have explained are all manipulations done to the “Filtered Data” state. When the users want to glitch “Compressed Data” in PNGlitch, they must use the glitch_after_compress method.
png.glitch_after_compress do |data|
data[rand(data.size)] = 'x'
data
end
Appendix B includes a list of glitch variations that were not covered in the main article. This catalogue will reveal how wide the variety of PNG glitch expressions can be.
I will define 3 simple methods to destroy data.
Replace:
Randomly rewrite the byte string.
Transpose:
Divide the byte string into large chunks and re-arrange them.
Defect:
Randomly delete the byte string (to rewrite as an empty string).
It mentions five types of filters which are: Sub, Up, Average, Paeth, and the optimized and combined filter.
It also shows 120 patterns of combinations of if there is an alpha or not, if it is interlaced or not, and which state was glitched.
The generating script is shown at the end.
count = 0
infiles = %w(lena.png lena-alpha.png)
infiles.each do |file|
alpha = /alpha/ =~ file
[false, true].each do |compress|
[false, true].each do |interlace|
infile = file
if interlace
system(“convert -interlace plane %s tmp.png” % infile)
infile = ‘tmp.png’
end
[:optimized, :sub, :up, :average, :paeth].each do |filter|
[:replace, :transpose, :defect].each do |method|
count += 1
png = PNGlitch.open infile
png.change_all_filters filter unless filter == :optimized
options = [filter.to_s]
options << ‘alpha’ if alpha
options << ‘interlace’ if interlace
options << ‘compress’ if compress
options << method.to_s
outfile = “lena-%03d-%s.png” % [count, options.join(‘-‘)]
process = lambda do |data, range|
case method
when :replace
range.times do
data[rand(data.size)] = ‘x’
end
data
when :transpose
x = data.size / 4
data[0, x] + data[x * 2, x] + data[x * 1, x] + data[x * 3..-1]
when :defect
(range / 5).times do
data[rand(data.size)] = ”
end
data
end
end
unless compress
png.glitch do |data|
process.call data, 50
end
else
png.glitch_after_compress do |data|
process.call data, 10
end
end
png.save outfile
png.close
end
end
end
end
end
File.unlink ‘tmp.png’
Appendix C: Incorrect filters
A PNG scanline consists of a combination of a filter type byte and filtered pixel data. Deliberately making an incorrect combination is another technique in PNG glitching.
Figure C.1) PNG applied wrong filter types
The image above is generated by the code below.
In PNGlitch, the method graft is prepared so that the user can attach an incorrect filter type to a scanline.
require 'pnglitch'
PNGlitch.open('png.png') do |png|
png.each_scanline do |line|
line.graft rand(5)
end
png.save "png-glitch-graft.png"
end
This technique is convenient, even for checking how different the glitching effect of each filter is. Next five images are the results that applied one particular filter type byte to every scanline, without modifying scanline data.
Figure C.2) PNG applied wrong filter types, changing every filter type as NoneFigure C.3) PNG applied wrong filter types, changing every filter type as SubFigure C.4) PNG applied wrong filter types, changing every filter type as UpFigure C.5) PNG applied wrong filter types, changing every filter type as Average Figure C.6) PNG applied wrong filter types, changing every filter type as Paeth
require 'pnglitch'
(0..4).each do |filter|
PNGlitch.open('png.png') do |png|
png.each_scanline do |line|
line.graft filter
end
png.save "png-glitch-graft-#{filter}.png"
end
end
Implementation of an incorrect filter
What will happen if an incorrect filter is implemented? PNGlitch is designed to allow the user to freely change filter methods, so the user can test what happens at that state. A normal viewer application that uses a standard filter method is decoding a PNG image that is encoded by a distinctive filter method. This will perhaps generate an algorithmic glitch (I will not argue about if we should call that a glitch or not). The images below are part of such generated images.
Figure C.7) PNG encoded with an incorrect filter 1
require 'pnglitch'
PNGlitch.open('png.png') do |p|
p.each_scanline do |l|
l.register_filter_encoder do |data, prev|
data.size.times.reverse_each do |i|
x = data.getbyte(i)
v = prev ? prev.getbyte(i - 1) : 0
data.setbyte(i, (x - v) & 0xff)
end
data
end
end
p.output 'png-incorrect-filter01.png'
end
Figure C.8) PNG encoded with an incorrect filter 2
require 'pnglitch'
PNGlitch.open('png.png') do |p|
p.change_all_filters 4
p.each_scanline do |l|
l.register_filter_encoder do |data, prev|
data.size.times.reverse_each do |i|
x = data.getbyte(i)
v = prev ? prev.getbyte(i - 6) : 0
data.setbyte(i, (x - v) & 0xff)
end
data
end
end
p.output 'png-incorrect-filter02.png'
end
Figure C.9) PNG encoded with an incorrect filter 3
require 'pnglitch'
PNGlitch.open('png.png') do |png|
png.change_all_filters 4
sample_size = png.sample_size
png.each_scanline do |l|
l.register_filter_encoder do |data, prev|
data.size.times.reverse_each do |i|
x = data.getbyte i
is_a_exist = i >= sample_size
is_b_exist = !prev.nil?
a = is_a_exist ? data.getbyte(i - sample_size) : 0
b = is_b_exist ? prev.getbyte(i) : 0
c = is_a_exist && is_b_exist ? prev.getbyte(i - sample_size) : 0
p = a + b - c
pa = (p - a).abs
pb = (p - b).abs
pc = (p - c).abs
pr = pa <= pb && pa <= pc ? a : pb <= pc ? b : c
data.setbyte i, (x - pr) & 0xfe
end
data
end
end
png.output 'png-incorrect-filter03.png'
end
Figure C.10) PNG encoded with an incorrect filter 4
require 'pnglitch'
PNGlitch.open('png.png') do |p|
p.change_all_filters 2
p.each_scanline do |l|
l.register_filter_encoder do |data, prev|
data.size.times.reverse_each do |i|
x = data.getbyte(i)
v = prev ? prev.getbyte(i) : 0
data.setbyte(i, (x - v) & 0xfe)
end
data
end
end
p.output 'png-incorrect-filter04.png'
end
Surveying art that tries to reach beyond itself and the limits of our knowledge and experience, The Quick and the Dead seeks, in part, to ask what is alive and dead within the legacy of conceptual art. Though the term “conceptual” has been applied to myriad kinds of art, it originally covered works and practices from the 1960s and ‘70s that emphasized the ideas behind or around a work of art, foregrounding language, action, and context rather than visual form. But this basic definition fails to convey the ambitions of many artists who have been variously described as conceptual: as Sol LeWitt asserted in 1969, conceptual artists are “mystics rather than rationalists.” Although some of their work involves unremarkable materials or even borders on the invisible, these artists explore new ways of thinking about time and space, often aspiring to realms and effects that fall far outside of our perceptual limitations.
The exhibition title derives from a biblical phrase describing the judgment of the living and the dead at the end of time. But it has been used in innumerable ways since, including by the designer and engineer R. Buckminster Fuller, who in 1947 lauded what he called the “quick realities” of modern physics, condemning the “dead superstitions” of classical, object-based Newtonian theories. This distinction between objects and events underlined many conceptual practices of the late 1960s and ‘70s that pressed at the edges of the discernable—the work of artists like George Brecht, who seamlessly transformed objects into motionless events and asked us to consider “an art verging on the non-existent, dissolving into other dimensions;” Lygia Clark, whose foldable sculptures sought to dissolve the boundary between inside and outside, each “a static moment within the cosmological dynamics from which we came and to which we are going;” and James Lee Byars who, obsessed with a magically gothic idea of perfection that included metaphorical enactments of his own death, declared that “the perfect performance is to stand still.”
With an international group of 53 artists in a range of media, The Quick and the Dead expands beyond the here and now, reaffirming conceptual art’s ability to engage some of the deeper mysteries and questions of our lives. The exhibition brings together more than 90 works, juxtaposing a core group from the 1960s and ‘70s with more recent examples that might only loosely qualify as conceptual. Included in the show are new works made specifically for the exhibition and a number that have not been previously shown or realized. The presentation expands beyond the Walker’s main galleries to its public spaces, parking ramp, the Minneapolis Sculpture Garden, and the nearby Basilica of Saint Mary.
Artists in the Exhibition
Francis Alÿs, Robert Barry, Joseph Beuys, George Brecht, James Lee Byars, John Cage, Maurizio Cattelan, Paul Chan, Lygia Clark, Tony Conrad, Tacita Dean, Jason Dodge, Trisha Donnelly, Marcel Duchamp, Harold Edgerton, Ceal Floyer, Felix Gonzalez-Torres, Roger Hiorns, Douglas Huebler, Pierre Huyghe, The Institute For Figuring, Stephen Kaltenbach, On Kawara, Christine Kozlov, David Lamelas, Louise Lawler, Paul Etienne Lincoln, Mark Manders, Kris Martin, Steve McQueen, Helen Mirra, Catherine Murphy, Bruce Nauman, Rivane Neuenschwander, Claes Oldenburg, Roman Ondák, Giuseppe Penone, Susan Philipsz, Anthony Phillips, Adrian Piper, Steven Pippin, Paul Ramírez Jonas, Charles Ray, Tobias Rehberger, Hannah Rickards, Arthur Russell, Michael Sailstorfer, Roman Signer, Simon Starling, John Stezaker, Mladen Stilinović, Sturtevant, Shomei Tomatsu
4.301 Introduction to Visual Arts Assignment:Body Extension Create an art work that exists as a “body extension,” a sculptural/architectural/corporeal supplement. Use your own body as a starting point for an exploration of the self, including psychological and physical aspects and concepts of identity. The second part of the assignment asks you to interact with your body extension in a way that significantly alters your experience of it. Your interaction can be represented by video documentation, or by live performance. Exercises: – Bring in images that illustrate the concept of existing “body extensions.” – Use any appropriate means to visually represent an aspect of your body experience such as an inner emotional, physical or sensory process. Do not explain what you know about it, but what you experience, do not use language. Notation methods can include drawing, diagrams, collage, etc.. Readings: Horn, Rebecca and Celant, Germano, “The Bastille Interviews.” Rebecca Horn, 1993 Linker, Kate, “On Language and Its Ruses: Poetry into Performance.” Vito Acconci, 1994 Pejic, Bojana, “Being-in-the-Body, On the Spiritual in Marina Abramovic’s Art.” Abramovic, 1993
4.301 Introduction to Visual Arts Assignment: Shaping Time Using demonstrated production and editing techniques, make a 90 second experimental video. Sound must be diegetic or self generated. This unit asks you to express emotional qualities of your choice by creating a rhythm and/or procession of events through editing. Use as material for your video your physical self in space, possibly in relation to objects, props, processes or movements. Exercises: Make (2) one minute videos: one in which time appears to be moving quickly, the other in which time appears to be moving slowly. (Consider using a tripod.) Remix: Using a provided film clip, make a one minute video edited to convey an emotive quality not ordinarily associated with the clip. Reading: Brand, Stewart, The Clock of the Long Now Viola, Bill, “The Porcupine and the Car” and “I Do Not Know What It Is I Am Like”, Reasons for Knocking at an Empty House Gaensheimer, Susanne, “Moments in Time”, On Slowness, Lenbachhaus München
4.301 Introduction to Visual Arts Assignment:Body Extension Create an art work that exists as a “body extension,” a sculptural/architectural/corporeal supplement. Use your own body as a starting point for an exploration of the self, including psychological and physical aspects and concepts of identity. The second part of the assignment asks you to interact with your body extension in a way that significantly alters your experience of it. Your interaction can be represented by video documentation, or by live performance. Exercises: – Bring in images that illustrate the concept of existing “body extensions.” – Use any appropriate means to visually represent an aspect of your body experience such as an inner emotional, physical or sensory process. Do not explain what you know about it, but what you experience, do not use language. Notation methods can include drawing, diagrams, collage, etc.. Readings: Horn, Rebecca and Celant, Germano, “The Bastille Interviews.” Rebecca Horn, 1993 Linker, Kate, “On Language and Its Ruses: Poetry into Performance.” Vito Acconci, 1994 Pejic, Bojana, “Being-in-the-Body, On the Spiritual in Marina Abramovic’s Art.” Abramovic, 1993
2 κατεύθυνση των επίκαιρων βιωμάτων του δείχνει και σε αυτόν πιο πολλά πράγματα από όσα μπορεί να πιάσει, περισσότερα ίχνη από όσα μπορεί να ακολουθήσει, πιο πολλές δυνατότητες εμπειρίας από όσες μπορεί να πραγματοποιήσει. Υπόκειται και αυτός στην ανάγκη να επιλέξει. Αυτό που τον διακρίνει από διαφορετικά τοποθετημένους πράττοντες είναι μόνον η διαφορετική οργάνωση αυτής της επιλεκτικότητας» (Luhmann 1996: 438)1. Στις θετικές επιστήμες, τα εντυπωσιακά σχήματα των Φράκταλ συνεχίζουν να μας γοητεύουν αναδεικνύοντας λεπτομερώς την κρυμμένη («αόρατη») αρχιτεκτονική των φυσικών δομών2. Γενικότερα, στη σύγχρονη πολυπολιτισμική ψηφιακή εποχή της παγκόσμιας δικτυακής οικονομίας, της τεχνολογικής σύγκλισης και των αμφίδρομων και διαδραστικών συναλλαγών, η έννοιες της «μη γραμμικότητας», της «ανάδυσης», του «χάους», της «πολυπλοκότητας» και των «πολύπλοκων κοινωνικών συστημάτων» αποκτούν ένα ουσιαστικά νέο και δυναμικότερο περιεχόμενο. Η επιστήμη της Πολυπλοκότητας, ως η διαπειθαρχική ολιστική μελέτη των πολύπλοκων προσαρμοστικών συστημάτων (complex adaptive systems)3, αναπτύσσεται ραγδαία τις τελευταίες δεκαετίες, τόσο σε θεωρητικό όσο και σε πρακτικό επίπεδο, καταδεικνύοντας εναργώς ότι η φύση και η κοινωνία είναι πολύ πιο σύνθετες και απρόβλεπτες από ό,τι φανταζόμαστε, αλλά και υπερβαίνοντας κατά τρόπο ριζικό κλασσικά δυιστικά διλήμματα και άκαμπτους αναγωγισμούς. Ο απλοϊκός δυιστικός/διλημματικός και αναγωγιστικός τρόπος σκέψης και γλώσσας (η λογική του είτε-είτε) αντικαθίσταται πλέον από παραγωγικές, εύπλαστες και 1 Ο Luhmann συνδέει αναστοχαστικά το πρόβλημα της πολυπλοκότητας, το οποίο επιλέγεται ως «έσχατο σημείο αναφοράς των λειτουργικών αναλύσεων», με το «γεγονός ότι υπάρχουν πάντοτε περισσότερες δυνατότητες από εκείνες που μπορούν να ληφθούν υπόψη. Αυτό το πρόβλημα είναι τελικά το ίδιο για τη συγκρότηση θεωριών και για τα άλλα είδη δράσης. Συνδέει τις συνθήκες εργασίας του θεωρητικού με την κατάσταση εκείνου που θέλει ή θα έπρεπε να εφαρμόσει θεωρίες. Αυτό πρέπει λοιπόν να είναι και η βάση συνεννόησης μεταξύ τους» (Luhmann 1996: 442-443). Η πολυπλοκότητα της σύγχρονης επιστήμης συνεπάγεται (και επιτρέπει) επομένως πολλές και διαφορετικές θεωρητικές περιγραφές. Παραφράζοντας τον Clifford Geertz, θα λέγαμε ότι μία μόνο μεταφορά δεν μπορεί να κατανοήσει ούτε την κοινωνική ούτε την επιστημονική ζωή. Για τον παραπάνω λόγο, χρειάζεται να αναγνωρίσουμε, με αναστοχαστική ταπεινότητα, πως ζούμε σ’ έναν κόσμο που «είναι πολύ μεγαλύτερος απ’ ό,τι εμείς ως ανθρώπινα πλάσματα που κατέχουμε μια ορισμένη θέση μπορούμε να φανταστούμε» (Hayles 1998: 330), αλλά και πως είναι απαραίτητη η μόνιμη και ενεργητική συμμετοχή μας σε έναν ανοιχτό δημοκρατικό διάλογο σχετικά με τις «θέσεις, τις προδιαθέσεις και τις θεσιληψίες μας» (Vandenberghe 2002: 63). 2 Κατά τη διάρκεια του προηγούμενου αιώνα, ένας μεγάλος αριθμός φυσικοεπιστημονικών εξελίξεων προσέδωσε σημαντική ώθηση στην αναστοχαστική εννοιολογική σύλληψη της «πολυπλοκότητας»: η θεωρία των διασυνδεμένων πιθανοτήτων του A. Markov, η Γενική Θεωρία της Σχετικότητας του Α. Einstein, η Θεωρία της Αβεβαιότητας του W. Heisenberg, η κβαντική θεωρία του N. Bohr, η υπόθεση του νετρίνου του W. Pauli, η «θερμοδυναμική μακράν της ισορροπίας» (non-equilibrium thermodynamics) και η θεωρία των «καταστροφικών δομών» (dissipative structures) του I. Prigogine, κ.ά. 3 Πρώτιστης σημασίας είναι η ικανότητα των συστημάτων αυτών για προσαρμοστική μάθηση (adaptive learning). Η «μαθησιακή ικανότητα» τούς επιτρέπει να αναδιαρθρώνουν λειτουργικά τις ποικίλες ιδιότητές τους έτσι ώστε να αντιμετωπίζουν με ευελιξία και αποτελεσματικότητα τις συχνές αλλαγές του περιβάλλοντος.
