Chromatographic Ballads [2013]

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The installation received a honorary mention at VIDA 15.0

The Artwork

Chromatographic Orchestra is an artistic installation which allows a visitor to direct a software framework with an EEG device. In an exhibition environment with semi-transparent video screens a visitor is sitting in an armchair and learns to navigate unconsciously – with his/her brain waves the parameter space of our software – Neurovision.

Neurovision interacts with live video footage of the location of the exhibition and its surroundings. By navigating with his/her own brain waves the visitor can define and navigate the degree of abstraction of a generative (machine learning) algorithm,  performed on the footage of different, nearby video cameras.

Lisa training the Interface

Lisa training the Interface

The installation refers back to painting techniques in the late 19th and early 20th century, when painting became more an analysis of the perception of a setting then a mere representation of the latter. Impressionism and Cubism were fragmenting the items of observation while the way of representation was given by the nature of the human sensory system.

The installation “chromatographic orchestra” does not apply arbitrary algorithms to the live footage: we developed a software – the Neurovision framework – which mimics the visual system of the human brain. Thus we question whether our algorithms meet the well being of the spectator by anticipating processing steps of our brain.

Artistic Motivation

How much complexity can our senses endure, or rather how could we make endurable what we see and hear? Many communication tools have been developed, to adjust human capabilities to the requirements of the ever more complex city.

Our installation poses the opposite question: How can information emerging from the city be adjusted to the capabilities of the human brain, so processing them is a pleasure to the eye and the mind?

At the core of our installation is the NeuroVision Sandbox, a custom made framework for generative video processing in the browser based on WebGL shaders.

Martin explaining the Neurovision software

Martin explaining the Neurovision software

Inside this Sandbox we developed several sketches, culminating in the
“Chromatographic Neural Network”, where both optical flow and color information of the scene are processed, inspired by information processing in the human visual system.

We critically assess the effect of our installation on the human sensory system:

  • Does it enhance our perception of the city in a meaningful way?
  • Can it and if so – how will it affect the semantic level of visual experience?
  • Will it create a symbiotic feedback loop with the visitor’s personal way to interpret a scene?
  • Will it enable alternate states of consciousness? Could it even allow visitors to experience the site in a sub-conscious state of “computer augmented clairvoyance”

Installation

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In a location close to the site a single visitor directs a video-presentation on a large screen with a setup we like to call “the Neural Chromatographic Orchestra” (NCO).
Our installation uses an EEG-Device (Emotiv NeuroHeadset) that lets visitors interact with a custom neural network. The setup allows visitors to navigate through various levels of abstraction by altering the parameters of the artificial neural net.

With the NCO device, a visitor can select and explore real-time views provided by three cameras located in public space with different perspectives on the passer-byes (birds-eye view and close-ups)

The installation is based on the NeuroVision Sandbox used in the development of “transits”.
Other than transits, chromatographic ballads uses multi-channel real-time video-input and enables a visitor to interact with irectly via biofeedback with the neural network.

The Neural Chromatographic Orchestra investigates how human perception reacts to the multifaceted visual impressions of public space via an artistic setting. Using an EEG-Device visitors can interact with a self-organizing neural network and explore real-time views of an adjacent hall from several perspectives and at various levels of abstraction.

Biological Motivation

The Chromatographic Neural Network is a GPU-based video processing tool. It was inspired by parallel information processing in the visual system of the human brain. Visual information processing inside the brain is a complex process involving various processing stages.The visual pathway includes the retina, the Lateral Geniculate Nucleus (LGN) and the visual cortex

Scheme of the optical tract with the image being processed (simplified): http://en.wikipedia.org/wiki/File:Lisa_analysis.png

Low-level visual processing is already active at the various layers of the retina. The Interconnection of neurons between retina layers, and the ability to retain information using storage or delayed feedback, allows for filtering the visual image in the space and time domain.

Both image filters and motion detection can easily be achieved by accumulating input from neurons in a local neighborhood, in a massively parallel way.

Our Chromatographic Neural Network uses this approach to cluster colors and to compute the visual flow (or retina flow ) from a video source. The resulting attraction-vectors and flow-vectors are used to transform the memory retained in the memory layer.

The visual output of the system directly corresponds to the state of the output layer of the neural network. The neural layers of the Chromatographic Neural Network, are connected to form a feedback loop. This giving rise to a kind of homeostatic-system that is structurally coupled to the visual input but develops its own dynamics over time.

The set-up

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main hall with passangers

A visitor enters the site – a highly frequented passage, a spacious hall or a public place. Two videocameras, mounted on a tripod, can be moved around at will.

Another camera observes the passer-byes – their transits and gatherings – from an elevated location. The video footage from this site is streamed into a neighboring room – the orchestra chamber of the Neural Chromatographic Orchestra.

Here one can see – in front of a a large video wall a monitor displaying the videos from the adjacent room and the “orchestra pit” – an armchair equipped with a touch device and a neuro-headset. The video wall, showing abstract interpretations of the site itsself, should ideally be visible both from the orchestra pit and from the large hall.

The Orchestra Chamber

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view from the “orchestra chamber”

Inside the chamber the visitor is seated in a comfortable armchair and an assistant helps her put on and adjust the neuro-headset.

The orchestra chamber should be isolated from the public area as much as possible. A sense of deprivation from outside stimuli allows the visitor to gain control over her own perception and achieve a state of mind similar to meditation or clairvoyance.

The Orchestral Performance

Training Cognitive Control

A performance with the Neural Chromatographic Orchestra starts with a training of up to six mental actions, corresponding to the “push/pull”, “left/right“ and “up/down” mental motions provided by the Emotiv Cognitiv suite. The training typically lasts 10 to 30 minutes.

Playing the Sandbox

After successful training the visitor is asked to sit in front of the NeuroVision Sandbox:

The visitor in the orchestra chamber has three modes of conducting the neural network

  • either the birds-eye view or one of the cameras that take a pedestrian’s perspective
  • A graphical user interface lets her switch between different neural networks and control their parameters
  • A menu lets her choose any of the three cameras as a video source:
  • the NeuroHeadset allows to navigate the parameter space of the selected neural network

http://perceptify.com/neurovision/

Conducting the Orchestra

Once the visitor feels comfortable conducting the NCO on the small screen, she can perform on the large screen, that is also visible from the outside.

On the public screen sliders are not shown, but the conductor may still use a tablet device to access the graphical user interface.

The current position in parameter spaces is represented by a 3d-cursor or wire-frame box, which is very helpful for making the transition from voluntary conduction moves, to a style of conducting that is more directly informed by immersion and interaction with the output of the Chromatographic Neural Network.

The Chromatographic Neural Network

The flow of information is arranged into several processing layers. To realize memory, each processing layer is in turn implemented as stack of one or more memory layers.This allows us to access the state of a neuron at a previous point in time.

Example

The video layer is made up of two layers, so the system can access the state of any input neuron at the current point in time, and its state in the previous cycle.

Processing Layers

 

The Video layer

The Video layer contains the input neurons. Each neuron corresponds to a pixel of the video source. The Video layer provides the input for the Flow layer.

The Ghost Layer

The Ghost layer represents a haunting image from the past. It implements the long term memory, that interferes and interacts with the current visual input. It does not change over time, and is provided as additional input to the Flow layer

 

The Flow layer

The Flow layer accumulates the input from the Video layer and the Ghost layer. Each Neuron aggregates input from its neighborhood in the Video Layer at times (t) and (t-1). The computed 2d vector is directly encoded into the the state of the neuron, creating a flow map.

The Blur layers

The Blur layers are used to blur the flow map. While the computation of visual flow is restricted to a very small neighborhood, the blur layer is needed to spread the flow information to a larger region, since flow can only be detected on the edge of motion.

For efficiency reasons the blur function is split into two layers, performing a vertical and a horizontal blur respectively.

 

Neuron Processing

The state of each neuron corresponds to an RGB color triplet. Every neuron of the Flow layer gets input from corresponding neurons inside a local neighborhood of the input layers. Each of those input samples corresponds to a single synapse. The vector from the center of the neuron towards the input neuron is referred to as the synapse vector.

Color Attraction

To achieve some kind of color dynamics, colors that are close in color space are supposed to attract each other.

The distance between synapse input and the neuron state in RGB color-space, serves as a weight, which is used to scale the synapse vector. The sum of scaled synapse vectors results in a single color attraction vector.

 

Color Flow

While color attraction is the result of color similarities or differences in space, color flow is the result of a color changes over time. Rather than calculating the distance of the neuron state to a single synapse input, its temporal derivative is calculated by using input from a neuron and its corresponding memory neuron. This time the sum of scaled synapse vectors results in a flow vector.

Encoding

Both color flow and color attraction vectors are added up and their components are encoded in the flow layer.

Parameters

here are various parameters in each layer controlling the amount and direction of color attraction, color flow, the metrics used for calculating color distances, the neuron neighborhood, etc …

Implementation

All neural computation is performed on the GPU using OpenGL and GLSL shaders. This is the mapping from neural metaphors to OpenGL implementation:

Memory layers → Texture-Buffers
Processing Layers → GLSL shaders
Parameters → GLSL uniforms

Outlook

In our implementation both color flow and attraction are integrated into a single level flow map. While this generates interesting local interactions, there is little organization on a global level. The work on Multilevel Turing Patterns as popularized by Jonathan McCabe shows that it is possible to obtain complex and visually interesting self organizing patterns without any kind of video input.

Our future research will combine several layers of flow maps, each operating on a different level of detail. Additional directions include alternate color spaces and distance metrics.
In the current model input values are mixed and blurred, resulting in a loss of information over time. We have also been experimenting with entropy-conserving models and are planning to further investigate this direction.

This project is based on two recent artworks, “transits” and “IseethereforeIam”