Abstract
Scientific studies have contributed, over the last years, to an expansion of the Image concept, in articulation with new developments in Computational Media, based in a stratification around technical digital properties, which frame its existence to the form of digital information – extending it beyond a visual surface idea. Biometric data, artificial intelligence, bitcoin, glitches or machine learning are examples of instantiation tools used by artists to explore elements of mediation included in Post-images. This chapter addresses today's perspectives in Contemporary Imagetics emerging from the field of Digital Media Art (DMA), curating contributions from classic postproduction techniques to computational media instantiations and contextualizing imagery creation practice in DMA.
TopOn Imagetics
We start by analyzing the technical/computational conditions underlying the emergence of these image artworks. The scope of this first survey was to integrate artworks and their associated technology in a way that could benefit an historical perspective. First experiments in electronic imagery can be traced to 1946, after the creation of the Electronic Numerical Integrator and Computer (ENIAC), at the University of Pennsylvania. The first research developments in the use of electronics to generate images can be attributed to Laposky, in the 1950s. Ben F. Laposky developed a cathode ray device that created a visual record of the electrical current in a fluorescent screen. This device was called Oscilloscope. Laposky used it in the generation of abstract visual art (“electrical compositions”). One of the first electronic representations was made by Russel A. Kirsch, in 1950, using a scanner device designed by him and his colleagues at the National Bureau of Standards. The representation was materialized using an oscilloscope (Mitchell, 1992: 4). Years later, in 1966, Leon Harmon and Ken Knowlton created “Study in perception”, a nude figure that used 8 frames of 35mm microfilm (Figure 1).
Key Terms in this Chapter
Glitch: A temporary fault in a computer system; interference; for a brief moment a system displays visual errors in screen; some examples of “glitch” are distorted screen or noise effects.
Neural Network: Artificial network composed of nodes.
Machine Learning: Computer algorithms that use data to train and automatically improve over experience.
Generative Adversarial Networks (GAN): Machine learning framework in which two neural networks compete against each other to win within a gaming environment using a supervised learning pattern.
Face Recognition: Technology that allows to identify people in a video frame through the analysis of facial shape or texture.
Borderline Tools: Those set in an experimental context, belonging to unconsolidated fields, or having been forgotten or considered less interesting by mainstream sectors. This notion derives from previous work with digital tools and reflects the need to look at pathways of technologies that we assume as unquestionable, but derive from an overwhelming commercial or industrial process that leaves behind technical instances because of strategic options.
Morphing Algorithms: Technology to transform a visual object into another, used for instance to change an image of a person into another through digital techniques based in image dissolving.
Swarm Algorithms: Computer systems that are inspired by collective intelligence of nature: the collective patterns of movement in species, like fish, birds, ants, flies could be examples.
Structural Expressive Configuration (SEC): A framework to explore remediation mechanisms in Digital Media Art, derived from Arnheim’s “Expression embedded in the structure” ( Arnheim, 2004 , p. 449). This framework considers expressive elements from the process of creation in the digital medium. It explores poetic elements that emerge from the medium or that were placed in it through circumstances specific to the creative involvement with the medium.
Artificial Intelligence: A type of intelligence displayed by machines, in which a process of mimicking human thought attributes, like learning or solving problems.
Eye Tracking: Technology to measure eye gaze as a person looks at a screen.
Bitcoin Technology: A digital currency that doesn’t have a physical center or location, but instead is based in a peer-to-peer network.
Biometric Data: Information data from the human body, such as height, fingerprints, body measurements, temperature, etc.
Cybersecurity Metrics: Data produced by computer security institutions in the process of protecting networks of computers from external attacks.