2020f
Instructor
MAT594G Techniques, History & Aesthetics of the Computational Photographic Image
George Legrady | http://vislab.mat.ucsb.edu
Course materials are protected by US Copyright laws and by University policy. Contents of this course may not be reproduced, distributed, or displayed without my express prior written consent.
Elings Hall, lab 2611, CNSI Building 2nd floor - Tues-Thurs 1-2:50pm
Course Information
- The aesthetic creative applications, opportunities and constraints,
- The computational processes from image processing to machine-learning, and
- The social considerations – to what degree can we believe in the image given the considerable software-based processing and autonomous image detail generation we are seeing through machine-learning processes.
The end goal is to investigate the photograph’s transformation through weekly presentations of projects, methods and discussion leading up to the impact of machine-learning on the creative process resulting in computational generated artworks.
Weekly contribution to course journal at Student Forum | Legal agreement
Final presentation pdf documentation of either a research paper OR project
10/01
Course Overview, Introductions
10/08
An overview of the optical-mechanical image capture machine
Photographic History Selection (1830-1990)
A range of explorations from documentary, pictorialism, composite assembly, photograms, formal composition studies, etc.
10/15
The image as a 2D matrix data structure consisting of numerically assignable pixels
Image Processing Fundamentals
The manipulation of the image through mathematical filtering
10/22
Examples of the artistic application of the analog/chemical based materiality of the photograph and electronic delivery systems such as screens
Data, Signal & Noise/Glitch
Data for artistic exploration, Information Theory’s noise and signal, randomness, Brownian motion
10/29
Motion and depth sensing laser-based devices, artistic applications of photogrammetry image stitching (Weidi Zhang presentation)
Computational photography
The transformed camera through computers embedded within it
11/05
An engineer’s approach to understanding and quantifying the rules of aesthetic processes
Generative Art
Rule-based artistic explorations
11/12
Human/Animation visual perception and how it determines what we see and the design of the machines by which we see and capture images
Machine-Learning, CNN, Deep Learning
An introduction to machine-learning, convolutional neural networks (Weihao Qiu presentation)
11/19
An overview of some artistic applications of machine-learning
Fabian Offert Presentation
Guest speakers Fabian Offert lecture – with a Humanities, critical perspective on image classification and machine-learning applications, followed by an overview by Mert Toka of the Xavier Snelgrove’s texture neural synthesis demo software
11/26
Humanities perspectives, news perspectives, use in arts and entertainment and business, applications of Deep Fakes
THANKSGIVING
12/03
Concept Presentation
12/10
Final Class Presentation (Reports due December 15, 2020)
Chad Ress
Katie Parker
Mert Toka
Weidi Zhang
Weihao Qiu
Yichen Li
Ten Thousand faces
Computationally Recognize the Subconscious
Deep Reflection
Lebrun
Image Representation with CNN
A visualization of how Google Maps constructs one’s subjectivity through automated decision making (ADM) in its map labels