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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
An interdisciplinary course that examines, thorugh case studies, the state of the photographic image, its history, the theoretical, conceptual, and philosophical underpinnings. The course bridges studio arts, engineering, and humanities. This course may be of interest to artists, humanities researchers or programmers as there are three directions to explore:
  1. The aesthetic creative applications, opportunities and constraints,

  2. The computational processes from image processing to machine-learning, and

  3. 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.


Course Workload

Attendance and participation at zoom lectures
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



10/06

10/08


Apparatus Fundamentals

Photographic History Selection (1830-1990)



10/13

10/15


Raster/Pixel Digital Photographic Explorations (1960-1990)

Image Processing Fundamentals

10/20

10/22


Material, Machine-Generated Images

Data, Signal & Noise/Glitch


10/27

10/29


Volumetric Data Points, Photogrammetry

Computational photography


11/03

11/05


Computational Aesthetics

Generative Art

11/10

11/12


Vision Science & Neuro-Aesthetics

Machine-Learning, CNN, Deep Learning


11/17

11/19


Aesthetic Explorations of ML, CNN, DL, GANS

Aesthetic Explorations of ML, CNN, DL, GANS

11/24

11/26


Various Deep Fakes, Social Implications

Project Proposals Presentations

12/01

12/03


Project Development

Project Development

12/08

12/10


Final Presentation & Reporting

Final Presentation & Reporting