You go to the airport and your picture is taken at the security checkpoint. What is the imaging software doing and how? What features of your face are being scanned and how are they being matched against a database? Can you conceal your identity through expressions? Make-up? How did we get here?
The design of artificial vision involves some analysis of human cognition/perception and considerable processing of visual images and representations. Automated systems capable of “machine vision” are already being integrated into our lives. This SDG will look at the theoretical issues, historical development, and critical ideas at play in the current research in this field. Since many of these issues touch directly on our daily experience, the discussions will have relevance to ongoing activities.
This SDG looks at the development of computer vision (the capacity for machines to “see”) through a combination of materials. The core book, Jill Walker Rettberg, Machine Vision: How Algorithms are Changing the Way We See the World. (Polity Press, 2023), will be supplemented by readings freely available online. These will address theories of vision, models of vision and cognition, and some of the ways computer scientists have designed systems capable of artificial vision. Rettberg’s book is lively and information-rich book, but also filled with ideas that touch on the integration of artificial systems into our daily lives. The author discusses the ways automated visual technology is changing our understanding of identity, security, and the visible world. This book is a cultural investigation of the changes that innovations in technology, specifically algorithms relating to visual images, impact our activities.
In her first chapter, she offers a succinct history of optical technology—beginning with the oldest mirror, a surface of polished black obsidian which may date to 6000 BCE, ground glass lenses used in antiquity for magnification, and other extensions of human visual capacity from light-based to infrared cameras.
The central argument of the book is that technology cannot be examined without attention to the situations and circumstances of its use. She terms these relations between technologies and users “assemblages.” To illustrate this concept, she describes the difference between attitudes and effects of surveillance systems in the urban United States and in small-town Norway.
The book is organized in five chapters, that focus on different aspects of visual experience—Seeing More, Seeing, Differently, Seeing Everything, Being Seen and a Conclusion. The book is much more about ideas, cultural patterns, and experience than it is about technology and the questions it raises about how we understand ourselves and the world through digitally mediated images. Who doesn’t have an opinion about selfies and the algorithms that adjust, without our realizing it, the proportions of our features? Or have thoughts about automated shopping that uses facial recognition? Or the ethics and dangers of surveillance cameras weighed against their benefits?
Professor of Digital Culture at University of Bergen in Norway, Rettberg is a clear writer whose prose is packed with interesting ideas and references. I know Jill Rettberg and would invite her to join one of our sessions which I think will add to the overall experience. Rettberg’s book is relatively new and has received little critical response, but that is not unusual for academic books. Link to Amazon: https://www.amazon.com/Machine-Vision-Algorithms-Changing-World/dp/1509545239
Format: This is a 10-week SDG, hybrid, meeting on Mondays 1-3.
Week 1: Introduction (1-24)
Week 2: Chapter 1 Seeing More (part 1), (25-40) and Computer Vision
IBM scientists https://www.ibm.com/think/topics/computer-vision,
David Marr, https://www.sciencedirect.com/science/article/pii/S0960982207011396 or this succinct summary: https://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/GOMES1/marr.html
Week 3: Chapter 1 Seeing More (part 2), (40-59) and More about Computer Vision
OpenCV.org https://opencv.org/blog/what-is-computer-vision/,
ImageNet: https://image-net.org/about.php
Week 4: Chapter 2 Seeing Differently (part 1) (60-71) and Is Vision Historical?
Bence Nanay, “The History of Vision,” Aesthetics as Philosophy of Perception, (2016)
https://academic.oup.com/book/3110/chapter/143937243?login=false
Week 5: Chapter 2 Seeing Differently (part 2) (72-82) and Eyes and Perception
Neuro Launch: Eye and Brain, https://neurolaunch.com/brain-eye/
https://web.stanford.edu/class/history13/earlysciencelab/body/eyespages/eye.html
Week 6: Chapter 3 Seeing Everything (part 1) (83-101) and Eyes and Brain
Kaia Glickman, Computers are Getting Much Better at Image Recognition https://www.smithsonianmag.com/innovation/computers-are-getting-much-better-at-image-recognition-180987614/
Trinh Nguyen, Image Recognition Applications: The Basics and Use Cases https://www.neurond.com/blog/image-recognition-applications
Week 7: Chapter 3 Seeing Everything (part 1) (101-115) and More Eyes and Brain
Pure Optical: https://pureoptical.com/blog/how-eyes-connect-to-the-brain/
Aditi Babu, https://medium.com/@aditib259/from-pixels-to-predictions-how-computer-vision-models-understand-images-0de26dac32a9
Week 8: Chapter 4 Being Seen, (part 1) (117-127) and Facial Recognition
AnyConnectAcademy: https://anyconnect.com/blog/the-history-of-facial-recognition-technologies
Week 9: Chapter 4 Being Seen, (part 2) (127-142) and History of Facial Recognition
Issues in facial recognition: https://www.geeksforgeeks.org/blogs/problems-in-facial-recognition/
Ethics of facial recogntino: https://learn.g2.com/ethics-of-facial-recognition
Week 10: Chapter 5, and Conclusion (143-160) and the Bias Issue
Bias detection: https://viso.ai/computer-vision/bias-detection/
ACLU, https://www.aclu.org/news/privacy-technology/machine-surveillance-is-being-super-charged-by-large-ai-models
Bonus: Ralitsa Golemanova, Image Recognition Trends for 2025 and Beyond https://imagga.com/blog/the-future-of-image-recognition-trends-for-2025-and-beyond/
Jill Rettberg, Machine Vision: How Algorithms are Changing the Way We See the World. (Polity Press, 2023)
Additional weekly readings will include articles published by IBM scientists https://www.ibm.com/think/topics/computer-vision, OpenCV.org https://opencv.org/blog/what-is-computer-vision/, Stanford University History of Early Science https://web.stanford.edu/class/history13/earlysciencelab/body/eyespages/eye.html sites, and many others. These short but authoritative pieces will provide information about current research as well as some of the fascinating issues in the history and theory of vision.