A revealing genealogy of image-recognition techniques and technologies
 

Today’s most advanced neural networks and sophisticated image-analysis methods come from 1950s and ’60s Cold War culture—and many biases and ways of understanding the world from that era persist along with them. Aerial surveillance and reconnaissance shaped all of the technologies that we now refer to as computer vision, including facial recognition. The Birth of Computer Vision uncovers these histories and finds connections between the algorithms, people, and politics at the core of automating perception today.

James E. Dobson reveals how new forms of computerized surveillance systems, high-tech policing, and automated decision-making systems have become entangled, functioning together as a new technological apparatus of social control. Tracing the development of a series of important computer-vision algorithms, he uncovers the ideas, worrisome military origins, and lingering goals reproduced within the code and the products based on it, examining how they became linked to one another and repurposed for domestic and commercial uses. Dobson includes analysis of the Shakey Project, which produced the first semi-autonomous robot, and the impact of student protest in the early 1970s at Stanford University, as well as recovering the computer vision–related aspects of Frank Rosenblatt’s Perceptron as the crucial link between machine learning and computer vision.

Motivated by the ongoing use of these major algorithms and methods, The Birth of Computer Vision chronicles the foundations of computer vision and artificial intelligence, its major transformations, and the questionable legacy of its origins.


Cover alt text: Two overlapping circles in cream and violet, with black background. Top is a printed circuit with camera eye; below a person at a 1977 computer.

Computer vision is an interdisciplinary scientific field that deals with how computers obtain, store, interpret and understand digital images or videos using artificial intelligence based on neural networks, machine learning and deep learning methodologies. They are used in countless applications such as image retrieval and classification, driving and transport monitoring, medical diagnostics and aerial monitoring.

An in-depth examination of how computer vision in AI is indebted to artistic ideals from the Enlightenment

When Dutch megabank ING launched a 2016 advertising campaign spearheaded by an AI-generated “next Rembrandt,” it demonstrated just how deeply Western assumptions about the history of art had infiltrated fledgling artificial intelligence technologies. Working backward from this computer-generated Old Master painting, Elizabeth C. Mansfield traces the history of computer vision (the branch of AI research concerned with visual perception, recognition, and design) back to Enlightenment fantasies of intelligent machines and artistic automata.

Through her inquiry in eighteenth-century European fascination with art-making machines, Mansfield connects centuries-old automata to contemporary AI systems like DALL-E and Midjourney, showing how the history of art abides as a ghost in the machine of computer vision. She documents the artistic ambitions, aesthetic experiments, commercial pressures, and crises of authenticity that occur during the development of a technology designed to emulate aspects of human visual perception.

An innovative art historical response to computer vision, Ground Truth offers important new directions for a renewed engagement with art’s humanist and digital generation history. At a time when the humanist enterprise is being questioned, Mansfield makes the different worlds of art history and computer vision mutually intelligible in this compelling account of both.

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Intelligent multimedia involves the computer processing and understanding of perceptual input from speech, text, videos and images. Reacting to these inputs is complex and involves research from engineering, computer science and cognitive science. Intelligent multimedia processing deals with the analysis of images and videos to extract useful information for numerous applications including medical imaging, robotics, remote sensing, autonomous driving, AR/VR, law enforcement, biometrics, multimedia enhancement and reconstruction, agriculture, and security. Intelligent multimedia processing and computer vision have seen an upsurge over the last few years. With the increasing use of intelligent multimedia processing techniques in various sectors, the requirement for fast and reliable techniques to analyse and process multimedia content is increasing day by day.

Medical images can highlight differences between healthy tissue and unhealthy tissue and these images can then be assessed by a healthcare professional to identify the stage and spread of a disease so a treatment path can be established. With machine learning techniques becoming more prevalent in healthcare, algorithms can be trained to identify healthy or unhealthy tissues and quickly differentiate between the two. Statistical models can be used to process numerous images of the same type in a fraction of the time it would take a human to assess the same quantity, saving time and money in aiding practitioners in their assessment.