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In the era of big data and multi-connectivity via IoTs, protecting and securing multimedia data has become a real necessity and priority for organizations and businesses, but this can be a rather difficult task due to the heterogeneous nature of platforms and data sets. It is therefore essential to improve the security level of multimedia information by developing core technologies to prevent the loss and damage of information during processing and transmission.

This book explores the cultural and ethical implications of generative AI through the lens of Indigenous visual culture in Latin America. 

Responding to the rise of tools like DALL·E 2 and Midjourney, this groundbreaking volume documents the AIAI project (2023–), which invited a group of Indigenous artists and writers from Argentina, Bolivia, Brazil, Chile, and Peru to critically engage with image generation technologies. Through collaborative experimentation with a range of both commercial and bespoke tools, contributors reflect on the promises and pitfalls of AI—from its potential for ‘dreaming,’ for imagining Indigenous futures, to the frustration of its ability to ‘hallucinate,’ to distort, and misrepresent Indigenous cultures.

Bringing together artists, writers, and scholars, the book challenges dominant narratives around AI and representation, using a decolonial framework rooted in Indigenous epistemologies. Structured in three parts—context and theory, creative reflections, and generative critique—it foregrounds multivocal and multilingual perspectives, resisting homogenization and reframing participatory research. This is the first book-length study of generative AI’s impact on Indigenous visual culture in Latin America, making a timely and vital contribution to global debates on AI, art, and cultural sovereignty.

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.

Medical images, in various formats, are used by clinicians to identify abnormalities or markers associated with certain conditions, such as cancers, diseases, abnormalities or other adverse health conditions. Deep learning algorithms use vast volumes of data to train the computer to recognise certain features in the images that are associated with the disease or condition that you wish to identify.

This two-volume book set explores how sensors and computer vision technologies are used for the navigation, control, stability, reliability, guidance, fault detection, self-maintenance, strategic re-planning and reconfiguration of unmanned aircraft systems (UAS).

This two-volume book set explores how sensors and computer vision technologies are used for the navigation, control, stability, reliability, guidance, fault detection, self-maintenance, strategic re-planning and reconfiguration of unmanned aircraft systems (UAS).

There is a growing interest in the development and deployment of surveillance systems in public and private locations. Conventional approaches rely on the installation of wide area CCTV (Closed Circuit Television), but the explosion in the numbers of cameras that have to be monitored, the increasing costs of providing monitoring personnel and the limitations that humans have to maintain sustained levels of concentration severely limit the effectiveness of these systems. Advances in information and communication technologies, such as computer vision for face recognition and human behaviour analysis, digital annotation and storage of video, transmission of video/audio streams over wired and wireless networks, can potentially provide significant improvements in this field.

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.

The result of the author's extensive practical experience: a decade in computer process control using large scale systems, another decade in machine pattern-recognition for vision systems, and nearly a decade dealing with artificial intelligence and expert systems. These real-life projects have taught Vámos a critical appreciation of, and respect for, both abstract theory and the practical methodology that grows out of—and, in turn, shapes—those theories.Machine representation means a level of formalization that can be expressed by the instruments of mathematics, whereas programming is not more and not less than a special linguistic translation of these mathematical formulae. How these all are related and controlled is a most practical philosophical and computation professional task. Wide experience in the practical fields of computer science, and the research of the underlying theoretical issues have led Vámos to the development of the attitude and activity of constructive skepticism.

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.

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.