マシン・ビジョン:アルゴリズムが変える世界の見え方<br>Machine Vision : How Algorithms are Changing the Way We See the World

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  • 電子書籍

マシン・ビジョン:アルゴリズムが変える世界の見え方
Machine Vision : How Algorithms are Changing the Way We See the World

  • 著者名:Rettberg, Jill Walker
  • 価格 ¥3,072 (本体¥2,793)
  • Polity(2023/09/11発売)
  • ポイント 27pt (実際に付与されるポイントはご注文内容確認画面でご確認下さい)
  • 言語:ENG
  • ISBN:9781509545223
  • eISBN:9781509545247

ファイル: /

Description

Humans have used technology to expand our limited vision for millennia, from the invention of the stone mirror 8,000 years ago to the latest developments in facial recognition and augmented reality. We imagine that technologies will allow us to see more, to see differently and even to see everything. But each of these new ways of seeing carries its own blind spots.

In this illuminating book, Jill Walker Rettberg examines the long history of machine vision. Providing an overview of the historical and contemporary uses of machine vision, she unpacks how technologies such as smart surveillance cameras and TikTok filters are changing the way we see the world and one another. By analysing fictional and real-world examples, including art, video games and science fiction, the book shows how machine vision can have very different cultural impacts, fostering both sympathy and community as well as anxiety and fear.

Combining ethnographic and critical media studies approaches alongside personal reflections, Machine Vision is an engaging and eye-opening read. It is suitable for students and scholars of digital media studies, science and technology studies, visual studies, digital art and science fiction, as well as for general readers interested in the impact of new technologies on society.

Table of Contents

Acknowledgements

Introduction

Chapter 1: Seeing More: Histories of Augmenting Human Vision

Chapter 2: Seeing Differently: Exploring Nonhuman Vision

Chapter 3: Seeing everything: surveillance and the desire for objectivity and security

Chapter 4: Being seen: The Algorithmic Gaze

Chapter 5: Seeing Less: The Blind Spots of Machine Vision

Conclusion: Hope

Notes

References

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