Learning from Nature's Cameras: Bio-Inspired Computational Imaging

For the past half-billion years, evolution has produced a diversity of eyes and brains that work together to solve visual problems with remarkable efficiency and robustness. By reverse-engineering these systems, we can uncover powerful principles to build the next generation of computational cameras.

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Publication Details

Journal
XRDS Crossroads The ACM Magazine for Students
Published
2024-12-01
DOI
https://doi.org/10.1145/3703402
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
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article

Learning from Nature's Cameras: Bio-Inspired Computational Imaging

E. Alexander
XRDS Crossroads The ACM Magazine for Students
Cell Image Analysis Techniques
article

Learning from Nature's Cameras: Bio-Inspired Computational Imaging

E. Alexander
article en

Abstract

For the past half-billion years, evolution has produced a diversity of eyes and brains that work together to solve visual problems with remarkable efficiency and robustness. By reverse-engineering these systems, we can uncover powerful principles to build the next generation of computational cameras.

XRDS Crossroads The ACM Magazine for StudentsVol. 31(2)
McCormick (United States) (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 84%
Cell Image Analysis Techniques
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Learning from Nature's Cameras: Bio-Inspired Computational Imaging — E. Alexander · XRDS Crossroads The ACM Magazine for Students (2024) | TGRS Research Map | TGRS