Human-Inspired Methods for Extending Advances in Computer Vision to Data- and Compute-Constrained Environments

dc.contributor.author	Laura E. Brandt
dc.date.accessioned	2026-06-15T06:41:35Z
dc.date.issued	2024
dc.description.abstract	Recent developments in computer vision have often relied on access to big data, powerful compute, or both. City-based systems, such as self-driving cars and airport checkpoints, have benefited greatly from these advances, so much so that automated cars and security checks are beginning see true deployment in modern society. In contrast, robots and autonomous systems in data- and compute-constrained environments, like remote wilderness regions or off-Earth, are still relying on pre-deep learning era computer vision algorithms. Robots in the most challenging of environments — and, correspondingly, the environments that require the highest level of autonomy for robots — have been left behind by modern computer vision.
dc.identifier.uri	https://demo.dspace.org/handle/10673/1327
dc.language.iso	en
dc.title	Human-Inspired Methods for Extending Advances in Computer Vision to Data- and Compute-Constrained Environments
dc.type	Thesis

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