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Five Learnings from 15 Years in Perception
Tangram Vision co-founder Adam Rodnitzky explores five ways that computer vision and perception have changed in fifteen years.
While this isn’t necessarily a bad thing, it is important for those in both academia and industry to recognize that those core areas of perception and computer vision that were once popular still aren’t entirely solved, and there are rich veins of opportunity for curious engineers and researchers who want to move the field forward. Examples that spring immediately to mind include feature detection along self-similar surfaces (a very tricky problem remaining for autonomous warehouse robots, for instance), or developing better algorithms for high dynamic range sensing in extreme lighting conditions (super important for agriculture automation). Perception and computer vision now find themselves as central participants in diverse industries that touch every aspect of the human experience: medicine (analysis of medical imaging), finance (visual interpretation of financial chart movements), defense (autonomous drones), food production (vision-powered weeding, fertilizing, and harvesting), transit (biometric passports), and the list goes on and on.
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