ArticleProceedings of the National Academy of Sciences of the United States of America2022
A topological solution to object segmentation and tracking.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Ecological Resonance Is Reflected in Human Brain Activity.Psychophysiology · 2025Article
- Orthogonal neural representations support perceptual judgments of natural stimuli.Scientific reports · 2025Article
- Is the impact of spontaneous movements on early visual cortex species specific?Trends in neurosciences · 2025Review
- Figure-ground segmentation based on motion in the archerfish.Animal cognition · 2024Article
- Article
- New Approaches to 3D Vision.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
- The reasonable effectiveness of contours in vision.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- A topological solution to object segmentation and tracking.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
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2 authors.
Funding
Abstract
The world is composed of objects, the ground, and the sky. Visual perception of objects requires solving two fundamental challenges: 1) segmenting visual input into discrete units and 2) tracking identities of these units despite appearance changes due to object deformation, changing perspective, and dynamic occlusion. Current computer vision approaches to segmentation and tracking that approach human performance all require learning, raising the question, Can objects be segmented and tracked without learning? Here, we show that the mathematical structure of light rays reflected from environment surfaces yields a natural representation of persistent surfaces, and this surface representation provides a solution to both the segmentation and tracking problems. We describe how to generate this surface representation from continuous visual input and demonstrate that our approach can segment and invariantly track objects in cluttered synthetic video despite severe appearance changes, without requiring learning.
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