On a realization of motion and similarity group equivalence classes of labeled points in $\mathbb R^k$ with applications to computer vision
arXiv:2103.12980v1
AI Analysis
This addresses a theoretical problem in computer vision for researchers, but appears incremental as it builds on existing group equivalence concepts.
The paper tackles the problem of representing equivalence classes of labeled points under motion and similarity groups in Euclidean space as a metric space with a computable metric, motivated by applications in computer vision, but no concrete results or numbers are provided.
We study a realization of motion and similarity group equivalence classes of $n\geq 1$ labeled points in $\mathbb R^k,\, k\geq 1$ as a metric space with a computable metric. Our study is motivated by applications in computer vision.