arXiv (All)
open access
Granulometric Smoothing on Manifolds
Given a random sample from a density function supported on a manifold $M$, a new method for the estimating highest density regions of the underlying population is introduced. The new proposal is based on the empirical version of the opening operator from mathematical morphology combined with a preli
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Improving Kernel Density Estimation Near the Boundary#411893
- DP-Splat: Bayesian Nonparametric Complexity Control for Gaussian Splatting#1052231
- The Bayesian SIAC filter#820248
- Improving clustering quality evaluation in noisy Gaussian mixtures#144258
- Uncertainty-aware geometry processing on Gaussian Process Implicit Surfaces#147666
Record · ID 1201835
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive