GRASSMANNIAN DIMENSIONALITY REDUCTION USING TRIPLET MARGIN LOSS FOR UME CLASSIFICATION OF 3D POINT CLOUDS

Yuval Haitman, Joseph M. Francos, Louis L. Scharf

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We consider the problem of classifying 3-D objects undergoing rigid transformations. It has been shown that the rigid transformation universal manifold embedding (RTUME) provides a mapping from the orbit of observations on some object to a single low-dimensional linear subspace of Euclidean space. This linear subspace is invariant to the geometric transformations. In the classification problem the RTUME subspace extracted from an experimental observation is tested against a set of subspaces representing the different object manifolds, in search for the nearest class. We elaborate on the design problem of the RTUME operator in the case where the point cloud sampled from the object is sparse, noisy, and non-uniformly sampled. By introducing metric learning and negative-mining techniques into the framework of Grassmannian dimensionality reduction for universal manifold embedding, we improve classification performance for these challenging sampling conditions.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8982-8986
Number of pages5
ISBN (Electronic)9781665405409
DOIs
StatePublished - 1 Jan 2022
Event47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore
Duration: 23 May 202227 May 2022

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2022-May
ISSN (Print)1520-6149

Conference

Conference47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Country/TerritorySingapore
CityVirtual, Online
Period23/05/2227/05/22

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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