Object Recognition using Sparse, Reduced-Dimension Point Cloud Data

Edward Watson

doi:10.1364/lsc.2022.lstu4c.3

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JOURNAL ARTICLE

Object Recognition using Sparse, Reduced-Dimension Point Cloud Data

Edward Watson

Year: 2022 Journal: Optica Advanced Photonics Congress 2022 Pages: LsTu4C.3-LsTu4C.3

DOI: 10.1364/lsc.2022.lstu4c.3

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Abstract

We use sparse point clouds to develop a reduced-dimension feature for pattern recognition. Using this feature, templates generated from random realization are developed and show reliable discrimination between two objects using a log-likelihood ratio measure.

Keywords:

Point cloud Dimension (graph theory) Computer science Artificial intelligence Pattern recognition (psychology) Feature (linguistics) Cognitive neuroscience of visual object recognition Measure (data warehouse) Point (geometry) Object (grammar) Realization (probability) 3D single-object recognition Feature extraction Computer vision Template Data mining Mathematics Statistics

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3D Shape Modeling and Analysis

Physical Sciences → Engineering → Computational Mechanics

Remote Sensing and LiDAR Applications

Physical Sciences → Environmental Science → Environmental Engineering

Computer Graphics and Visualization Techniques

Physical Sciences → Computer Science → Computer Graphics and Computer-Aided Design

Object Recognition using Sparse, Reduced-Dimension Point Cloud Data

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