Multi-Class Categorization of Three-Dimensional (3-D) Objects for Digital Holographic Information Using Deep Learning

Uma Mahesh R N, Yogesh N

2025

Abstract

In this paper, n-class (n=3) categorization of three-dimensional (3-D) objects using digital holographic data has been achieved with a deep learning network. For n=3 categories, the 3-D object “triangle-square” is assigned to category 1, the 3-D object “circle-square” to category 2, and the 3-D objects “triangle-circle” and “square-triangle” are grouped into category 3. The dataset, comprising phase-only images derived from digital holographic data, was generated using the phase-shifting digital holography (PSDH) technique. It includes 2880 images created through the application of a rotation invariance method. The deep learning network was trained on the dataset to generate the output. The results, including the n-class (n=3) error matrix, receiver operating characteristic (ROC), and positive predictive value (PPV)–true positive rate (TPR) characteristic are presented to validate the work.

Paper Citation

in Harvard Style

Mahesh R N U. and N Y. (2025). Multi-Class Categorization of Three-Dimensional (3-D) Objects for Digital Holographic Information Using Deep Learning. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 384-388. DOI: 10.5220/0013592800004664

in Bibtex Style

@conference{incoft25,
author={Uma Mahesh R N and Yogesh N},
title={Multi-Class Categorization of Three-Dimensional (3-D) Objects for Digital Holographic Information Using Deep Learning},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={384-388},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013592800004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Multi-Class Categorization of Three-Dimensional (3-D) Objects for Digital Holographic Information Using Deep Learning
SN - 978-989-758-763-4
AU - Mahesh R N U.
AU - N Y.
PY - 2025
SP - 384
EP - 388
DO - 10.5220/0013592800004664
PB - SciTePress