Target Recognition Based on Singular Value Decomposition in a Single-Pixel Non-Imaging System

We propose a single-pixel non-imaging target recognition scheme which that exploits the singular values of target objects. By choosing the first few singular values and the corresponding unitary matrices in the singular value decomposition of all the targets, we form the measurement matrices to be projected onto the target in a single-pixel non-imaging scheme. One can quickly and accurately recognize the target images after directly recording the single-pixel signals. From the simulation and experimental results, we found that the accuracy of target recognition was high when the first three singular values were used. The efficiency of target recognition was improved by randomly rearranging the orders of the row vectors in the measurement matrix. Therefore, our research results offer a novel perspective for recognizing non-imaging targets.

Authors

Institutions

Publication Details

Journal
Photonics
Published
2024-09-27
DOI
https://doi.org/10.3390/photonics11100909
Citations
4
Primary Topic
Random lasers and scattering media
Type
article
Field-Weighted Citation Impact
1.11
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Target Recognition Based on Singular Value Decomposition in a Single-Pixel Non-Imaging System

De-Zhong Cao, Lin-Shan Chen, Yining Zhao, Cheng Ren et al.
4 citations
Photonics
Random lasers and scattering media
1.11
article

Target Recognition Based on Singular Value Decomposition in a Single-Pixel Non-Imaging System

De-Zhong Cao, Lin-Shan Chen, Yining Zhao, Cheng Ren, Chong Wang
article en
4 citations

Abstract

We propose a single-pixel non-imaging target recognition scheme which that exploits the singular values of target objects. By choosing the first few singular values and the corresponding unitary matrices in the singular value decomposition of all the targets, we form the measurement matrices to be projected onto the target in a single-pixel non-imaging scheme. One can quickly and accurately recognize the target images after directly recording the single-pixel signals. From the simulation and experimental results, we found that the accuracy of target recognition was high when the first three singular values were used. The efficiency of target recognition was improved by randomly rearranging the orders of the row vectors in the measurement matrix. Therefore, our research results offer a novel perspective for recognizing non-imaging targets.

PhotonicsVol. 11(10)
Yantai University (CN)
Openalex Percentile: Top 26%
Random lasers and scattering media
1.11
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.