Changed Detection Based on Patch Robust Principal Component Analysis
Abstract
:1. Introduction
2. Preprocessing
2.1. Intensity Correction
2.2. Linear Interpolation
3. Methodology
3.1. LowRank Decomposition Modeling
3.2. Patch LowRank Decomposition Modeling
3.3. Algorithm and Flow Chart
Algorithm 1: Algorithm procedure of PRPCA 
Input: A registered pair of fundus image ${I}_{1}$ and ${I}_{2}$; Output: Change detection of ${I}_{2}$ according to ${I}_{1}$

4. Experiments and Discussion
4.1. Data
4.2. Validation Measurement
4.3. Results with PRPCA Method
4.4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Zhu, W.; Zhang, Z.; Zhao, X.; Fu, Y. Changed Detection Based on Patch Robust Principal Component Analysis. Appl. Sci. 2022, 12, 7713. https://doi.org/10.3390/app12157713
Zhu W, Zhang Z, Zhao X, Fu Y. Changed Detection Based on Patch Robust Principal Component Analysis. Applied Sciences. 2022; 12(15):7713. https://doi.org/10.3390/app12157713
Chicago/Turabian StyleZhu, Wenqi, Zili Zhang, Xing Zhao, and Yinghua Fu. 2022. "Changed Detection Based on Patch Robust Principal Component Analysis" Applied Sciences 12, no. 15: 7713. https://doi.org/10.3390/app12157713