Analysis of Microalgal Density Estimation by Using LASSO and Image Texture Features
Abstract
:1. Introduction
2. Data Collection
2.1. Monitoring System
2.2. Preprocessing Data
3. Feature Design
3.1. Averages of Color Channels
3.2. Mean Intervals of Color Channels
3.3. Spatial Frequency
3.4. Entropy
4. L1-Regularization Based Estimation Method
4.1. LASSO
4.2. Penalty Term Learning
4.3. Microalgal Density Estimation
5. Experimental Result Analysis
5.1. Single Feature Combinations
- S1: Features of average values of all three color channels: R, G and B.
- S2: Six interval features: , , , , , and .
- S3: Three spatial frequency features: , , and .
- S4: Three entropy features: , , and .
5.2. Two-Feature Combinations
- S5: Features of three color channel averages and three entropies: R, G, B, , , and .
- S6: Features of six interval bounds and three entropies: , , , , , , , , and .
- S7: Features of three color channel averages and three spatial frequency powers: R, G, B, , , and .
- S8: Features of three color channel averages and six interval bounds: R, G, B, , , , , , and .
- S9: Features of three spatial frequency powers and three entropies: , , , , , and .
- S10: Features of six interval bounds and three spatial frequency powers: , , , , , , , , and .
5.3. Three and Four-Feature Combinations
- S11: Features of three color channel averages, six interval bounds, and three entropies: R, G, B, , , , , , , , , and .
- S12: Features of three color channel averages, six interval bounds, and three spatial frequency powers: R, G, B, , , , , , , , , and .
- S13: Features of three color channel averages, three entropies, and three spatial frequency powers: R, G, B, , , , , , and .
- S14: Features of six interval bounds, three entropies, and three spatial frequency powers: , , , , , , , , , , , and .
5.4. Higher-Order and Nonlinear Entropy Features
- S16: Features including R, G, B, , , , , , , , , and .
- S17: Features including R, G, B, , , , , , and .
- S18: Features including , , , , , and .
- S19: Features including R, G, B, , , , , , , , , and .
- S20: Features including R, G, B, , , , , , , , , and .
- S21: Features including R, G, B, , , , , , , , , , and .
5.5. Estimation Results
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
LASSO | Least absolute shrinkage and selection operator |
RGB | Red, green, and blue |
ITU | International Telecommunication Union |
DFT | Discrete Fourier transform |
RMSE | Root mean square error |
GP | Gaussian process |
GT | Ground truth |
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Minimum MSE | 2.313 | 2.215 | 2.278 |
0.001 | 0.001 | 0.001 |
S1 | S2 | S3 | S4 | |
---|---|---|---|---|
mean | 2.40 | 2.12 | 2.36 | 4.90 |
std | 0.25 | 0.26 | 0.28 | 0.67 |
S5 | S6 | S7 | S8 | S9 | S10 | |
---|---|---|---|---|---|---|
mean | 1.75 | 1.78 | 1.89 | 2.09 | 1.79 | 2.09 |
std | 0.23 | 0.23 | 0.25 | 0.26 | 0.24 | 0.24 |
S11 | S12 | S13 | S14 | S15 | |
---|---|---|---|---|---|
mean | 1.76 | 2.11 | 1.76 | 1.77 | 1.79 |
std | 0.24 | 0.23 | 0.24 | 0.23 | 0.24 |
S16 | S17 | S18 | S19 | S20 | S21 | |
---|---|---|---|---|---|---|
mean | 1.63 | 1.64 | 3.76 | 1.67 | 1.54 | 1.56 |
std | 0.21 | 0.21 | 0.44 | 0.23 | 0.20 | 0.24 |
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Nguyen, L.; Nguyen, D.K.; Nguyen, T.; Nguyen, B.; Nghiem, T.X. Analysis of Microalgal Density Estimation by Using LASSO and Image Texture Features. Sensors 2023, 23, 2543. https://doi.org/10.3390/s23052543
Nguyen L, Nguyen DK, Nguyen T, Nguyen B, Nghiem TX. Analysis of Microalgal Density Estimation by Using LASSO and Image Texture Features. Sensors. 2023; 23(5):2543. https://doi.org/10.3390/s23052543
Chicago/Turabian StyleNguyen, Linh, Dung K. Nguyen, Thang Nguyen, Binh Nguyen, and Truong X. Nghiem. 2023. "Analysis of Microalgal Density Estimation by Using LASSO and Image Texture Features" Sensors 23, no. 5: 2543. https://doi.org/10.3390/s23052543
APA StyleNguyen, L., Nguyen, D. K., Nguyen, T., Nguyen, B., & Nghiem, T. X. (2023). Analysis of Microalgal Density Estimation by Using LASSO and Image Texture Features. Sensors, 23(5), 2543. https://doi.org/10.3390/s23052543