SNR Analysis for Quantitative Comparison of Line Detection Methods
Abstract
:1. Introduction
- Streamlined derivation of the SNRs of line detectors including the derivation of correlations among noises after smoothing; derivations of signal and noise strengths of elementary operators; combination of SNRs for line detectors; and application of a penalty function for considering the influence of blurring, smoothing, and line width on line detectors.
- Analytical quantification of the SNRs of line detectors using error propagation and visualization of the quantity of the derived values including signal strengths, noise strengths, and SNRs.
- Verification of the validity of the SNRs derived for the SD- and SGAD-based line detectors by investigating the relationship of the SNRs with completeness and correctness of their line detection results.
2. Related Work
3. Method—SNR of ED and SD for Line Detection
3.1. Line Model and Derivation of Its Derivatives
3.2. Measure of Signal Strengths
3.3. Measure of Noise Strengths
3.3.1. Correlation of Noises in Smoothed Images
3.3.2. Measure of Noise in Derivatives
3.4. Derivation of SNRs
4. Method—SNR of SGAD for Line Detection
4.1. Definition of SGAD
4.2. Dispersion of Gradient Angle Differences
4.3. Derivation of SNRs
5. Experimental Results and Discussion
6. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Seo, S. SNR Analysis for Quantitative Comparison of Line Detection Methods. Appl. Sci. 2021, 11, 10088. https://doi.org/10.3390/app112110088
Seo S. SNR Analysis for Quantitative Comparison of Line Detection Methods. Applied Sciences. 2021; 11(21):10088. https://doi.org/10.3390/app112110088
Chicago/Turabian StyleSeo, Suyoung. 2021. "SNR Analysis for Quantitative Comparison of Line Detection Methods" Applied Sciences 11, no. 21: 10088. https://doi.org/10.3390/app112110088
APA StyleSeo, S. (2021). SNR Analysis for Quantitative Comparison of Line Detection Methods. Applied Sciences, 11(21), 10088. https://doi.org/10.3390/app112110088