A Low Redundancy Wavelet Entropy Edge Detection Algorithm
Abstract
:1. Introduction
2. Related Work
2.1. First Order
2.2. Second Order
2.3. Entropy Based
2.4. Wavelet Based
2.5. Deep Learning/Machine Learning Based
3. Methodology
3.1. Wavelet Decomposition
3.2. Wavelet Decomposition Level Selection
3.3. Entropy Thresholding
4. Results
4.1. Computational Efficiency
Algorithm 1: LRWEEDA edge detection |
4.2. Noise Resilience
4.3. Performance against Standard Edge Detection Metrics
- The Boundary F1 score is defined as the harmonic mean (F1-measure) of the precision and recall values which measure the matching weight for the predicted boundary and the ground truth boundary, as
- The Jaccard coefficient for two sets is defined as the size of the intersection of the two sets divided by the size of their union as
- Pratt’s FOM uses Euclidean distance to compare two edge images [60]. It multiplies a scale factor ∝ to the Euclidean distance calculated between the two images to penalize displaced edges, as
- Qualitative results of the proposed algorithm were obtained and compared with similar edge detection algorithms (Figure 9)
- Ten images were used to calculate the average processing times of the algorithms (Figure 10).
- Noise resilience of the proposed algorithm was analyzed by using four images and compared with Canny (Figure 9).
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Tao, Y.; Scully, T.; Perera, A.G.; Lambert, A.; Chahl, J. A Low Redundancy Wavelet Entropy Edge Detection Algorithm. J. Imaging 2021, 7, 188. https://doi.org/10.3390/jimaging7090188
Tao Y, Scully T, Perera AG, Lambert A, Chahl J. A Low Redundancy Wavelet Entropy Edge Detection Algorithm. Journal of Imaging. 2021; 7(9):188. https://doi.org/10.3390/jimaging7090188
Chicago/Turabian StyleTao, Yiting, Thomas Scully, Asanka G. Perera, Andrew Lambert, and Javaan Chahl. 2021. "A Low Redundancy Wavelet Entropy Edge Detection Algorithm" Journal of Imaging 7, no. 9: 188. https://doi.org/10.3390/jimaging7090188