Biological Basis and Computer Vision Applications of Image Phase Congruency: A Comprehensive Survey
Abstract
:1. Introduction
2. Phase Congruency in Biological Perception
2.1. Frequency Analysis in Biological Perception
2.2. Fourier Transform and Phase Congruency
2.3. Biological Basis of Phase Congruency
2.3.1. Spatial Phase Congruency
2.3.2. Temporal Phase Congruency
2.4. Image Phase Congruency and Phase Correlation
3. Computational Implementations of IPC
3.1. Relevant Computational Aspects for IPC Implementation
3.2. IPC Computation from FOURIER and Hilbert Transforms
3.3. IPC Computation from Wavelet Transform
3.4. IPC Computation from Monogenic Filters
3.5. A unified Formulation for IPC Computations
3.6. IPC for 3D Images
4. Applications of Phase Congruency in Low-Level Computer Vision
4.1. Image Denoise Using IPC
4.2. Image Quality Evaluation Using IPC
4.2.1. Full-Reference IQA
4.2.2. Reduced-Reference and No-Reference IQA
4.3. Autofocus and Blur Detection Using IPC
4.4. Image Super-Resolution Using IPC
4.5. Image Watermarking and Slicing Detection Using IPC
5. Applications of Phase Congruency in Mid-Level Computer Vision
5.1. Feature Detection Using IPC
5.1.1. Edge Detection
5.1.2. Corner Detection
5.1.3. Ridge Detection
5.2. Image Segmentation Using IPC
5.2.1. Image Binarization
5.2.2. Image Segmentation
5.3. Image Matching and Registration Using IPC
5.3.1. Same-Mode Image Registration
5.3.2. Multi-Modal Image Registration
5.4. Image Fusion Using IPC
5.4.1. Same-Mode Image Fusion
5.4.2. Multi-Modal Image Fusion
6. Applications of Phase Congruency in High-Level Computer Vision
6.1. Object Detection, Tracking, and Recognition Using IPC
6.2. Other High-Level Applications Using IPC
7. Challenges for Practical Applications of IPC
7.1. Noise Sensitivity
7.2. Computational Complexity
7.3. Parameter Tuning
7.4. Integration with Other Image Features
8. Potential Improvement of IPC Using Deep Learning
9. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Tian, Y.; Wen, M.; Lu, D.; Zhong, X.; Wu, Z. Biological Basis and Computer Vision Applications of Image Phase Congruency: A Comprehensive Survey. Biomimetics 2024, 9, 422. https://doi.org/10.3390/biomimetics9070422
Tian Y, Wen M, Lu D, Zhong X, Wu Z. Biological Basis and Computer Vision Applications of Image Phase Congruency: A Comprehensive Survey. Biomimetics. 2024; 9(7):422. https://doi.org/10.3390/biomimetics9070422
Chicago/Turabian StyleTian, Yibin, Ming Wen, Dajiang Lu, Xiaopin Zhong, and Zongze Wu. 2024. "Biological Basis and Computer Vision Applications of Image Phase Congruency: A Comprehensive Survey" Biomimetics 9, no. 7: 422. https://doi.org/10.3390/biomimetics9070422
APA StyleTian, Y., Wen, M., Lu, D., Zhong, X., & Wu, Z. (2024). Biological Basis and Computer Vision Applications of Image Phase Congruency: A Comprehensive Survey. Biomimetics, 9(7), 422. https://doi.org/10.3390/biomimetics9070422