References
Key Resources
NumPy Documentation
Matplotlib Documentation
Image Processing
Deep Learning Frameworks
References
Textbooks and Monographs
Gonzalez, R. C., & Woods, R. E. (2018). Digital Image Processing (4th ed.). Pearson Education.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Available at: https://www.deeplearningbook.org/
Burger, W., & Burge, M. J. (2016). Digital Image Processing: An Algorithmic Introduction Using Java (2nd ed.). Springer.
Szeliski, R. (2022). Computer Vision: Algorithms and Applications (2nd ed.). Springer. Available at: https://szeliski.org/Book/
Prince, S. J. D. (2023). Understanding Deep Learning. MIT Press. Available at: https://udlbook.github.io/udlbook/
Python-Specific Resources
Kinser, J. M. (2018). Image Operators: Image Processing in Python. CRC Press.
VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data. O’Reilly Media. Available at: https://jakevdp.github.io/PythonDataScienceHandbook/
Raschka, S., & Mirjalili, V. (2019). Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 (3rd ed.). Packt Publishing.
Online Resources and Courses
Kinsley, H., & Kukieła, D. (2020). Neural Networks from Scratch in Python. https://nnfs.io/
Chollet, F. (2021). Deep Learning with Python (2nd ed.). Manning Publications.
Computer Vision with Python
Rosebrock, A. (2021). Deep Learning for Computer Vision with Python. PyImageSearch.
Howse, J., & Minichino, J. (2020). Learning OpenCV 4 Computer Vision with Python 3 (3rd ed.). Packt Publishing.
Mallick, S. (2021). Deep Learning for Vision Systems. Manning Publications.
Research Papers and Seminal Works
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097-1105.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770-778).
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 234-241). Springer.
Python Libraries Documentation
Harris, C. R., Millman, K. J., van der Walt, S. J., et al. (2020). Array programming with NumPy. Nature, 585(7825), 357-362. https://doi.org/10.1038/s41586-020-2649-2
Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools.
Paszke, A., Gross, S., Massa, F., et al. (2019). PyTorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, 32, 8024-8035.
Abadi, M., Agarwal, A., Barham, P., et al. (2016). TensorFlow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467.
van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., et al. (2014). scikit-image: Image processing in Python. PeerJ, 2, e453. https://doi.org/10.7717/peerj.453
Grayscale Conversion Standards: - ITU-R Recommendation BT.709: Parameter values for the HDTV standards for production and international programme exchange (1990) - ITU-R Recommendation BT.601: Studio encoding parameters of digital television for standard 4:3 and wide screen 16:9 aspect ratios (1982)
Medical Imaging: - Guidelines for urothelial cell analysis and grading - Quantitative pathology and automated image analysis
Additional Resources
For questions, corrections, or suggestions, please contact the author or submit an issue on the book’s repository.