Computer-Vision-Notes

This GitHub repository contains my notes and code examples on computer vision, image processing and computational imaging fields.

Table of Contents

• Codes and Notebooks
• Tools
• References
• Repositories
• Online Courses
• Logs

Notebooks

Chapter	OpenCV (Python)	OpenCV (C++)	Scikit Image	PIL (Pillow)	Tags
01 — Image I/O					#read #write #formats
02 — Color Spaces					#RGB #HSV #Grayscale
03 — Geometric Transforms					#rotate #scale #affine
04 — Filtering					#blur #gaussian #median
05 — Histogram & Intensity					#histogram #equalization
06 — Thresholding					#binary #otsu #adaptive
07 — Edges & Gradients					#sobel #canny #prewitt
08 — Morphology					#erosion #dilation #opening
09 — Contours & Measurements					#contours #area #perimeter
10 — Segmentation					#watershed #region #masking
11 — Feature Detection					#SIFT #ORB #Harris
12 — I/O Advanced (Video, Drawing)					#video #draw #annotate

Codes and Notebooks

1. Computer Vision - Seeing the world with the eye of computers and machines [dir] [ipynb] [nbviewer]

Tools

• OpenCV is a fast, C++-based computer-vision library with Python bindings; it excels at performance and real-time use cases and offers a broad set of classical CV algorithms (filtering, feature detection, geometry, and video I/O), making it a common choice for production pipelines.
• Scikit Image is a pure-Python library built on NumPy, SciPy, and Matplotlib; it emphasizes a clean, consistent API and tight integration with the scientific Python stack, which makes it ideal for research, education, and prototyping where readability and interoperability matter.
• PIL is the actively maintained fork of the Python Imaging Library; it focuses on simplicity and covers image I/O, format conversion, and basic manipulations (resize, crop, and filtering), so it’s widely used in data preprocessing and lightweight image tasks.

Repositories

1. Awesome Computer Vision by Jia-Bin Huang
2. Numerical Tours

Online Courses

1. First Principles of Computer Vision Specialization This is five-course specialization course offered by Prof. Shree Nayar for learning fundamentals of computer vision field.
   • Camera and Imaging
   • Features and Boundaries
   • 3D Reconstruction - Single Viewpoint
   • 3D Reconstruction - Multiple Viewpoint
   • Visual Perception
2. Fundamentals of Digital Image and Video Processing by Aggelos Katsaggelos

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Journals

• IEEE Transactions on Image Processing (TIP)
• IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
• IEEE Transactions on Computational Imaging
• International Journal of Computer Vision (IJCV)
• Computer Vision and Image Understanding (CVIU)
• Image and Vision Computing (IVC)
• Pattern Recognition (PR)
• Pattern Recognition Letters (PRL)
• Machine Vision and Applications (MVA)
• Neurocomputing
• Journal of Machine Learning Research (JMLR)
• IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

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Conferences

• IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
• IEEE International Conference on Computer Vision (ICCV)
• European Conference on Computer Vision (ECCV)
• Asian Conference on Computer Vision (ACCV)
• British Machine Vision Conference (BMVC)
• Winter Conference on Applications of Computer Vision (WACV)
• International Conference on Pattern Recognition (ICPR)
• Conference on Neural Information Processing Systems (NeurIPS)
• International Conference on Machine Learning (ICML)
• International Conference on Learning Representations (ICLR)
• AAAI Conference on Artificial Intelligence (AAAI)
• International Joint Conference on Artificial Intelligence (IJCAI)

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References

1. Richard Szeliski — Computer Vision: Algorithms and Applications, 2nd edition (Springer, 2022)
2. Rafael C. Gonzalez & Richard E. Woods — Digital Image Processing, 4th edition (Pearson, 2018)
3. David A. Forsyth & Jean Ponce — Computer Vision: A Modern Approach, 2nd edition (Pearson, 2011)
4. Hartley & Zisserman — Multiple View Geometry in Computer Vision, 2nd edition (Cambridge University Press, 2004)
5. Jan Erik Solem — Programming Computer Vision with Python (O’Reilly, 2012) — practical Python focus
6. Adrian Rosebrock — Deep Learning for Computer Vision (PyImageSearch, 2019) — applied deep learning
7. Berthold K. P. Horn — Robot Vision (MIT Press, 1986)
8. Alan C. Bovik (ed.) — The Essential Guide to Image Processing, 2nd ed. (Academic Press, 2009)
9. Antonio Torralba & William T. Freeman — Fundamentals of Computer Vision (MIT, 2020)

Logs

• 23 September 2025, update notebooks table with tags.
• 19 September 2025, add 05_opencv_video_processing notebook.
• 15 September 2025, update references section, add journals & conferences with links, revise notebook table
• 15 September 2025, update references.
• 15 September 2025, add tool descriptions for OpenCV, Scikit Image, and PIL in README file.
• 15 September 2025, create OpenCV, Scikit Image and PIL Notebooks table in README file.
• 2 September 2025 - 10 September 2025, the first four OpenCV notebooks created.
• 18 March 2025, the repo created.