If you’re starting out in the field of Computer Vision, find below an exhaustive list of topics one must know.

📘 A. Beginner level

Mathematics :

🔸 Linear Algebra
🔸Singular Value Decomposition
🔸 Introductory level Pattern Recognition
🔸 Principal Component Analysis
🔸 Kalman filtering
🔸 Fourier Transform
🔸 Wavelets

Image Processing:

🔸 Online Course offered by Duke University on Coursera
🔸 Digital Image Processing by Gonzalez and Woods

B. Advanced level

🔸 Linear Discriminant AnalysisProbability, Bayes rule, Maximum Likelihood, MAP
🔸 Mixtures and Expectation-Maximization Algorithm
🔸 Introductory level Statistical Learning
🔸 Support Vector Machines
🔸 Genetic Algorithms
🔸 Hidden Markov Models
🔸 Bayesian Networks

To gain practical knowledge about how things work especially the algorithms, start learning about OpenCV from Computer Vision perspective:

📘 Learning OpenCV: Computer Vision with the OpenCV Library
🔸 Tombone’s Computer Vision Blog

#ComputerVision
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