Computer Vision Study Plan
A comprehensive 14-week curriculum covering fundamental and advanced topics in computer vision with interactive p5.js visualizations.
Recommended Study Path
Prerequisites
Foundations Study Plan
- Python Foundations
- NumPy & Data Wrangling
- Mathematical Foundations
Complete the Foundations study plan first →
Foundations
Weeks 1-4
- Ch 1-2: Introduction & Image Formation
- Ch 3: Image Processing
Core concepts: pixels, transformations, filtering
Core CV
Weeks 5-8
- Ch 4-5: Optimization & Deep Learning
- Ch 6-8: Recognition & Features
Neural networks, detection, segmentation
Advanced
Weeks 9-14
- Ch 9-10: Motion & Comp. Photography
- Ch 11-14: 3D Vision & Rendering
SLAM, depth estimation, neural rendering
All Chapters
Introduction
What is computer vision? A brief history, book overview, and notation.
Image Formation
Geometric primitives, transformations, photometric image formation, and digital camera concepts.
Image Processing
Point operators, linear filtering, Fourier transforms, pyramids, wavelets, and geometric transformations.
Model Fitting and Optimization
Least squares fitting, RANSAC for robust estimation, and total variation regularization.
Deep Learning
Neural network fundamentals, backpropagation, CNNs, and modern architectures like ResNet and Transformers.
Recognition
Instance recognition, image classification, object detection, and semantic segmentation.
Feature Detection and Matching
Points, patches, edges, contours, lines, vanishing points, and segmentation.
Image Alignment and Stitching
Pairwise alignment, image stitching, global alignment, and compositing.
Motion Estimation
Translational alignment, parametric motion, optical flow, and layered motion.
Computational Photography
HDR imaging, super-resolution, denoising, matting, and texture synthesis.
Structure from Motion and SLAM
Camera calibration, pose estimation, SfM, and simultaneous localization and mapping.
Depth Estimation
Epipolar geometry, stereo matching, multi-view stereo, and monocular depth.
3D Reconstruction
Shape from X, 3D scanning, surface representations, and model-based reconstruction.
Image-Based Rendering
View interpolation, light fields, video-based rendering, and neural rendering.
Timed Tests
Practice Problem Sets
Sharpen your skills with coding challenges and system design problems.
Content inspired by Computer Vision: Algorithms and Applications, 2nd Edition by Richard Szeliski