Computer Vision Foundations
A structured path from light and pixels to geometry, classical features, and modern deep networks — with checkpoints and exercises at every step.
What you'll learn
- Imaging
- Geometry
- Features
- Deep learning
- Deployment
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Your progress
0 / 6 lessons reached
Lessons in this path
Work top to bottom within each module, or jump in from the table of contents on each lesson page.
Module 1. Imaging & digital images
How cameras turn light into arrays of numbers, and how we preprocess those arrays for algorithms.
- Lesson 155 min
Light, sensors, and the imaging pipeline
Radiance, photons, sensor noise, demosaicing, gamma, and the end-to-end path from scene to stored image.
- Lesson 260 min
Pixels, convolution, and edges
Grids, channels, linear filters, separable kernels, gradients, and building intuition before neural networks.
Module 2. Geometry & correspondence
Pinhole cameras, projection, epipolar geometry, and how 2D images relate to 3D structure.
Module 3. Learning-based vision
Convolutional networks for classification, detection, and segmentation — plus practical constraints on mobile and edge.
- Lesson 575 min
Convolutional networks for images
From fully-connected layers to conv blocks, receptive fields, pooling, and training objectives.
- Lesson 670 min
Detection, segmentation, and on-device trade-offs
Anchors vs queries, instance vs semantic segmentation, latency, memory, and quantization at a high level.