> ## Documentation Index > Fetch the complete documentation index at: https://aegean.ai/llms.txt > Use this file to discover all available pages before exploring further. # Foundations of CV > Project brief for building supplemental tutorial notebooks that replicate the figures in the MIT Foundations of Computer Vision book. This project builds a set of supplemental tutorial sections for [*Foundations of Computer Vision*](https://visionbook.mit.edu) by Antonio Torralba, Phillip Isola, and William T. Freeman. The book explains almost every idea with a figure or a plot. Your job is to reproduce those figures as runnable code, so each concept becomes something you can execute, change, and inspect for yourself. ## The task Pick a chapter from the list below and write a tutorial section that reproduces its key figures. Treat the book as the specification: for every plot the chapter uses to make a point, write the code that regenerates it from scratch and explain the concept the figure illustrates. The result is a companion page that sits next to the book and moves a reader from "read the figure" to "run the figure". ## What a finished section looks like * It reproduces the chapter's main figures with runnable code. * It states the concept each figure demonstrates and the math behind it. * It runs end to end, so you can change a parameter and watch the plot update. Follow the conventions already used across this site: * One chapter per section, each in its own directory. Reserve that directory's `images/` folder for block diagram artifacts only: the diagram source (Mermaid or C4) together with its rendered `.png`. Notebook output figures (the plots your code generates) are embedded in the page directly and do not need to be stored under `images/`. * Markdown cells and inline comments never discuss the plotting library or the toolchain (matplotlib, headless rendering, install steps, and the like). See the coding guidelines below for what they should emphasize. * Pure-plotting cells are tagged so the page shows the figure but hides the drawing code. * The prose addresses you directly and stays evergreen. ## Coding guidelines * Write all computational code in **PyTorch** and libraries built on the PyTorch foundation, such as [Kornia](https://kornia.readthedocs.io/en/stable/) for differentiable computer vision. Express the math each figure illustrates with tensors and these libraries rather than NumPy or framework-specific equivalents, so every section runs the same way on CPU or GPU. * Markdown cells and inline comments explain the **code first and the concept second**: describe what each block does, then connect it back to the idea the figure illustrates. ## Where your work goes Submit your work to the [`pantelis/eng-ai-agents`](https://github.com/pantelis/eng-ai-agents) repository through a fork and pull request. 1. **Fork** [`pantelis/eng-ai-agents`](https://github.com/pantelis/eng-ai-agents) to your own account and clone your fork. 2. **Branch.** Create one branch per section, named `mit-book-chapter--
`. For chapter 12, section 5, the branch is `mit-book-chapter-12-5`. 3. **Location.** Place every notebook under the `notebooks/` folder. 4. **Pull request.** Push the branch to your fork and open a pull request against `main` of `pantelis/eng-ai-agents`. ## Chapters to treat The chapters below are grouped by the parts of the book. There are 28 chapters in scope. The **Assignee** column records who has claimed each chapter; all 28 are currently claimed. Any chapter shown as **Open** is still available, so add your name in the course channel to take it. ### Image formation | Chapter | Title | Assignee | Status | | ------- | ------------------------- | --------------- | ------- | | 5 | Imaging | Ishan Tandon | Claimed | | 6 | Lenses | Kimberly Milner | Claimed | | 7 | Cameras as linear systems | Ishan Tandon | Claimed | | 8 | Color | Ishan Tandon | Claimed | ### Foundations of learning | Chapter | Title | Assignee | Status | | ------- | -------------------------------------------- | --------------- | ------- | | 13 | Neural networks as distribution transformers | Kimberly Milner | Claimed | ### Image processing | Chapter | Title | Assignee | Status | | ------- | ---------------------- | --------------- | ------- | | 15 | Linear image filtering | Kimberly Milner | Claimed | | 16 | Fourier analysis | Kimberly Milner | Claimed | ### Sampling and multiscale image representations | Chapter | Title | Assignee | Status | | ------- | ---------------------------------- | ---------- | ------- | | 20 | Image sampling and aliasing | Nazib Khan | Claimed | | 21 | Downsampling and upsampling images | Nazib Khan | Claimed | | 22 | Filter banks | Nazib Khan | Claimed | | 23 | Image pyramids | Nazib Khan | Claimed | ### Neural architectures for vision | Chapter | Title | Assignee | Status | | ------- | ------------------------- | --------------- | ------- | | 24 | Convolutional neural nets | Kimberly Milner | Claimed | | 26 | Transformers | Kimberly Milner | Claimed | ### Generative image models and representation learning | Chapter | Title | Assignee | Status | | ------- | ----------------------------- | --------------------------------------------- | ------- | | 30 | Representation learning | Shaury Pratap Singh (Nazib Khan contributing) | Claimed | | 34 | Conditional generative models | Shaury Pratap Singh | Claimed | ### Understanding geometry | Chapter | Title | Assignee | Status | | ------- | ---------------------------------------------- | ------------------- | ------- | | 38 | Representing images and geometry | Kaushik Kachireddy | Claimed | | 39 | Camera modeling and calibration | Ruimeng Yang | Claimed | | 40 | Stereo vision | Ruimeng Yang | Claimed | | 41 | Homographies | Ruimeng Yang | Claimed | | 42 | Single view metrology | Kaushik Kachireddy | Claimed | | 43 | Learning to estimate depth from a single image | Ruimeng Yang | Claimed | | 44 | Multiview geometry and structure from motion | Shaury Pratap Singh | Claimed | | 45 | Radiance fields | Ruimeng Yang | Claimed | ### Understanding motion | Chapter | Title | Assignee | Status | | ------- | ------------------------------- | ------------------- | ------- | | 46 | Motion estimation | Ruimeng Yang | Claimed | | 47 | 3D motion and its 2D projection | Kaushik Kachireddy | Claimed | | 48 | Optical flow estimation | Shaury Pratap Singh | Claimed | | 49 | Learning to estimate motion | Shaury Pratap Singh | Claimed | ### Understanding vision with language | Chapter | Title | Assignee | Status | | ------- | ------------------- | --------------------------------------------- | ------- | | 51 | Vision and language | Shaury Pratap Singh (Nazib Khan contributing) | Claimed | ## Reference *Foundations of Computer Vision*, Antonio Torralba, Phillip Isola, and William T. Freeman, MIT Press. Read it online at [visionbook.mit.edu](https://visionbook.mit.edu). *** [Edit this page on GitHub](https://github.com/aegean-ai/eaia/edit/main/src/courses/cv/foundations-of-cv/index.mdx) or [file an issue](https://github.com/aegean-ai/eaia/issues/new/choose).