Fundamentals and Trends in Vision and Image Processing

Course Level:

The Course is part of the graduate program on Visual Computing at IMPA. It is a regular course for the master program, and it is also recommendend for first year PhD students of the program.

The course is divided into fundamentals and trends. The fundamental part encompasses basic concepts of the area while the trends part addresses recent developments.

Under this methodology, the basic material does not change, but each edition of the course focuses on a research theme of interest (see previous offerings below).


- Knowledge of Algorithm & Programming (C, C++, Java, etc.);
- Linear Algebra and Calculus;
- Basic knowledge about graphics and imaging;

Topics Covered:

- Signal Processing Basics
- Continuous and Discrete Fourier Transform
- Shannon Sampling Theory
- Color Fundamentals
- Digital Image
- Image Representation and Reconstruction
- Multiscale Representations and Wavelets
- Computer Vision / Image Analysis
- Machine Learning / Convolutional Neural Networks
- Video Understanding

Additional Topics:

These topics are in general covered in short seminars, with the students, in the final part of the course:

- Scene Understanding
- 3D Reconstruction from Images
- Tracking and Pose Detection
- Segmentation and Localization
- Image based Rendering


The course covers chapters 1 to 7 of the book

- J. Gomes & L. Velho, Image Processing for Computer Graphics, Springer-Verlag, 1997.

The book site contains additional material about the topics covered on the course, as well as, exercises for each of the chapters.

Additional references for the course :

- Computer Vision: Algorithms and Applications, Rchard Szeliski
- Deep Learning, an Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press

Current offering of this course

Themes and projects of previous years

Besides the regular course exercises and tests, the course evaluation uses the student seminars based on additional topics, and a final project. This link contains additional Information and material of some of the courses that we have already taught.

2022: Two Sides of Neural Networks

2021: Rebirth of Generative Models

2020: Deep Video

2019: Projects - 3D reconstruction

2018: Projects - Generative Models

2017: Presentations - CNN

2016: Presentations - Deep Learning

2015: projects - RGB-D / Mobile

2014: projects - Omnidirectional Imaging

2013: projects - Mobile Computational Photography

2012: projects - Mobile Applications

2011: projects - RGB-D Video

2010: projects - Computational Photography II

2009: projects - Gigapixel Images

2008: projects - Image Collections

2007: projects - Statistical Image Models

2006: projects - Computational Photograpy I

2005: projects - Vision and Graphics

2003: projects - SIGGRAPH Trends

2002: projects - Color, Etc

2001: projects - Artistic Image Generation

2000 : projects - Dithering

1998 : "Image Mosaics"

1998 : "Query by Image"

1997 : "Impressionist Filtering"

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