ETH Zurich - D-INFK - IVC - CVG - Lectures - Computer Vision

Computer Vision


Instructors: Marc Pollefeys, Luc Van Gool
Teaching assistants: Gim Hee Lee, Jens Puwein, Mansfield Alexander Paul, Bogdan Alexe
Lectures: Wednesdays from 13:15-16:00 in CHN C14
Exercises: Thursdays from 13:15-15:00 in NO C44
Exercises: Thursdays from 15:00-17:00 in HG G 26.3

Computer Vision (following Tomaso Poggio, MIT): Computer Vision, formerly an almost esoteric corner of research and regarded as a field of research still in its infancy, has emerged to a key discipline in computer science. Vision companies have emerged and commercial applications become available, ranging from industrial inspection and measurements to security database search, surveillance, multimedia and computer interfaces. Computer Vision is still far from being a solved problem, and most exciting developments, discoveries and applications still lie ahead of us. Understanding the principles of vision has implications far beyond engineering, since visual perception is one of the key modules of human intelligence.

Course Objectives

The objectives of this course are:
1.To introduce the fundamental problems of computer vision.
2.To introduce the main concepts and techniques used to solve those.
3.To enable participants to implement solutions for reasonably complex problems.
4.To enable participants to make sense of the computer vision literature.

Course Topics

Camera models and calibration, invariant features, Multiple-view geometry, Model fitting, Stereo Matching, Segmentation, 2D Shape matching, Shape from Silhouettes, Optical flow, Structure from motion, Tracking, Object recognition, Object category recognition

Target Audience

The target audience of this course are Master students, that are interested to get a basic understanding of computer vision.

Requirements

Fundamentals of calculus and linear algebra, basic concepts of algorithms and data structures, basic programming skills in Matlab and C.

Some useful links

The Computer Vision Homepage
CVonline
Middlebury Stereo Vision Page
VLFeat SIFT package for MATLAB
STUD-IDES
Course Notes
Computer Vision: Algorithms and Applications

Lecture Slides

Introduction[pdf]
Feature Extraction and Matching[pdf]
Camera Models and Calibration[pdf]
Multi-View Geometry[pdf]
Model Fitting[pdf]
Stereo Matching[pdf]
Image Segmentation[pdf]
Shape from Silhouettes[pdf]
Optical Flow[pdf]
Structure from Motion[pdf]
Tracking[pdf]
Object Category Recognition[pdf][18Mb] [pptx][44Mb]
Specific Object Recognition[pdf][55Mb!]

© CVG, ETH Zürich lm@inf.ethz.ch