Computer Vision

Semester:
WS 2016
Type:
Lecture
Lecturer:
Credits:
V3 + Ü1 (6 ECTS credits)
Course Dates:
Type
Date
Room
Lecture Mon 10:15-11:45 UMIC 025
Lecture Wed 10:15-11:45 UMIC 025

Announcement

IMPORTANT: Shift of exam time

Due to the large number of registrations, we had to shift the time for the Computer Vision exam to the following slot:

Friday, 24th of February, 12:00-15:00 (entry ~12:15, exam lasts 2 hours)

Lecture Description

Cameras and images form an ever-growing part of our daily lives. Billions of images and massive amounts of video data are becoming available on the Internet. Large search engines are being created to make sense out of this data. And more and more commercial applications are coming up, e.g. in surveillance and security, on consumer devices, for video special effects, in mobile robotics and automotive contexts, and for medical image processing. All those applications are building on visual capabilities. For us humans, those capabilities are natural. But how do we actually accomplish them? And how can we teach a machine to perform similar tasks for us?

The goal of Computer Vision is to develop methods that enable a machine to "understand" or analyze images and videos. This lecture will teach the fundamental Computer Vision techniques that underlie such capabilities. In addition, it will show current research developments and how they are applied to solve real-world tasks. The lecture is accompanied by Matlab-based exercises that will allow you to collect hands-on experience with the algorithms introduced in the lecture (there will be one exercise sheet roughly every two weeks).

Literature

In the last decades, Computer Vision has evolved into a rapidly growing field with research going into so many directions that no single book can cover them all. We will mainly make use of the following books: Forsyth &: Ponce: Computer Vision - A Modern Approach

  • D. Forsyth, J. Ponce, Computer Vision - A Modern Approach, Prentice Hall, 2002
  • R. Szeliski, Computer Vision - Algorithms and Applications, Springer, 2010
  • R. Hartley, A. Zisserman, Multiple View Geometry in Computer Vision, 2nd Edition, Cambridge University Press, 2004

However, a good part of the material presented in this class is the result of very recent research, so it hasn't found its way into textbooks yet. Wherever research papers are necessary for a deeper understanding, we will make them available on this web page.

Course Schedule
Date Title Content Material
Introduction Why vision? Applications, Challenges, Image Formation
Exercise 1 Intro Matlab
Image Processing I Binary Images, Thresholding, Morphology, Connected Components, Region Descriptors
Image Processing II Linear Filters, Gaussian Smoothing, Median Filter
Edge Detection Multi-scale Representations, Image Derivatives, Edge Detection
Structure Extraction Chamfer Matching, Line Fitting, Hough Transform, Gen. Hough Transform
Segmentation I Segmentation as Clustering, k-means, EM, Mean-Shift
Exercise 2 Thresholding, Morphology, Derivatives, Edges
Segmentation II Segmentation as Energy Minimization, (Markov Random Fields, Graph Cuts)
Categorization I Sliding Window-based Object Detection, SVM, HOG
Categorization II Haar-Wavelets, AdaBoost, Viola-Jones
Local Features I Interest points, Harris Detector, Hessian Detector
Local Features II Scale Invariance, Local Descriptors, SIFT
Exercise 3 Structure Extraction, Mean-Shift Segmentation, Segmentation with Graph Cuts
Local Features III Specific Object Recognition with Local Features, Geometric Verification, Robust Estimation, RANSAC, Gen. Hough Transform
Matching & Indexing Matching&Indexing, Visual Vocabularies, Inverted File, Vocabulary Tree, tf-idf Weighting
Categorization III Part-Based Methods, Bag-of-Words Model
Exercise 4 Histograms, Recognition, Interest Points
Categorization IV Deep Learning, CNNs, Large-Scale Visual Recognition with Deep Learning
Exercise 5 Interest points, Local Feature Matching, Homography Estimation
Deep Learning Deep Learning for other Vision tasks, Segmentation, Face identification, Human Pose Estimation, etc.
3D Reconstruction I Epipolar Geometry, Essential Matrix, Correspondence Search
3D Reconstruction II Camera Parameters, Calibration, Triangulation
3D Reconstruction III Fundamental Matrix, Eight-Point Algorithm, Active Stereo
Motion & Optical Flow Motion Fields, Optical Flow, Lucas-Kanade, KLT Tracking
Exercise 6 3D Reconstruction, Eight-point algorithm, RANSAC, Triangulation
3D Reconstruction IV Structure from Motion, Projective Ambiguity, Affine SfM, Factorization, Projective SfM, Euclidean Upgrade, Bundle Adjustment
Repetition -
Exercise Exam -
1st Exam (AH IV+V)
Einsicht/Inspection 1st Exam (UMIC 025)
2nd Exam (AH IV+V)
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