IAPR

Tutorials on topics in 2D image analysis, computer vision


Online Written Course Notes

  1. General
    1. Dealing with Imprecise Spatial Information in Cognitive Vision (Isabelle Bloch)
    2. Neural nets in Vision (Roger Boyle, David Hogg)
    3. Vision Systems (A D Marshall)
    4. Vision Computacional (L. E. Sucar and G. Gomez - in Spanish)
    5. Vision Through Optimization (N. A. Thacker and T. F. Cootes)
    6. Computer Vision Online Course (Andrew Wallace)
    7. Textured Motion and Complex Motion Modeling (Yizhou Wang)
  2. Physics/Devices
    1. High dynamic range imaging for digital still camera: an overview (S. Battiato, A. Castorina, M. Mancuso)
    2. Tsai Camera Calibration Method Revisited (Interior Orientation) (Berthold K. P. Horn)
    3. Observations on the physics of imaging and image coding (Julio Marten)
  3. Representation/recognition
    1. Object Categorization (Axel Pinz)
  4. Mathematics/Geometry
    1. An Introduction to Projective Geometry (for computer vision) (Stan Birchfield)
    2. Tutorial on Rectification of Stereo Images (Andrea Fusiello)
    3. The Essential Matrix ... (Coplanarity Condition) (Berthold K. P. Horn)
    4. Quaternions and Rotation (Berthold K. P. Horn)
    5. Resources for Discrete Geometry (IAPR TC 18)
    6. Digital Geometry and Mathematical Morphology (Christer Kiselman)
    7. Linear inverse problems: A discrete presentation (Ali Mohammad-Djafari)
    8. Transforme de Radon et ses applications (French) (Ali Mohammad-Djafari)
    9. Inference bayesienne pour les problemes inverses (French) (Ali Mohammad-Djafari)
    10. Detection-Estimation (Ali Mohammad-Djafari)
    11. Problemes inverses (French) (Ali Mohammad-Djafari)
    12. Projective Geometry for Image Analysis (Roger Mohr, Bill Triggs)
    13. Monte Carlo methods (Jonathan Pengelly)
    14. Visual 3D Modeling from Images (Marc Pollefeys)
    15. Principal Components Analysis (Lindsay Smith)
    16. Fourier Analysis (Yerin Yoo)
    17. Parameter Estimation Techniques: A Tutorial with Application to Conic Fitting (Zhengyou Zhang)
    18. Geometric Framework for Vision I: Single View and Two-View Geometry (Andrew Zisserman)
  5. Image processing/analysis
    1. Fixed Flow (Constant Optical Flow) (Berthold K. P. Horn)
    2. Optical Flow with Fixed Translation and Rotation (Berthold K. P. Horn)
    3. Principles for automatic scale selection (Tony Lindeberg)
    4. Automatic scale selection as a pre-processing stage for interpreting the visual world (Tony Lindeberg)
    5. Scale-space theory: A basic tool for analysing structures at different scales (Tony Lindeberg)
    6. A Gentle Introduction to Bilateral Filtering and its Applications (Sylvain Paris, Pierre Kornprobst, Jack Tumblin, and Frédo Durand )
  6. Applications
    1. Particle Filtering for Visual Tracking (Pedram Azad)
    2. Spatial Augmented Reality (Oliver Bimber, Ramesh Raskar, Masahiko Inami)
    3. A Tutorial on CAPTCHA - Completely Automated Public Turing test to tell Computers and Humans Apart (Theo Pavlidis)
    4. Modern Techniques In Remote Sensing (Maria Petrou)
    5. Supervised Neural Networks in Machine Vision (Neil Thacker)
    6. Performance Characterisation in Computer Vision: The Role of Statistics in Testing and Design (Neil Thacker)
    7. An Empirical Design Methodology for the Construction of Machine Vision Systems (Neil Thacker)
    8. Performance Characterisation in Computer Vision: A Guide to Best Practices (Neil Thacker)
    9. New Trends in 3D Video (Christian Theobalt, Stephan Wuermlin, Edilson de Aguiar, Christoph Niederberger)
    10. Introduction to Computer Vision from Automatic Face Analysis Viewpoint (Erno Makinen)

Online Tutorial PPT/PDF Slides

  1. General
    1. The Monogenic Framework: A Systematic Approach to Image Processing and Computer Vision (Michael Felsberg)
    2. Introduction to some aspects of biological vision (Li Zhaoping)
    3. Visual Recognition in Primates and Machines (Tomaso Poggio, Thomas Serre)
    4. Computational Photography (Ramesh Raskar, Jack Tumblin)
    5. Tutorial on Coded Light Projection Techniques (Joaquim Salvi, Jordi Pagis)
    6. The business case for implementing machine vision (PPT) - selecting a vision system from a management perspective (Nello Zuech)
  2. Physics/Devices
    1. Optics (George Barbastathis)
    2. Workshop on Diffusion Tensor Imaging (National Alliance for Medical Image Computing)
    3. Spectral color imaging (Jussi Parkkinen)
    4. Spectral image applications (Jussi Parkkinen)
    5. 8 lectures on color (Jussi Parkkinen)
  3. Representation/recognition
    1. Recognizing and Learning Object Categories (Li Fei-Fei, Rob Fergus, Antonio Torralba)
    2. Object Recognition (Li Fei-Fei)
    3. Geometric Model Acquisition (PPT) (Steve Maybank)
    4. Probabilisitic models of visual object categories (Andrew Zisserman)
    5. Template Matching Techniques in Computer Vision (Roberto Brunelli)
  4. Mathematics/Geometry
    1. Gradient Domain Manipulation Techniques in Vision and Graphics (Amit Agrawal, Ramesh Raskar)
    2. Bayesian Techniques in Vision and Perception (Olivier Aycard and Luis Enrique Sucar)
    3. Graph Cuts (Andrew Blake)
    4. Graph-Cuts versus Level-Sets (Yuri Boykov, Daniel Cremers, Vladimir Kolmogorov )
    5. Bayesian Methods for Multimedia Signal Processing (A. Taylan Cemgil)
    6. Clustering and Robust Techniques in Computer Vision (Per-Erik Forssen)
    7. Deep Belief Nets (Geoffrey Hinton)
    8. Lectures on Discrete Geometry (IAPR TC 18)
    9. Hidden Markov Models (Philip Jackson)
    10. Discrete Optimization in Computer Vision (Nikos Komodakis, Philip Torr, Vladimir Kolmogorov, Yuri Boykov)
    11. Hidden Markov Models (PPT) (Dimitrios Makris)
    12. Bayes Optimality in Pattern Recognition (Aleix Martinez)
    13. Stereo Vision: Algorithms and Applications (Stefano Mattoccia)
    14. Tensor Voting: A Perceptual Organization Approach to Computer Vision and Machine Learning (Philippos Mordohai)
    15. Tensor Methods for Machine Learning, Computer Vision, and Computer Graphics (Amnon Shashua)
  5. Image processing/analysis
    1. Graph Based Image Segmentation (Jianbo Shi, Charless Fowlkes, David Martin, Eitan Sharon)
    2. Lectures on Image Processing (Richard Peters)
    3. Texture (Mike Chantler)
  6. Applications
    1. Kernel Methods in Remote Sensing: Introduction, Applications and Research Opportunities (Gustavo Camps-Valls)
    2. Visual servoing (Francois Chaumette)
    3. Image Processing in Biomedical Applications (S. Colantonio, D. Moroni, O. Salvetti)
    4. Tutorial Medical Image Retrieval (Thomas Deselaers, Henning Muller)
    5. Iterative Methods for Image Reconstruction (Jeffrey Fessler)
    6. Computer Vision for Augmented Reality (Jan-Michael Frahm)
    7. 3D Camera Tracking, Reconstruction and View Synthesis at Interactive Frame Rates (Jan-Michael Frahm, Reinhard Koch, Jan-Friso Evers-Senne)
    8. Integration of Vision and Graphics, with Sports Applications part 1, part 2 (Oliver Grau)
    9. Improving Image Quality in Image Compositing (Mark Grundland)
    10. Open-source Insight Toolkit (ITK) for medical image segmentation and registration (Luis Ibanez, Lydia Ng, Joshua Cates, Stephen Aylward, Bill Lorensen, Julien Jomier)
    11. Face Detection and Recognition using Machine Learning (Sebastien Marcel)
    12. Face detection and recognition (Sebastien Marcel)
    13. Face Recognition (Sebastien Marcel)
    14. Computer Vision for Wearable Visual Interface (Walterio Mayol, Takeshi Kurata)
    15. Animate Vision concepts for image retrieval (Vincenzo Moscato)
    16. Spatio-temporal Data Mining (Shashi Shekhar)
    17. Multimedia Information Retrieval (Alan Smeaton)
    18. Multimedia Information Retrieval Evaluation Initiatives (Alan Smeaton)
    19. Designing multi-scale algorithms medical image analysis (Bart M. ter Haar Romeny)
    20. Biometrics for Surveillance (S. Kevin Zhou, Rama Chellappa)

Online Video Tutorials

  1. Graph Cuts (Andrew Blake)
  2. Graph Cuts (Shorter) (Andrew Blake)
  3. Visual localization and tracking (Andrew Blake)
  4. Advanced Vision Course (Bob Fisher)
  5. Introduction to Vision and Robotics Course (Bob Fisher)
  6. Learning with spectral representations and use of MDL principles (Edwin Hancock )
  7. Probabilistic Relaxation Labeling by Fokker-Planck Diffusion on a Graph (Edwin Hancock )
  8. Graph Spectral Image Smoothing (Edwin Hancock )
  9. Videolectures->Computer Science->Computer Vision (videolectures.net)

Return to Student/Researcher Resource page


Valid XHTML 1.0!

© 2008 Robert Fisher