Advanced Computer Vision

Cambridge University 2012 - Module 5F16

 

 

Teachers:

Slides will be online after each lecture:

 

Lecture 1&2     (18.1):  pdf ppt

Lecture 3&4     (25.1):  pdf ppt

Lecture 5&6     (1.2):    pdf ppt

Lecture 7&8     (8.2):    pdf

Lecture 9&10   (15.2):  pdf

Lecture 11&12 (22.2):  pdf ppt

 

(You are welcome to use the slides for presentations. Please give appropriate credits).

 

Time: Lent 2012 (18.1. until 22.2) every Wednesday: 10.00-12.00 

 

Prerequisites: Computer Vision and robotics (4F12) is essential.
                                 Machine Learning (4F13) in Lent 2011is useful but not essential  

Assessment: Projects (Start of Easter term 2012)
                                

Syllabus:

 

Lecture 1&2: - Probabilistic models
                    - Different approaches for learning (probabilistic vs loss-based)
                    - Generative/discriminative models, discriminative functions

Lecture 3&4: - Graphical models
                    - Expressing vision problems as labelling problems
                    - Discrete vs. continuous labels and domain

Lecture 5&6: Optimization: 
                    - Message passing (Factor Graph BP, etc.)
                    - Combinatorial optimization (submodular functions, Graph cuts, etc.)

Lecture 7&8: Optimization:
                    - Transformation schema: multiple labels, higher-order models
                    - Relaxation techniques (LP relaxation)
                    - Move-making techniques

Lecture 9&10: Case Studies:
                     - Kinect Pose estimation [Shotton et al. CVPR ‘10]
                     - Unwrap Mosaics for Video Editing
                     - Hough Transforms for Object Detection

Lecture 11&12: - Comparison of optimization techniques
                       - Case Study: Decision Tree Fields [Nowozin, ICCV ‘11]
                       - Case Study: graph-cut based segmentation with connectivity prior [Vicente et al. CVPR ‘08]
                       - Case Study: tba

 

About the Teachers

Carsten Rother received his Diploma degree with distinction in 1999 at the University of Karlsruhe/Germany. He did his PhD at the Royal Institute of Technology Stockholm/Sweden, supervised by Stefan Carlsson and Jan-Olof Eklundh. Since 2003 he is a researcher at Microsoft Research Cambridge/UK. His research interests are in the field of “Physics-based scene recovery and understanding, in particular Segmentation and Matting, Stereo, Object Recognition”, and in the area of “Vision for Graphics”.  He has published more than 65 articles (H-index 29) at international conferences and journals. He won the best paper honourable mention awards at ACCV ’10, CHI ’07, CVPR ’05 and best paper award at Indian Conference on Computer Vision ‘10. HHe was awarded the DAGM Olympus price 2009. He has influenced various Microsoft products, in particular GrabCut for Office 2010 and AutoCollage. He serves on the program committee of major conferences (e.g. SIGGRAPH, ICCV, ECCV, CVPR, NIPS), and has been area chair for ICCV ’11, ECCV ’12, BMVC ’08-12‘ and DAGM ’10-‘12.

 

 Pushmeet Kohli is a research scientist in the Machine Learning and Perception group at Microsoft Research Cambridge, an associate of the Psychometric Centre and Trinity Hall, University of Cambridge. Pushmeet’s research revolves around Intelligent Systems and Computational Sciences, and he publishes in the fields of Machine Learning, Computer Vision, Information Retrieval, and Game Theory. His current research interests include “human behaviour analysis” and the “prediction of user preferences”. Pushmeet is interested in designing autonomous and intelligent computer vision, bargaining and trading systems which learn by observing and interacting with users on social media sites such as Facebook. He is also investigating the use of new sensors such as KINECT for the problems of human pose estimation, scene understanding and robotics. Pushmeet has won a number of awards and prizes for his research. His PhD thesis, titled "Minimizing Dynamic and Higher Order Energy Functions using Graph Cuts", was the winner of the British Machine Vision Association’s “Sullivan Doctoral Thesis Award”, and was a runner-up for the British Computer Society's “Distinguished Dissertation Award”. Pushmeet’s papers have appeared in SIGGRAPH, NIPS, ICCV, AAAI, CVPR, PAMI, IJCV, CVIU, ICML, AISTATS, AAMAS, UAI, ECCV, and ICVGIP and have won best paper awards in ICVGIP 2006, 2010, ECCV 2010 and ISMAR 2011. His research has also been the subject of a number of articles in popular media outlets such as Forbes, The Economic Times, New Scientist and MIT Technology Review. Pushmeet is a part of the Association for Computing Machinery's (ACM) Distinguished Speaker Program.