Lunds universitet

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All papers will be presented as orals. Each paper is assigned 20 minutes (15 min presentation + 5 min questions).


10:15    PhD student Day

17:30    Registration

18:00    Get Together


8:20      Registration

8:50      Welcome

9:00      Session 1:  Computer Vision and Applications (4 talks)

10:20    Coffee

10:50    Session 2a: Tracking and SLAM (6 talks)

             Session 2b: Medical Image Analysis (6 talks)

13:00    Lunch

14:30    Industrial Session 1 (4 talks)

15:50    Coffee

16:30    Annual Meeting of the SSBA

19:00    Symposium Dinner


8:30      Session 3: Correlation Methods in Computer Vision (3 talks)

9:30      Invited speaker: Christian Igel, DIKU.

10:30    Coffee              

11:00    Session 4a: Image Processing and Analysis (5 talks)

             Session 4b: Pattern Recognition and Machine Learning (5 talks)

12:40    Lunch

13:40    Industrial Session 2 (4 talks)

15:00    Farewell and Coffee

A detailed program can be found here.

Invited talk

Machine Learning Meets Image Analysis: From looking inside ourselves to gazing at the stars

Machine learning (ML) plays an increasing role in image analysis.
This talk presents recent examples from medical imaging and astronomy,
ranging from applying standard ML algorithms to hand-crafted image features,
over supervised feature learning using deep neural networks, to unsupervised
image categorization.

About the invited speaker

Christian Igel,
Professor and Dr. habil.
University of Copenhagen
Department of Computer Science (DIKU)

C. Igel studied Computer Science at the Technical University of Dortmund, Germany and received his Doctoral degree from the Faculty of Technology, Bielefeld University in 2002. From 2003 to 2010, he was a Junior professor for Optimization of Adaptive Systems at the Institut für Neuroinformatik, Ruhr-University Bochum. In October 2010, C. Igel was appointed professor with special duties in machine learning at DIKU. Since December 2014 he is full professor at DIKU. Among his main research interests are support vector machines and other kernel-based methods, as well as stochastic neural networks and undirected graphical models.


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