The goal of improving the performance of computer vision systems has brought new challenges to the field of machine learning, for example, learning from structured descriptions, partial information, incremental learning, focusing attention or learning regions of interests (ROI), learning with many classes. Solving problems in visual domains will result in the development of new, more robust machine learning algorithms that will be able to work in more realistic settings.
From the standpoint of computer vision systems, machine learning can offer effective methods for automating the acquisition of visual models, adapting task parameters and representation, transforming signals to symbols, building trainable image processing systems, focusing attention on target object and learning when to apply what algorithm in a vision system.
From the standpoint of machine learning systems, computer vision can
provide interesting and challenging problems for example: learning models
rather than hand-crafting them, learning to transfer experience gained
in one application domain to another domain, learning from large sets of
images with no annotation, designing evaluation criteria for the quality
of learning processes in computer vision systems.
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9:15 - 9:30am | Welcome and registration | - |
9:30 - 10:00am | Paper 1:
Recent Progress on RAIL: Automating Clustering and Comparison of Multiple Classification Techniques on High Resolution Remotely Sensed Imagery [pdf] |
A. Chen
School of Computer Science and Engineering, University of New South Wales |
10:00 - 10:30am | Paper 2:
Boosted Image Classification: An Empirical Study [pdf] |
N. R. Howe Smith College |
10:30 - 11:00am | Coffee break | - |
11:00 - 11:30am | Paper 3:
Signal Discrimination in Fluorescence In Situ Hybridization Images [pdf] |
B. Lerner Department of Electrical and Computer Engineering, Ben-Gurion University |
11:30 - 12:00pm | Paper 4:
Combining Wrapper and Filter Approaches for Learning Concepts from Images provided by a Mobile Robot [pdf] |
N. Bredeche
LIMSI-University |
12:00 - 2:00pm | Lunch break | - |
2:00 - 2:30pm | Paper 5:
Automatic Feature Construction and a Simple Rule Induction Algorithm for Skin Detection [pdf] |
G.Gomez
ITESM - Campus Cuernavaca |
2:30 - 3:00pm | Paper 6:
A Statistical Approach To Texture Description of Medical Images: A Preliminary Study [pdf] |
M. Bevk
Faculty of Computer and Information Science, University of Ljubljana |
3:00 - 3:30pm | Paper 7:Learning to Recognize Objects - Toward Automatic Calibration
of Colour Vision
for Sony Robots [pdf] |
T. Zrimec
Centre for Health Informatics & SCE University of New South Wales |
3:30 - 4:00pm | Coffee break | - |
4:00 - 5:00pm | Panel discussion | - |
Tatjana Zrimec
University of New South Wales
NSW 2052, AUSTRALIA
E-mail
tatjana@cse.unsw.edu.au