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Computer vision has started to achieve some very impressive results over the last 5-10 years. It is now possible to quickly and reliably detect faces, recognize and localize target images, and even classify pictures of objects into generic categories. Unfortunately, knowledge of these techniques remains largely confined to academia. In this session we'll go over some of the tools available, placing an emphasis on exploring the ideas and algorithms behind their design.
To show how these components can be put together, a sample system will be developed over the course of the presentation. Starting with standard image descriptors, we'll first see how to do direct image recognition. We'll then extend that into a simple object classifier, which will be able to distinguish (for example) between images which contain a bicycle and those that don't.
Topics covered will include:
Python code for the sample SIFT-matching and bag-of-words classifier applications can be found "here":http://www.cs.ubc.ca/~mrd/OSB/OSB.zip .
The slides can be found "here":http://www.cs.ubc.ca/~mrd/OSB/OSB09-slides.odp and in PDF format "here":http://www.cs.ubc.ca/~mrd/OSB/OSB09-slides.pdf .
17th–19th June 2009