Pilot
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The PCCV Project:
Benchmarking Vision Systems
Overview
Tutorials
Methodology
Case studies
Test datasets
Our image file format
HATE test harness
Information
General links
Conferences
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Research groups
Societies
Techniques (CVonline)
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Linux on ThinkPad
These web pages provide a comprehensive resource concerning all aspects of performance characterization in computer vision. In this context, performance does not mean how quickly a piece of software runs but how well it performs its task.
There is rather a lot of information here. You can nagivate your way through it using the links to the left; but if you have a particular need, the following links are probably the most appropriate.
- If you're a novice to benchmarking:
Why performance characterization is important
Benchmarking tutorials, especially the first one
Case studies
- If you want to use benchmarking in your work:
Why performance characterization is important
Benchmarking tutorials
The HATE test harness
- If you're interested in benchmarking research:
Examples of the use of benchmarking
In-depth case studies
Current and past events concerning benchmarking
A comprehensive bibliography of relevant publications
- If you're a professor looking for lecture material:
Why performance characterization is important
Benchmarking tutorials
Case studies
The principal aim of this web-site is to show you how to carry out benchmarking exercises yourself, both by providing tutorials that describe the principles and in-depth case studies that illustrate the practice. There are also examples of people's work and a comprehensive bibliography of relevant publications.
We also provide a test harness to automate the testing process. The same test harness also allows you to compare the performances of techniques, both those you have developed yourself and others available on the Internet.
If you're already active in performance characterization, you might be interested in current and past events in the area. You might also like to look at groups whose work encompasses performance characterization and benchmarking.
Our aim is to make these pages as comprehensive as possible, covering all aspect of benchmarking. If you can't find what you're looking for, do pose a question to the Pixel forum that covers benchmarking or contact the maintainers.
The development and maintenance of the PEIPA web-site is currently funded under a European project entitled Performance Characterization in Computer Vision (PCCV). This project was set up by Patrick Courtney while at Visual Automation in Manchester (UK), though it has been managed by Neil Thacker of ISBE, University of Manchester since Patrick moved to Perkin-Elmer. The other partners in PCCV are Henrick Christensen of KTH in Stockholm (Sweden) and Adrian Clark of the University of Essex in Colchester (UK), where these pages reside.
You are also welcome to take a look a the project's latest progress report: 18pp, available as PDF (100 Kbytes), PostScript (3.5 Mbytes).
The PCCV project is funded under the EU's IST programme.