1st Tuesday Journal-Paper Club: August 2015 meeting
This month's paper is:
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
Random Sample Consensus: A Paradigm for Model Fitting with Apphcatlons to Image Analysis and Automated Cartography (i.e., the original RANSAC paper), Martin A. Fischler and Robert C., Communications of the ACM, June 1981, Volume 24, Number 6.
Gary Einicke has kindly agreed to lead the discussion and the venue will again be the Brisbane Brewhouse (see address details & map below).
*About the 1st Tuesday Journal-Paper Club:* the idea is to meet regularly, usually on the 1st Tuesday of the month as the name suggests (inspired by the ABC TV series "1st Tuesday Book Club"). Each month, the participants would agree on a highly cited, 'top ten' or major-prize-winning article in an SPS or ComSoc journal (but not one of our own!). We would also select a Discussion Leader. Through the month, each of the participants would read the article. At the next meeting, the Discussion Leader would lead a discussion of that article, starting with his/her own appraisal. In this way, it is hoped that we could all broaden our understanding of the field and further develop a sense of community. 1st rule of 1st Tuesday Journal-Paper Club: tell everyone about 1st Tuesday Journal-Paper Club.
Date and Time
Location
Hosts
Registration
- Date: 18 Aug 2015
- Time: 06:00 PM to 08:00 PM
- All times are (UTC+10:00) Brisbane
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- 601 Stanley St.
- Woolloongabba, Queensland
- Australia 4102
- Building: Brewhouse Brisbane
- Starts 25 June 2015 06:00 AM
- Ends 18 August 2015 12:00 PM
- All times are (UTC+10:00) Brisbane
- No Admission Charge
Agenda
Abstract
A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSACis capable of interpreting/ smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSACto the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSACrequirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing and analysis conditions. Implementation details and computational examples are also presented.