BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Australia/Brisbane BEGIN:STANDARD DTSTART:19920301T020000 TZOFFSETFROM:+1100 TZOFFSETTO:+1000 TZNAME:AEST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20150730T230610Z UID:413750E7-1AE7-11E5-A923-0050568D2FB3 DTSTART;TZID=Australia/Brisbane:20150818T180000 DTEND;TZID=Australia/Brisbane:20150818T200000 DESCRIPTION:This month's paper is:\n\nRandom sample consensus: a paradigm f or model fitting with applications to image analysis and automated cartogr aphy\n\nRandom Sample Consensus: A Paradigm for Model Fitting with Apphcat lons to Image Analysis and Automated Cartography (i.e.\, the original RANS AC paper)\, Martin A. Fischler and Robert C.\, Communications of the ACM\, June 1981\, Volume 24\, Number 6.\n\nGary Einicke has kindly agreed to le ad the discussion and the venue will again be the Brisbane Brewhouse (see address details & map below).\n\n*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 Clu b"). Each month\, the participants would agree on a highly cited\, 'top te n' 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 w ith his/her own appraisal. In this way\, it is hoped that we could all bro aden our understanding of the field and further develop a sense of communi ty. 1st rule of 1st Tuesday Journal-Paper Club: tell everyone about 1st Tu esday Journal-Paper Club.\n\nSpeaker(s): \, \, \, \n\nAgenda: \nAbstract\n \nA new paradigm\, Random Sample Consensus (RANSAC)\, for fitting a model to experimental data is introduced. RANSACis capable of interpreting/ smoo thing data containing a significant percentage of gross errors\, and is th us ideally suited for applications in automated image analysis where inter pretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSACto the Lo cation Determination Problem (LDP): Given an image depicting a set of land marks with known locations\, determine that point in space from which the image was obtained. In response to a RANSACrequirement\, new results are d erived on the minimum number of landmarks needed to obtain a solution\, an d algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system th at can solve the LDP under difficult viewing and analysis conditions. Impl ementation details and computational examples are also presented.\n\nBldg: Brewhouse Brisbane\, 601 Stanley St.\, Woolloongabba\, Queensland\, Austr alia\, 4102 LOCATION:Bldg: Brewhouse Brisbane\, 601 Stanley St.\, Woolloongabba\, Queen sland\, Australia\, 4102 ORGANIZER:a.bradley@itee.uq.edu.au SEQUENCE:2 SUMMARY:1st Tuesday Journal-Paper Club: August 2015 meeting URL;VALUE=URI:https://events.vtools.ieee.org/m/35110 X-ALT-DESC:Description: <br /><p>This month's paper is:</p>\n<p>Random samp le consensus: a paradigm for model fitting with applications to image anal ysis and automated cartography</p>\n<div class="page" title="Page 1">\n<di v class="layoutArea">\n<div class="column">\n<p>Random Sample Consensus: A Paradigm for Model Fitting with Apphcatlons to Image Analysis and Automat ed Cartography (i.e.\, the original RANSAC paper)\, Martin A. Fischler and Robert C.\, Communications of the ACM\, June 1981\, Volume 24\, Number 6. </p>\n<p>&nbsp\;</p>\n</div>\n</div>\n</div>\n<p>&nbsp\;</p>\n<p>Gary Eini cke has kindly agreed to lead the discussion and the venue will again be t he Brisbane Brewhouse (see address details &amp\; map below).</p>\n<p>*Abo ut the 1st Tuesday Journal-Paper Club:* the idea is to meet regularly\, us ually on the 1st Tuesday of the month as the name suggests (inspired by th e ABC TV series "1st Tuesday Book Club"). Each month\, the participants wo uld 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 rea d the article. At the next meeting\, the Discussion Leader would lead a di scussion of that article\, starting with his/her own appraisal. In this wa y\, it is hoped that we could all broaden our understanding of the field a nd further develop a sense of community. 1st rule of 1st Tuesday Journal-P aper Club: tell everyone about 1st Tuesday Journal-Paper Club.</p>\n<p>&nb sp\;</p>\n<p>&nbsp\;</p><br /><br />Agenda: <br /><p>Abstract&nbsp\;</p>\n <div class="page" title="Page 1">\n<div class="section">\n<div class="layo utArea">\n<div class="column">\n<p>A new paradigm\, Random Sample Consensu s (RANSAC)\, for fitting a model to experimental data is introduced. RANSA Cis capable of interpreting/ smoothing data containing a significant perce ntage of gross errors\, and is thus ideally suited for applications in aut omated image analysis where interpretation is based on the data provided b y error-prone feature detectors. A major portion of this paper describes t he application of RANSACto the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations\, determine th at point in space from which the image was obtained. In response to a RANS ACrequirement\, 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 view ing and analysis conditions. Implementation details and computational exam ples are also presented.</p>\n<div class="page" title="Page 1">&nbsp\;</di v>\n<br />\n<p>&nbsp\;</p>\n</div>\n</div>\n</div>\n</div>\n<p>&nbsp\;</p> \n<p>&nbsp\;</p>\n<p>&nbsp\;</p> END:VEVENT END:VCALENDAR