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PRODID:IEEE vTools.Events//EN
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TZID:Europe/Warsaw
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DTSTART:20200329T030000
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BEGIN:STANDARD
DTSTART:20191027T020000
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BEGIN:VEVENT
DTSTAMP:20200120T101213Z
UID:97399C7D-7393-4721-93E1-AC67A848BB41
DTSTART;TZID=Europe/Warsaw:20191217T131500
DTEND;TZID=Europe/Warsaw:20191217T150000
DESCRIPTION:As humans we perform remarkably well in utilising our sense of 
 vision in such tasks as navigating in complex environments\, categorising 
 objects - even objects we have not seen before\, interacting with dynamica
 lly evolving surroundings or indeed predicting complex behaviours. We can 
 do all of this\, apparently with ease\, in an instant. Of course\, as huma
 ns we take these faculties for granted\, but it has proved extremely chall
 enging to reproduce these on machines.\n\nWith continued advances in mathe
 matical modelling\, ever increasing computational power and the recent unp
 recedented proliferation of shared information (e.g. with reported hundred
 s of hours of video uploaded to the YouTube servers every minute) or creat
 ion of large databases\, most notably in bioimaging\, machine learning has
  had in the recent years a profound impact on visual information processin
 g with many of the difficult vision problems successfully solved using the
  machine learning approaches.\n\nThe talk will introduce the field of comp
 uter vision\, including a small number of practical implementation example
 s to succinctly illustrate the key computer vision and machine learning co
 ncepts.\n\nCo-sponsored by: Wroclaw University of Science and Technology\n
 \nSpeaker(s): Bogdan Matuszewski\, \n\nRoom: 409\, Bldg: C5\, ul. Wyb. Wys
 pianskiego 27\, Wroclaw\, Dolnoslaskie\, Poland
LOCATION:Room: 409\, Bldg: C5\, ul. Wyb. Wyspianskiego 27\, Wroclaw\, Dolno
 slaskie\, Poland
ORGANIZER:boguslaw.b.szlachetko@ieee.org
SEQUENCE:1
SUMMARY:Visual Information Processing
URL;VALUE=URI:https://events.vtools.ieee.org/m/214850
X-ALT-DESC:Description: <br /><p>As humans we perform remarkably well in ut
 ilising our sense of vision in such tasks as navigating in complex environ
 ments\, categorising objects - even objects we have not seen before\, inte
 racting with dynamically evolving surroundings or indeed predicting comple
 x behaviours.&nbsp\; We can do all of this\, apparently with ease\, in an 
 instant.&nbsp\; Of course\, as humans we take these faculties for granted\
 , but it has proved extremely challenging to reproduce these on machines.&
 nbsp\;</p>\n<p>With continued advances in mathematical modelling\, ever in
 creasing computational power and the recent unprecedented proliferation of
  shared information (e.g. with reported hundreds of hours of video uploade
 d to the YouTube servers every minute) or creation of large databases\, mo
 st notably in bioimaging\, machine learning has had in the recent years a 
 profound impact on visual information processing with many of the difficul
 t vision problems successfully solved using the machine learning approache
 s.</p>\n<p>The talk will introduce the field of computer vision\, includin
 g a small number of practical implementation examples to succinctly illust
 rate the key computer vision and machine learning concepts.</p>
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