000 03508nam a22005175i 4500
001 978-3-642-24834-4
003 DE-He213
005 20140220083304.0
007 cr nn 008mamaa
008 111108s2012 gw | s |||| 0|eng d
020 _a9783642248344
_9978-3-642-24834-4
024 7 _a10.1007/978-3-642-24834-4
_2doi
050 4 _aTJ210.2-211.495
050 4 _aT59.5
072 7 _aTJFM1
_2bicssc
072 7 _aTEC037000
_2bisacsh
072 7 _aTEC004000
_2bisacsh
082 0 4 _a629.892
_223
100 1 _aCivera, Javier.
_eauthor.
245 1 0 _aStructure from Motion using the Extended Kalman Filter
_h[electronic resource] /
_cby Javier Civera, Andrew J. Davison, José María Martínez Montiel.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2012.
300 _aXVI, 172 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Tracts in Advanced Robotics,
_x1610-7438 ;
_v75
505 0 _aIntroduction -- Points at Infinity. Mosaics using the Extended Kalman Filter -- Inverse Depth Parametrization -- 1-Point RANSAC -- Degenerate Camera Motions and Model Selection -- Self-calibration -- Conclusions.
520 _aThe fully automated estimation of the 6 degrees of freedom camera motion and the imaged 3D scenario using as the only input the pictures taken by the camera has been a long term aim in the computer vision community. The associated line of research has been known as Structure from Motion (SfM). An intense research effort during the latest decades has produced spectacular advances; the topic has reached a consistent state of maturity and most of its aspects are well known nowadays. 3D vision has immediate applications in many and diverse fields like robotics, videogames and augmented reality; and technological transfer is starting to be a reality. This book describes one of the first systems for sparse point-based 3D reconstruction and egomotion estimation from an image sequence; able to run in real-time at video frame rate and assuming quite weak prior knowledge about camera calibration, motion or scene. Its chapters unify the current perspectives of the robotics and computer vision communities on the 3D vision topic: As usual in robotics sensing, the explicit estimation and propagation of the uncertainty hold a central role in the sequential video processing and is shown to boost the efficiency and performance of the 3D estimation. On the other hand, some of the most relevant topics discussed in SfM by the computer vision scientists are addressed under this probabilistic filtering scheme; namely projective models, spurious rejection, model selection and self-calibration.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aComputer vision.
650 1 4 _aEngineering.
650 2 4 _aRobotics and Automation.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aImage Processing and Computer Vision.
700 1 _aDavison, Andrew J.
_eauthor.
700 1 _aMartínez Montiel, José María.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642248337
830 0 _aSpringer Tracts in Advanced Robotics,
_x1610-7438 ;
_v75
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-24834-4
912 _aZDB-2-ENG
999 _c102349
_d102349