000 | 03590nam a2200505 i 4500 | ||
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001 | 6267417 | ||
003 | IEEE | ||
005 | 20190220121647.0 | ||
006 | m o d | ||
007 | cr |n||||||||| | ||
008 | 151223s1990 maua ob 001 eng d | ||
020 |
_a9780262280372 _qebook |
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020 |
_z0585354049 _qelectronic |
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020 |
_z9780585354040 _qelectronic |
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020 |
_z026228037X _qelectronic |
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020 |
_z9780262528160 _qprint |
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035 | _a(CaBNVSL)mat06267417 | ||
035 | _a(IDAMS)0b000064818b4411 | ||
040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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050 | 4 |
_aTA1635 _b.M87 1990eb |
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100 | 1 |
_aMurray, David W., _eauthor. |
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245 | 1 | 0 |
_aExperiments in the machine interpretation of visual motion / _cDavid W. Murray and Bernard F. Buxton. |
264 | 1 |
_aCambridge, Massachusetts : _bMIT Press, _cc1990. |
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264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[1990] |
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300 |
_a1 PDF (236 pages) : _billustrations. |
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336 |
_atext _2rdacontent |
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337 |
_aelectronic _2isbdmedia |
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338 |
_aonline resource _2rdacarrier |
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490 | 1 | _aArtificial intelligence series | |
504 | _aIncludes bibliographical references (p. 215-229) and index. | ||
506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
520 | _aIf robots are to act intelligently in everyday environments, they must have a perception of motion and its consequences. This book describes experimental advances made in the interpretation of visual motion over the last few years that have moved researchers closer to emulating the way in which we recover information about the surrounding world. It describes algorithms that form a complete, implemented, and tested system developed by the authors to measure two-dimensional motion in an image sequence, then to compute three-dimensional structure and motion, and finally to recognize the moving objects.The authors develop algorithms to interpret visual motion around four principal constraints. The first and simplest allows the scene structure to be recovered on a pointwise basis. The second constrains the scene to a set of connected straight edges. The third makes the transition between edge and surface representations by demanding that the wireframe recovered is strictly polyhedral. And the final constraint assumes that the scene is comprised of planar surfaces, and recovers them directly.David W. Murray is University Lecturer in Engineering Science at the University of Oxford and Draper's Fellow in Robotics at St Anne's College, Oxford. Bernard F. Buxton is Senior Research Fellow at the General Electric Company's Hirst Research Centre, Wembley, UK, where he leads the Computer Vision Group in the Long Range Research Laboratory.Contents: Image, Scene, and Motion. Computing Image Motion. Structure from Motion of Points. The Structure and Motion of Edges. From Edges to Surfaces. Structure and Motion of Planes. Visual Motion Segmentation. Matching to Edge Models. Matching to Planar Surfaces. | ||
530 | _aAlso available in print. | ||
538 | _aMode of access: World Wide Web | ||
588 | _aDescription based on PDF viewed 12/23/2015. | ||
650 | 0 | _aMotion perception (Vision) | |
650 | 0 | _aComputer vision. | |
655 | 0 | _aElectronic books. | |
700 | 1 | _aBuxton, Bernard F. | |
710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. |
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710 | 2 |
_aMIT Press, _epublisher. |
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776 | 0 | 8 |
_iPrint version _z9780262528160 |
830 | 0 | _aArtificial intelligence (Cambridge, Mass.) | |
856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267417 |
999 |
_c39330 _d39330 |