000			02655nam a22003618i 4500
001			CR9780511526657
003			UkCbUP
005			20200124160238.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			090407s1995||||enk o ||1 0|eng|d
020			_a9780511526657 (ebook)
020			_z9780521550635 (hardback)
020			_z9780521019781 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aTA1637 _b.S45 1995
082	0	0	_a006.4/2 _220
100	1		_aShapiro, Larry S., _eauthor.
245	1	0	_aAffine analysis of image sequences / _cLarry S. Shapiro.
264		1	_aCambridge : _bCambridge University Press, _c1995.
300			_a1 online resource (xi, 210 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aDistinguished dissertations in computer science
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
520			_aComputer vision is a rapidly growing field which aims to make computers 'see' as effectively as humans. In this book Dr Shapiro presents a new computer vision framework for interpreting time-varying imagery. This is an important task, since movement reveals valuable information about the environment. The fully-automated system operates on long, monocular image sequences containing multiple, independently-moving objects, and demonstrates the practical feasibility of recovering scene structure and motion in a bottom-up fashion. Real and synthetic examples are given throughout, with particular emphasis on image coding applications. Novel theory is derived in the context of the affine camera, a generalisation of the familiar scaled orthographic model. Analysis proceeds by tracking 'corner features' through successive frames and grouping the resulting trajectories into rigid objects using new clustering and outlier rejection techniques. The three-dimensional motion parameters are then computed via 'affine epipolar geometry', and 'affine structure' is used to generate alternative views of the object and fill in partial views. The use of all available features (over multiple frames) and the incorporation of statistical noise properties substantially improves existing algorithms, giving greater reliability and reduced noise sensitivity.
650		0	_aImage processing _xMathematical models _xData processing.
650		0	_aGeometry, Affine _xData processing.
776	0	8	_iPrint version: _z9780521550635
830		0	_aDistinguished dissertations in computer science.
856	4	0	_uhttps://doi.org/10.1017/CBO9780511526657
999			_c518304 _d518302