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020 _a9783540325345
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024 7 _a10.1007/11676959
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245 1 0 _aSpatial Coherence for Visual Motion Analysis
_h[electronic resource] :
_bFirst International Workshop, SCVMA 2004, Prague, Czech Republic, May 15, 2004, Revised Papers /
_cedited by W. James MacLean.
250 _a1st ed. 2006.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2006.
300 _aIX, 141 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
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490 1 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v3667
505 0 _a2D Motion Description and Contextual Motion Analysis: Issues and New Models -- Structure from Periodic Motion -- 3D SSD Tracking from Uncalibrated Video -- Comparison of Edge-Driven Algorithms for Model-Based Motion Estimation -- On the Relationship Between Image and Motion Segmentation -- Motion Detection Using Wavelet Analysis and Hierarchical Markov Models -- Segregation of Moving Objects Using Elastic Matching -- Local Descriptors for Spatio-temporal Recognition -- A Generative Model of Dense Optical Flow in Layers -- Analysis and Interpretation of Multiple Motions Through Surface Saliency -- Dense Optic Flow with a Bayesian Occlusion Model.
520 _aMotionanalysisisacentralproblemincomputervision,andthepasttwodecades have seen important advances in this ?eld. However, visual motion is still often considered on a pixel-by-pixel basis, even though this ignores the fact that image regions corresponding to a single object usually undergo motion that is highly correlated. Further, it is often of interest to accurately measure the boundaries of moving regions. In the case of articulated motion, especially human motion, discovering motion boundaries is non-trivial but an important task nonetheless. Another related problem is identifying and grouping multiple disconnected - gions moving with similar motions, such as a ?ock of geese. Early approaches focused on measuring motion of either the boundaries or the interior, but s- dom both in unison. For several years now, attempts have been made to include spatial coherence terms into algorithms for 2- and 3-D motion recovery, as well as motion boundary estimation. This volume is a record of papers presented at the First International Wo- shop on Spatial Coherence for Visual Motion Analysis, held May 15th, 2004 in Prague, in conjunction with the European Conference on Computer Vision (LNCS 3021–4). The workshop examined techniques for integrating spatial - herence constraints during motion analysis of image sequences. The papers were revised after the workshop to allow for incorporation of feedback from the workshop.
650 0 _aComputer vision.
650 0 _aImage processing
_xDigital techniques.
650 0 _aPattern recognition systems.
650 0 _aArtificial intelligence.
650 0 _aComputer graphics.
650 0 _aAlgorithms.
650 1 4 _aComputer Vision.
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
650 2 4 _aAutomated Pattern Recognition.
650 2 4 _aArtificial Intelligence.
650 2 4 _aComputer Graphics.
650 2 4 _aAlgorithms.
700 1 _aMacLean, W. James.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540325338
776 0 8 _iPrinted edition:
_z9783540820666
830 0 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v3667
856 4 0 _uhttps://doi.org/10.1007/11676959
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