Titlebook: Computer Vision -- ACCV 2010 Workshops; ACCV 2010 Internatio Reinhard Koch,Fay Huang Conference proceedings 2011 Springer-Verlag GmbH Berli

薄膜

胎兒

不透氣

vocation

FLIP

: Learning Object Displacementspecific object and calculate its motion field. The resulting motion representation is useful for a variety of visual applications (e.g., scene description, object tracking, action recognition) and it cannot be directly obtained using the existing methods.

obviate

canonical

0302-9743 cted and reviewed papers presented at the eight workshops that were held in conjunction with the 10th Asian Conference on Computer Vision, in Queenstown, New Zealand, in November 2010..From a total of 167 submissions to all workshops, 89 papers were selected for publication. The contributions are gr

裂隙

NAG

Bayesian Loop for Synergistic Change Detection and Trackingmework whereby both processes can virtuously influence each other according to a Bayesian loop: change detection provides a completely specified observation likelihood to the tracker and the tracker provides an informative prior to the change detector.

occurrence

Determining Spatial Motion Directly from Normal Flow Field: A Comprehensive Treatmentnts itself as a system of linear inequalities to bind the motion parameters; the second one uses the rotation magnitude’s globality to all image positions to constrain the motion parameters further. A two-stage iterative process in a coarse-to-fine framework is used to exploit the two constraints. E