By Alessandro Lanza, Federico Tombari, Luigi Di Stefano (auth.), Reinhard Koch, Fay Huang (eds.)
The two-volume set LNCS 6468-6469 includes the rigorously chosen and reviewed papers awarded on the 8 workshops that have been held along with the tenth Asian convention on machine imaginative and prescient, in Queenstown, New Zealand, in November 2010.
From a complete of 167 submissions to all workshops, 89 papers have been chosen for booklet. The contributions are grouped jointly in line with the most workshops subject matters, that have been: computational images and aesthetics; desktop imaginative and prescient in car expertise: from Earth to Mars; digital cultural history; subspace established tools; video occasion categorization, tagging and retrieval; visible surveillance; program of computing device imaginative and prescient for combined and augmented reality.
Read or Download Computer Vision – ACCV 2010 Workshops: ACCV 2010 International Workshops, Queenstown, New Zealand, November 8-9, 2010, Revised Selected Papers, Part I PDF
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Additional info for Computer Vision – ACCV 2010 Workshops: ACCV 2010 International Workshops, Queenstown, New Zealand, November 8-9, 2010, Revised Selected Papers, Part I
Dynamic Control of Adaptive Mixture-ofGaussians Background Model. In: CD-ROM Proceedings of IEEE International Conference on Advanced Video and Signal Based Surveillance 2006 (2006) 3. : Background and Foreground Modeling Using Non-parametric Kernel Density Estimation for Visual Surveillance. Proceedings of the IEEE 90, 1151–1163 (2002) 4. : A Fast Algorithm for Adaptive Background Model Construction Using Parzen Density Estimation. In: CDROM Proc. of IEEE International Conference on Advanced Video and Signal based Surveillance (2007) 5.
It Abstract. This paper is aimed at investigating background subtraction based on second-order polynomial models. Recently, preliminary results suggested that quadratic models hold the potential to yield superior performance in handling common disturbance factors, such as noise, sudden illumination changes and variations of camera parameters, with respect to state-of-the-art background subtraction methods. Therefore, based on the formalization of background subtraction as Bayesian regression of a second-order polynomial model, we propose here a thorough theoretical analysis aimed at identifying a family of suitable models and deriving the closed-form solutions of the associated regression problems.
Di Stefano Thanks to this approach, gradual as well as sudden photometric distortions do not yield false positives provided that they are explained by the model. Thus, the main issue concerns the choice of the a priori model: in principle, the more restrictive such a model, the higher is the ability to detect changes (sensitivity) but the lower is robustness to sources of disturbance (speciﬁcity). Some proposals assume disturbance factors to yield linear intensity transformations [4, 5]. Nevertheless, as discussed in , many non-linearities may arise in the image formation process, so that a more liberal model than linear is often required to achieve adequate robustness in practical applications.