Results for: robust methods
0387351558
2009-02-14 - extension: zip - size: 5 MB
0387351558
Advanced
Robust and Nonparametric
Methods in Efficiency Analysis: Methodology and Applications
Hosted on: rapidshare.com
Video results for: robust methodsMore results from video
Head pose recovery to slave wearable robot Here we consider the control of the active camera from head movements. In the context of (More) Here we consider the control of the active camera from head movements. In the context of teleoperation, these may be the head movements of a remote operator, perhaps acting as the wearer's assistant. The movements are likely to be larger than those in video-conference applications, and so frontal facial features are insufficient. We use a model which incrementally combines a fixed 3D shape model with specific features found on the observed head. Robust methods, including the incorporation of a colour model, are used to mitigate the effect of mismatching, the main contribution being the use of both interest point and colour features within a single random-sampling framework. (Less)
Robust Video Stabilizer: Moving Car Particle filters have been introduced as a powerful tool to estimate the posterior density of (More) Particle filters have been introduced as a powerful tool to estimate the posterior density of nonlinear systems. These filters are also capable of processing data online as required in many practical applications. In this system, we propose a novel technique for video stabilization based on the particle filtering framework. Scale-invariant feature points are extracted to form a rough estimate which is used to model the importance density. We use a constant-velocity Kalman filter model to estimate intentional camera movement. We also prove that the particle filtering estimate will lower the error variance. The superior performance and robustness of our algorithm is demonstrated by computer simulations [Reference: Junlan Yang, Dan Schonfeld, Chong Chen, and Magdi Mohamed, "Online Video Stabilization Based on Particle Filters", in the IEEE International Conference on Image Processing (ICIP'06), Atlanta, Georgia, October 8-11, 2006]. (Less)
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