Titlebook: Statistical Methods in Video Processing; ECCV 2004 Workshop S Dorin Comaniciu,Rudolf Mester,David Suter Conference proceedings 2004 Springe

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Combining Simple Models to Approximate Complex Dynamics either parametric or sample-based approaches. We show that if redundant representations are available, the individual state estimates may be improved by combining simpler dynamical systems, each of which captures some aspect of the complex behavior. For example, human body parts may be robustly tra

Eviction

Online Adaptive Gaussian Mixture Learning for Video Applicationser slow in learning or computationally and storage inefficient. Our solution is derived based on sufficient statistics of the short-term distribution. To avoid unnecessary computation or storage, we show that the equivalent estimates can be accomplished by a set of recursive parameter update equatio

半圆凿

Novelty Detection in Image Sequences with Dynamic Backgroundand snow. Novelty detection is the problem of classifying new observations from previous samples, as either novel or belonging to the background class. An adaptive background model, based on a linear PCA model in combination with local, spatial transformations, allows us to robustly model a variety

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A Framework for Foreground Detection in Complex Environments procedures into a unified probability framework by considering the spatial, spectral and temporal information of pixels to model different complex backgrounds. Firstly, a Bayesian framework, which combines the prior distribution of the pixel’s features and the likelihood probability with a homogene

流逝

Eviction

滔滔不绝地讲

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