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In this paper, we present a generic watermarking framework that employs perceptual modeling to embed a watermark in such a way that it cannot be perceived. Our approach consists in building computational models which take into account the most common properties of the HVS (Human Visual System) that can be exploited for watermarking. We describe two ways a perceptual model can be incorporated into...
In this paper, a memory-based Gaussian Mixture Model (MGMM) is proposed inspired by the way human perceives the environment. The human memory mechanism is introduced to model the background, which can make the model remember what the scene has ever been and help the model adapt to the variation of the scene more quickly. Experimental results show the effect of the memory mechanism in segmenting moving...
Gaussian Mixture Model (GMM) is one of the best models for modeling a background scene with gradual changes and repetitive motions. However, it fails in segmenting moving objects when the scene changes sharply. To handle this problem, an improved background modeling algorithm-Intelligent GMM (IGMM), which is inspired by the way human perceive the environment to tackle sharp changes in the scene, is...
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