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A JND model was applied as a human visual model as previously mentioned in [17].
This paper presents a multi-resolution recognition method for 3D objects, based on the human visual model.
The proposed method is further implemented in wavelet transform domain, where the insensitive wavelet coefficients are selected according to the modified human visual model for watermark embedding.
The local noise sensitivity (boldsymbol {S}^{1}_{2}) of the wavelet subband (boldsymbol {X}^{1}_{2}) is then computed by applying the modified human visual model (HVM) in [18].
where Y i is the watermarked vector tree and β w is a non-adaptive embedding strength because, to the best of our knowledge, no suitable human visual model has been reported in the literature for the situation in our scheme.
(5) Associate the allocated bit e i to w e i and perform the embedding as follows: Y i = T i + β w w e i, (39) where Y i is the watermarked vector tree and β w is a non-adaptive embedding strength because, to the best of our knowledge, no suitable human visual model has been reported in the literature for the situation in our scheme.
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In this paper, we introduce 2-dimensional human visual MTF model as an example of obtaining lower limit images.
The filter of 2-dimensional human visual MTF model cuts aoff imperceptible components from images.
Moreover they proposed mathematical 2-dimensional human visual MTF model about tristimulus on opponent color space.
The results were used to validate the human visual system model.
Then contrast sensitivity obtained by mathematical 2-dimensional human visual MTF model is defined as follows: (5).
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