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Second, we build a background distribution with these data.
Firstly, we build a background model with CB algorithm.
Suppose a buffer of 50 previous frames is used to build a background model.
We want to build a background model for each frame such that the only 'foreground' would be the real fish.
This advantage allows us to build a background model of relatively few samples, since the classifier is not very sensitive to the robustness of the background model.
We exploit this specific feature of these videos to build a background model for each frame using the similar frames that exist in the whole video.
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This study builds a background for future research on design for sustainability and for spreading its use by companies, universities, intermediaries and policy makers.
This study builds a background for miRNA-target interaction based-research in APP infection in pig.
Image mosaicing technique is used to build a planar background.
To build a correct background model when training samples are not foreground-free, we propose a novel robust dictionary learning algorithm.
Instead, for line removal, we can build a new background prior from nearby reconstructed frames, assuming that at some point in the past and future, the line does not appear in the original corrupted frames.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com