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Then watershed-based iterative algorithm is employed to segment the moving object region from the extracted moving edges.
Because of region homogeneity and high sensitivity of pixel intensity to noise, these methods show poor performance in detecting moving object region properly.
In ideal situation, high gradient values are expected to exist in the regions of, where boundary of moving object region exists.
A watershed-based algorithm is utilized which makes use of the extracted moving edges to segment moving object region with more accurate boundary.
As a result, horizontal and vertical scanning-basedVOP extraction method fails to extract moving object region properly as shown in Figures 12(f) and 12 j).
Since watershed has been proven to be very useful and efficient for image segmentation [23], it extracts more accurate moving object region during segmentation.
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Moving object regions were chosen by the inter-frame difference.
In KW, moving object regions are segmented out by horizontal and vertical scanning followed by morphological operation.
Moving edges and gradient infimum values of two difference images are used by an iterative algorithm which removes background segments from ROI to obtain moving object regions.
In this paper, the IMF is estimated from regions having high ZNCC only, because other regions are saturated or moving object regions that have low credibility in estimating the IMF.
Experimental results demonstrate that the proposed background updating method can update the background exactly and rapidly along with the variance of illumination, the self-adaptive thresholding segmentation method based on the Gaussian model can extract the moving object regions accurately and completely, and the shadow can be eliminated accurately.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com