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Figure 12 Segmentation of moving object.
Figure 2a, c shows the results of moving object detection.
Figure 7 Segmentation of moving object from moving edges.
Figure 4c shows the result of moving object detection.
Fig. 8 Tracking results of moving object with T-OTF algorithm Fig. 9 Tracking results of moving object with ET-MVC algorithm.
(4 where A x,y) denotes the binarization result of moving object detection.
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The model can provide the conditions for crowd optimization of moving objects by the objective state of different time domain.
According to formula (6), the multi-objective optimization model of moving objects is obtained.
Hence, POOT is shown to effectively conserve energy and achieve the objective of tracking of moving objects.
This paper has addressed the search and detection of moving objects in an image sequence using a moving camera.
Expect lots of moving objects, animated illustrations and a physics engine to power them all.
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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