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The color and motion contrast measures trace the object boundary and compare color values and motion vectors inside and outside the object.
The color and motion contrast measures are calculated by first tracing the object contour and then drawing a set of normal lines at equally spaced locations across the object boundary.
In these graphs, lower normalized values of the color and motion contrast measures indicate more accurate segmentation, and lower values of the histogram distance measure means that the object histogram is more stable over the clip, indicating better object tracking.
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The motion contrast of video sequence is measured by motion entropy.
To suppress the background noise, we propose a novel HOAOF to measure the motion contrast of the different pixels.
To suppress the produced noise while estimating optical flow, a novel HOAOF is proposed to measure the motion contrast.
In the temporal attention model, we propose a novel histogram of average optical flow to measure the motion contrast of the different pixels.
Due to the improved initial segmentation, the proposed method performs significantly better than reference method 1 and comparable to reference method 2. We use three objective measures [26] (see also [27] for other measures): color contrast, histogram distance, and motion contrast.
During object tracking, segmentation quality measures based on object color and motion contrast are taken.
However, a decrease in motion contrast does indicate a significant reduction in boundary accuracy, making motion contrast an effective measure for error checking.
(3) Motion contrast across the object boundary [26].
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