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TV Corner-Logo Adaptive Threshold Segmentation Algorithm Based on Saliency Detection

Authors: Xinwei Wang; Dongmei Li; Shaobin Li; Yuzhe Sun; Shanzhen Lan;

TV Corner-Logo Adaptive Threshold Segmentation Algorithm Based on Saliency Detection

Abstract

TV corner-logo is widely used in current video program, and becomes one of the hot spots in information extraction and analysis. TV corner-logo detection and segmentation algorithm is important for information extraction. In this paper, we presented an adaptive threshold segmentation algorithm, based on the combined time-averaged edge detection and saliency detection, to effectively separate and extract the TV corner-logo from the video sequence. First, we applied Canny operator to detect edges and then calculate the weighted average edge of ten frames, so as to get the time-averaged edge image. Then we combine the time-averaged edge image with the saliency map to get a more accurate segmentation. Finally, we used the adaptive threshold segmentation algorithm to separate the corner-logo. Experimental results show that, this method can effectively detect and separate the corner-logo from the background.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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