2009 4th IEEE Conference on Industrial Electronics and Applications 2009
DOI: 10.1109/iciea.2009.5138708
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A background separation method of nonuniform image segmentation

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Cited by 6 publications
(3 citation statements)
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“…Previous studies [2], [62] have indicated that the edges and lines of objects exhibit a stronger association with high frequency components, while the frequency of the background and object surface tends to be relatively low [41]. With this understanding, we hypothesize that by emphasizing learning from high frequency components, the model can more effectively capture the semantic features of objects while mitigating the risk of learning spurious correlations.…”
Section: Two-step High-pass Filtermentioning
confidence: 80%
See 1 more Smart Citation
“…Previous studies [2], [62] have indicated that the edges and lines of objects exhibit a stronger association with high frequency components, while the frequency of the background and object surface tends to be relatively low [41]. With this understanding, we hypothesize that by emphasizing learning from high frequency components, the model can more effectively capture the semantic features of objects while mitigating the risk of learning spurious correlations.…”
Section: Two-step High-pass Filtermentioning
confidence: 80%
“…(1) Frequency restriction. Prior studies [2], [62] have revealed that the edges and lines of objects are more related to the high frequency components, while the frequency of background and object surface is relatively low [41]. Consequently, assuming that by learning more from the high frequency components, the model can better extract the semantic concepts of objects while avoiding learning spurious correlations between backgrounds and labels.…”
Section: Introductionmentioning
confidence: 99%
“…This paper is aimed at inland ships recognition. Since the difference from the adjacent frames caused by low speed ship is not obvious, adopting the frame difference method would lead to the "cavity" phenomenon within ship image [1,2]. The optical flow is quite complex, and its anti-noise performance is poor without the special hardware devices, it cannot be used in the real-time processing of full-frame video stream [3].…”
Section: Introductionmentioning
confidence: 99%