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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Li, Longzhen; Ellis, Anna; Ferryman, James (2015)
Languages: English
Types: Unknown
While a multitude of motion segmentation algorithms have been presented in the literature, there has not been an objective assessment of different approaches to fusing their outputs. This paper investigates the application of 4 different fusion schemes to the outputs of 3 probabilistic pixel-level segmentation algorithms. We performed an extensive experimentation using 6 challenge categories from the changedetection.net dataset demonstrating that in general simple majority vote proves to be more effective than more complex fusion schemes.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    • Figure4.Representativecategory results.Leftcolumn: Dynamic Background; top-to-bottom: original image,ground truth,Bayes, MY, GMM, BC, MI, MAX, CMY.Right column: Thermal;top-tobottom: original image, ground truth, MY, MI, BC, Bayes, MAX, GMM, CMY (seeTable2 for category ranks).
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