Improved GrabCut segmentation via GMM optimisation
Chen, Brenden, Chen, Daniel, Fookes, Clinton, Mamic, George, & Sridharan, Sridha (2008) Improved GrabCut segmentation via GMM optimisation. In Ceballos, S (Ed.) Computing: Techniques and Applications, 2008, IEEE, Australia, Australian Capital Territory, Canberra, pp. 39-45.
Semi-automatic segmentation of still images has vast and varied practical applications. Recently, an approach "GrabCut" has managed to successfully build upon earlier approaches based on colour and gradient information in order to address the problem of efficient extraction of a foreground object in a complex environment. In this paper, we extend the GrabCut algorithm further by applying an unsupervised algorithm for modelling the Gaussian Mixtures that are used to define the foreground and background in the segmentation algorithm. We show examples where the optimisation of the GrabCut framework leads to further improvements in performance.
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|Item Type:||Conference Paper|
|Keywords:||Image segmentation, GrabCut|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Institutes > Information Security Institute
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright IEEE|
|Deposited On:||12 Feb 2010 12:41|
|Last Modified:||24 Jul 2012 17:07|
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