This paper explores the potential of brain-computer interfaces in segmenting objects from images. Our approach is centered around designing an effective method for displaying the image parts to the users such that they generate measurable brain reactions. When an image region, specifically a block of pixels, is displayed we estimate the probability of the block containing the object of interest using a score based on EEG activity. After several such blocks are displayed, the resulting probability map is binarized and combined with the GrabCut algorithm to segment the image into object and background regions. This study shows that BCI and simple EEG analysis are useful in locating object boundaries in images.
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Status = Published
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Subject = Engineering: Signal processing
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DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Electronic Engineering
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Subject = Engineering
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Publication Type = Conference or Workshop Item
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DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
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Subject = Computer Science: Artificial intelligence
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Subject = Computer Science: Image processing
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Subject = Computer Science
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DCU Faculties and Centres = DCU Faculties and Schools
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Dublin City University ->
Subject = Biological Sciences
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Subject = Biological Sciences: Neuroscience
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Dublin City University ->
DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing
Alan F. Smeaton,
Noel E. O'Connor,
Xavier Giró-i-Nieto,
Kevin McGuinness,
Graham Healy,
Eva Mohedano