Boundary learning by optimization with topological constraints

Boundary learning by optimization with topological constraints

Recent studies have shown that machine learning can improve the accuracy of detecting object boundaries in images. In the standard approach, a boundary detector is trained by minimizing its pixel-level disagreement with human boundary tracings. This naive metric is problematic because it is overly s...

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Main Authors: Helmstaedter, Moritz N. (Author), Briggman, Kevin L. (Author), Denk, Winfried (Author), Bowden, Jared B. (Author), Mendenhall, John M. (Author), Abraham, Wickliffe C. (Author), Harris, Kristen M. (Author), Kasthuri, Narayanan (Author), Hayworth, Kenneth J. (Author), Schalek, Richard (Author), Tapia, Juan Carlos (Author), Lichtman, Jeff W. (Author), Jain, Viren (Contributor), Bollmann, Benjamin (Contributor), Richardson, Mark A. (Contributor), Berger, Daniel R. (Contributor), Seung, H. Sebastian (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2012-06-27T16:45:42Z.
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