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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jain, V. Bollmann, B. Richardson, M. Berger, D.R. Helmstaedter, M.N. Briggman, K.L. Denk, W. Bowden, J.B. Mendenhall, J.M. Abraham, W.C. Harris, K.M. Kasthuri, N. Hayworth, K.J. Schalek, R. Tapia, J.C. Lichtman, J.W. Seung, H.S. |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Biomedical Optics, Max Planck Institute for Medical Research, Heidelberg, Germany (Helmstaedter, M.N.; Briggman, K.L.; Denk, W.) || Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA (Jain, V.; Bollmann, B.; Richardson, M.; Berger, D.R.; Seung, H.S.) || Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA, USA (Kasthuri, N.; Hayworth, K.J.; Schalek, R.; Tapia, J.C.; Lichtman, J.W.) || Dept. of Psychology and Brain Health and Repair Research Center, Univ. of Otago, Dunedin, New Zealand (Abraham, W.C.) || Center for Learning and Memory, Department of Neurobiology, University of Texas at Austin, TX, USA (Bowden, J.B.; Mendenhall, J.M.; Harris, K.M.) |
| Abstract | 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 sensitive to boundary locations. This problem is solved by metrics provided with the Berkeley Segmentation Dataset, but these can be insensitive to topo-logical differences, such as gaps in boundaries. Furthermore, the Berkeley metrics have not been useful as cost functions for supervised learning. Using concepts from digital topology, we propose a new metric called the warping error that tolerates disagreements over boundary location, penalizes topological disagreements, and can be used directly as a cost function for learning boundary detection, in a method that we call Boundary Learning by Optimization with Topological Constraints (BLOTC). We trained boundary detectors on electron microscopic images of neurons, using both BLOTC and standard training. BLOTC produced substantially better performance on a 1.2 million pixel test set, as measured by both the warping error and the Rand index evaluated on segmentations generated from the boundary labelings. We also find our approach yields significantly better segmentation performance than either gPb-OWT-UCM or multiscale normalized cut, as well as Boosted Edge Learning trained directly on our data. |
| Starting Page | 2488 |
| Ending Page | 2495 |
| File Size | 1308237 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424469840 |
| ISSN | 10636919 |
| e-ISBN | 9781424469857 |
| DOI | 10.1109/CVPR.2010.5539950 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Constraint optimization Image segmentation Detectors Cost function Machine learning Object detection Humans Supervised learning Topology Electrons |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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