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Volumetric Semantic Segmentation using Pyramid Context Features, ICCV 2013 - 2

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Published on Sep 1, 2013

Jonathan T. Barron, Pablo Arbeláez, Soile V. E. Keränen, Mark D. Biggin, David W. Knowles, Jitendra Malik

International Conference on Computer Vision (ICCV) 2013

http://www.cs.berkeley.edu/~barron/Ba...

Abstract:
We present an algorithm for the per-voxel semantic segmentation of a three-dimensional volume. At the core of our algorithm is a novel ``pyramid context'' feature, a descriptive feature designed such that exact per-voxel linear classification can be made extremely efficient. This feature not only allows for efficient semantic segmentation but enables other aspects of our algorithm, such as novel learned features and a stacked architecture that can reason about self-consistency. We demonstrate our technique on 3D fluorescence microscopy data of Drosophila embryos for which we are able to produce extremely accurate semantic segmentations in a matter of minutes, and for which other algorithms fail due to the size and high-dimensionality of the data, or due to the difficulty of the task.

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