Volumetric Semantic Segmentation Using Pyramid Context Features

By Jonathan T. Barron, Mark D. Biggin, Pablo Arbeláez, David W. Knowles, Soile V. E. Keränen, Jitendra Malik. 2013

19 citations recorded by the source corpus.

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 representation 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.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Media Technology

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In Biophysics

In Computer Vision and Pattern Recognition

In Media Technology

Jascha Narain Sohl-Dickstein

Citations in the corpus

Hypercolumns for object segmentation and fine-grained localization (2014, neutral): “[2] in their volumetric semantic segmentation system.”
Deep Unsupervised Learning using Nonequilibrium Thermodynamics (2015, methodology): “This method of achieving multiscale convolution was described in [1].”
Beyond Bounding Boxes: Precise Localization of Objects in Images (2015, unknown): “[3] in their volumetric semantic segmentation system.”