Volumetric Semantic Segmentation using Pyramid Context Features.
Volumetric Semantic Segmentation using Pyramid Context Features.
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DOI：
10.1109/iccv.2013.428
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发表时间：
2013-12
期刊：
影响因子：
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通讯作者：
Malik J
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文献类型：
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作者：
Barron JT;Arbeláez P;Keränen SV;Biggin MD;Knowles DW;Malik J
我们提出了一种针对三维体积的每体素语义分割的算法。我们算法的核心是一种新颖的“金字塔上下文”特征，这是一种描述性表示，其设计使得精确的每体素线性分类可以非常有效。此功能不仅可以实现高效的语义分割，还可以实现算法的其他方面，例如新颖的学习功能和可以推理自一致性的堆叠架构。我们在果蝇胚胎的 3D 荧光显微镜数据上展示了我们的技术，我们能够在几分钟内产生极其准确的语义分割，而其他算法由于数据的大小和高维性或由于任务的难度而失败。
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.