Memory Efficient 3D Integral Volumes

Memory Efficient 3D Integral Volumes. Urschler, M., Bornik, A., & Donoser, M. In 2013 IEEE International Conference on Computer Vision Workshops, pages 722-729, 12, 2013. IEEE.

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Integral image data structures are very useful in computer vision applications that involve machine learning approaches based on ensembles of weak learners. The weak learners often are simply several regional sums of intensities subtracted from each other. In this work we present a memory efficient integral volume data structure, that allows reduction of required RAM storage size in such a supervised learning framework using 3D training data. We evaluate our proposed data structure in terms of the tradeoff between computational effort and storage, and show an application for 3D object detection of liver CT data. © 2013 IEEE.

@inproceedings{
 title = {Memory Efficient 3D Integral Volumes},
 type = {inproceedings},
 year = {2013},
 keywords = {Integral volume,Memory efficient,Object detection,Random forest,Summed volume table},
 pages = {722-729},
 websites = {http://ieeexplore.ieee.org/document/6755967/},
 month = {12},
 publisher = {IEEE},
 city = {Sydney, AU},
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 created = {2015-02-18T08:30:19.000Z},
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 last_modified = {2019-11-08T01:40:19.689Z},
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 authored = {true},
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 abstract = {Integral image data structures are very useful in computer vision applications that involve machine learning approaches based on ensembles of weak learners. The weak learners often are simply several regional sums of intensities subtracted from each other. In this work we present a memory efficient integral volume data structure, that allows reduction of required RAM storage size in such a supervised learning framework using 3D training data. We evaluate our proposed data structure in terms of the tradeoff between computational effort and storage, and show an application for 3D object detection of liver CT data. © 2013 IEEE.},
 bibtype = {inproceedings},
 author = {Urschler, Martin and Bornik, Alexander and Donoser, Michael},
 doi = {10.1109/ICCVW.2013.99},
 booktitle = {2013 IEEE International Conference on Computer Vision Workshops}
}

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