Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICCVW.2013.99 | ICCVW '13 Proceedings of the 2013 IEEE International Conference on Computer Vision Workshops |
Keywords | Field | DocType |
integral volumes,supervised learning framework,training data,memory efficient,required ram storage size,computational effort,memory efficient integral volume,weak learner,proposed data structure,integral image data structure,liver ct data,data structure,learning artificial intelligence,data structures,computer vision | Training set,Object detection,Computer vision,Data structure,Computer science,Supervised learning,Memory management,Computed tomography,Solid modeling,Artificial intelligence,Random forest,Machine learning | Conference |
Volume | Issue | Citations |
2013 | 1 | 2 |
PageRank | References | Authors |
0.39 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Urschler | 1 | 347 | 23.94 |
| Alexander Bornik | 2 | 435 | 30.28 |
| Michael Donoser | 3 | 617 | 31.10 |