Object Recognition Robust to Imperfect Depth Data
Abstract
In this paper, we present an adaptive data fusion model that robustly integrates depth and image only perception. Combining dense depth measurements with images can greatly enhance the performance of many computer vision algorithms, yet degraded depth measurements (e.g., missing data) can also cause dramatic performance losses to levels below image-only algorithms. We propose a generic fusion model based on maximum likelihood estimates of fused image-depth functions for both available and missing depth data. We demonstrate its application to each step of a state-of-the-art image-only object instance recognition pipeline. The resulting approach shows increased recognition performance over alternative data fusion approaches.
Cite
Text
Fouhey et al. "Object Recognition Robust to Imperfect Depth Data." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33868-7_9Markdown
[Fouhey et al. "Object Recognition Robust to Imperfect Depth Data." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/fouhey2012eccv-object/) doi:10.1007/978-3-642-33868-7_9BibTeX
@inproceedings{fouhey2012eccv-object,
title = {{Object Recognition Robust to Imperfect Depth Data}},
author = {Fouhey, David F. and Collet, Alvaro and Hebert, Martial and Srinivasa, Siddhartha S.},
booktitle = {European Conference on Computer Vision},
year = {2012},
pages = {83-92},
doi = {10.1007/978-3-642-33868-7_9},
url = {https://mlanthology.org/eccv/2012/fouhey2012eccv-object/}
}