Object Recognition Robust to Imperfect Depth Data

David Fouhey, Alvaro Collet Romea, Martial Hebert, and Siddhartha Srinivasa
2nd Workshop on Consumer Depth Cameras for Computer Vision in conjunction with ECCV 2012, October, 2012.


Download
  • Adobe portable document format (pdf) (3MB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
n 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.

Notes
Associated Center(s) / Consortia: Quality of Life Technology Center

Text Reference
David Fouhey, Alvaro Collet Romea, Martial Hebert, and Siddhartha Srinivasa, "Object Recognition Robust to Imperfect Depth Data," 2nd Workshop on Consumer Depth Cameras for Computer Vision in conjunction with ECCV 2012, October, 2012.

BibTeX Reference
@inproceedings{Fouhey_2012_7252,
   author = "David Fouhey and Alvaro {Collet Romea} and Martial Hebert and Siddhartha Srinivasa",
   title = "Object Recognition Robust to Imperfect Depth Data",
   booktitle = "2nd Workshop on Consumer Depth Cameras for Computer Vision in conjunction with ECCV 2012",
   month = "October",
   year = "2012",
}