TY - GEN
T1 - Object recognition robust to imperfect depth data
AU - Fouhey, David F.
AU - Collet, Alvaro
AU - Hebert, Martial
AU - Srinivasa, Siddhartha
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84867696585&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867696585&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33868-7_9
DO - 10.1007/978-3-642-33868-7_9
M3 - Conference contribution
AN - SCOPUS:84867696585
SN - 9783642338670
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 83
EP - 92
BT - Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings
PB - Springer Verlag
T2 - Computer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings
Y2 - 7 October 2012 through 13 October 2012
ER -