Object recognition robust to imperfect depth data

David F. Fouhey, Alvaro Collet, Martial Hebert, Siddhartha Srinivasa

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationComputer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings
PublisherSpringer Verlag
Number of pages10
EditionPART 2
ISBN (Print)9783642338670
StatePublished - 2012
EventComputer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings - Florence, Italy
Duration: Oct 7 2012Oct 13 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7584 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceComputer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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