Ensemble Classifier for Combining Stereo Matching Algorithms

Aristotle Spyropoulos, Philippos Mordohai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Scopus citations

Abstract

Stereo matching, as many problems in computer vision, has been addressed by a multitude of algorithms, each with its own strengths and weaknesses. Instead of following the conventional approach and trying to tune or enhance one of the algorithms so that it dominates the competition, we resign to the idea that a truly optimal algorithm may not be discovered soon and take a different approach. We present a novel methodology for combining a large number of heterogeneous algorithms that is able to clearly surpass the accuracy of the most accurate algorithms in the set. At the core of our approach is the design of an ensemble classifier trained to decide whether a particular stereo matcher is correct on a certain pixel. In addition to features describing the pixel, our feature vector encodes the agreement and disagreement between the matcher under consideration and all other matchers. This formulation leads to high accuracy in disparity estimation on the KITTI stereo benchmark.

Original languageEnglish
Title of host publicationProceedings - 2015 International Conference on 3D Vision, 3DV 2015
EditorsMichael Brown, Jana Kosecka, Christian Theobalt
Pages73-81
Number of pages9
ISBN (Electronic)9781467383325
DOIs
StatePublished - 20 Nov 2015
Event2015 International Conference on 3D Vision, 3DV 2015 - Lyon, France
Duration: 19 Oct 201522 Oct 2015

Publication series

NameProceedings - 2015 International Conference on 3D Vision, 3DV 2015

Conference

Conference2015 International Conference on 3D Vision, 3DV 2015
Country/TerritoryFrance
CityLyon
Period19/10/1522/10/15

Keywords

  • Accuracy
  • Adaptive optics
  • Algorithm design and analysis
  • Benchmark testing
  • Estimation
  • Image edge detection
  • Optical sensors

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