Segment-tree based cost aggregation for stereo matching with enhanced segmentation advantage

Peng Yao, Hua Zhang, Yanbing Xue, Mian Zhou, Guangping Xu, Zan Gao, Shengyong Chen

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

11 Citations (Scopus)

Abstract

Segment-tree (ST) based cost aggregation algorithm for stereo matching successfully integrates the information of segmentation with non-local cost aggregation framework. The tree structure which is generated by the segmentation strategy directly determines the final results for this kind of algorithms. However, the original strategy performs unreasonable due to its coarse performance and ignores to meet the disparity consistency assumption. To improve these weaknesses we propose a novel segmentation algorithm for constructing a more faithful ST with enhanced segmentation advantage according to a robust initial over-segmentation. Then we implement non-local cost aggregation framework on this new ST structure and obtain improved disparity maps. Performance evaluations on all 31 Middlebury stereo pairs show that the proposed algorithm outperforms than other five state-of-the-art aggregated based algorithms and also keeps time efficiency.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2027-2031
Number of pages5
ISBN (Electronic)9781509041176
DOIs
Publication statusPublished - 16 Jun 2017
Externally publishedYes
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Country/TerritoryUnited States
CityNew Orleans
Period5/03/179/03/17

Keywords

  • non-local aggregation
  • Segment-tree
  • segmentation advantage
  • stereo matching

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