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Efficient cost aggregation for feature-vector-based wide-baseline stereo matching

Journal Article


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Abstract


  • In stereo matching applications, local cost aggregation techniques are usually preferred over global methods due to their speed and ease of implementation. Local methods make implicit smoothness assumptions by aggregating costs within a finite window; however, cost aggregation is a time-consuming process. Furthermore, most existing local methods are based on pixel intensity values, and hence are not efficient with feature vectors used in wide-baseline stereo matching. In this paper, a new cost aggregation method is proposed, where a Per-Column Cost matrix is combined with a feature-vector-based weighting strategy to achieve both matching accuracy and computational efficiency. Here, the proposed cost aggregation method is applied with the DAISY feature descriptor for wide-baseline stereo matching; however, this method can also be applied to a fast growing number of stereo matching techniques that are based on feature descriptors. A performance comparison with several benchmark local cost aggregation approaches is presented, along with a thorough analysis of the time and storage complexity of the proposed method.

Publication Date


  • 2018

Citation


  • X. Peng, A. Bouzerdoum & S. Phung, "Efficient cost aggregation for feature-vector-based wide-baseline stereo matching," Eurasip Journal on Image and Video Processing, vol. 2018, (24) pp. 1-16, 2018.

Scopus Eid


  • 2-s2.0-85045375396

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2244&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/1242

Number Of Pages


  • 15

Start Page


  • 1

End Page


  • 16

Volume


  • 2018

Issue


  • 24

Place Of Publication


  • Germany

Abstract


  • In stereo matching applications, local cost aggregation techniques are usually preferred over global methods due to their speed and ease of implementation. Local methods make implicit smoothness assumptions by aggregating costs within a finite window; however, cost aggregation is a time-consuming process. Furthermore, most existing local methods are based on pixel intensity values, and hence are not efficient with feature vectors used in wide-baseline stereo matching. In this paper, a new cost aggregation method is proposed, where a Per-Column Cost matrix is combined with a feature-vector-based weighting strategy to achieve both matching accuracy and computational efficiency. Here, the proposed cost aggregation method is applied with the DAISY feature descriptor for wide-baseline stereo matching; however, this method can also be applied to a fast growing number of stereo matching techniques that are based on feature descriptors. A performance comparison with several benchmark local cost aggregation approaches is presented, along with a thorough analysis of the time and storage complexity of the proposed method.

Publication Date


  • 2018

Citation


  • X. Peng, A. Bouzerdoum & S. Phung, "Efficient cost aggregation for feature-vector-based wide-baseline stereo matching," Eurasip Journal on Image and Video Processing, vol. 2018, (24) pp. 1-16, 2018.

Scopus Eid


  • 2-s2.0-85045375396

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2244&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/1242

Number Of Pages


  • 15

Start Page


  • 1

End Page


  • 16

Volume


  • 2018

Issue


  • 24

Place Of Publication


  • Germany