access icon free Robust local stereo matching under varying radiometric conditions

© The Institution of Engineering and Technology
Received 13/05/2013, Accepted 23/10/2013, Revised 14/10/2013, Published 28/11/2013

The authors present a local stereo matching algorithm whose performance is insensitive to changes in radiometric conditions between the input images. First, a prior on the disparities is built by combining the DAISY descriptor and Census filtering. Then, a Census-based cost aggregation with a self-adaptive window is performed. Finally, the maximum a-posteriori estimation is carried out to compute the disparity. The authors’ algorithm is compared with both local and global stereo matching algorithms (NLCA, ELAS, ANCC, AdaptWeight and CSBP) by using Middlebury datasets. The results show that the proposed algorithm achieves high-accuracy dense disparity estimations and is more robust to radiometric differences between input images than other algorithms.

Inspec keywords: radiometry; stereo image processing; filtering theory; image matching; maximum likelihood estimation

Other keywords: Census filtering; CSBP algorithm; robust local stereo matching algorithm; Census-based cost aggregation; NLCA algorithm; AdaptWeight algorithm; ELAS algorithm; self-adaptive window; maximum a-posteriori estimation; ANCC algorithm; radiometric condition; high-accuracy dense disparity estimation; Middlebury dataset; DAISY descriptor

Subjects: Other topics in statistics; Filtering methods in signal processing; Other topics in statistics; Image recognition; Computer vision and image processing techniques

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