JOURNAL ARTICLE

Feasibility Boundary in Dense and Semi-Dense Stereo Matching

Abstract

In stereo literature, there is no standard method for evaluating algorithms for semi-dense stereo matching. Moreover, existing evaluations for dense methods require a fixed parameter setting for the tested algorithms. In this paper, we propose a method that overcomes these drawbacks and still is able to compare algorithms based on a simple numerical value, so that reporting results does not take up much space in a paper. We propose evaluation of stereo algorithms based on Receiver Operating Characteristics (ROC) which captures both errors and sparsity. By comparing ROC curves of all tested algorithms we obtain the Feasibility Boundary, the best possible performance achieved by a set of tested stereo algorithms, which allows stereo algorithm users to select the proper method and parameter setting for a required application.

Keywords:
Computer science Boundary (topology) Matching (statistics) Artificial intelligence Set (abstract data type) Algorithm Receiver operating characteristic Computer vision Mathematics Machine learning

Metrics

15
Cited By
1.20
FWCI (Field Weighted Citation Impact)
20
Refs
0.81
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image Processing Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Optical Coherence Tomography Applications
Physical Sciences →  Engineering →  Biomedical Engineering

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