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Cross-trees, Edge and Superpixel Priors-based Cost aggregation for Stereo matching.

Feiyang Cheng1, Hong Zhang1, Mingui Sun2

  • 1Image Research Center, Beihang University, Beijing, China.

Pattern Recognition
|June 3, 2015
PubMed
Summary

This study introduces a novel cross-trees structure for efficient nonlocal cost aggregation in computer vision. This new method outperforms existing techniques like minimum spanning tree and segment-tree on benchmark datasets.

Keywords:
cost aggregationimage filteringspanning treesstereo matching

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Photography

Background:

  • Nonlocal cost aggregation is crucial for stereo matching and image processing tasks.
  • Existing tree-based methods like Minimum Spanning Tree (MST) and Segment-Tree (ST) have limitations in efficiency and uniqueness.
  • Accurate cost aggregation is essential for reliable depth estimation and scene reconstruction.

Purpose of the Study:

  • To propose a novel cross-trees structure for efficient and unique nonlocal cost aggregation.
  • To introduce edge and superpixel priors to prevent false cost aggregations across depth boundaries.
  • To evaluate the performance of the proposed cross-trees method against existing tree-based nonlocal algorithms.

Main Methods:

  • A novel cross-trees structure, comprising a horizontal-tree and a vertical-tree, is proposed for cost aggregation.
  • The cross-trees construction is independent of image properties, ensuring efficiency and uniqueness.
  • Two algorithms, cross-trees with edge prior and cross-trees with superpixel prior, are developed to handle depth discontinuities.

Main Results:

  • The proposed cross-trees structure provides efficient and unique tree constructions.
  • The algorithms incorporating edge and superpixel priors effectively tackle false cost aggregations across depth boundaries.
  • Performance evaluations on 27 Middlebury datasets demonstrate superior results compared to MST and ST methods.

Conclusions:

  • The novel cross-trees structure offers a significant advancement in nonlocal cost aggregation techniques.
  • The proposed priors effectively enhance the robustness of cost aggregation by respecting depth boundaries.
  • The cross-trees method presents a faster and more accurate alternative for stereo matching and related computer vision applications.