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Stereo correspondence with occlusion handling in a symmetric patch-based graph-cuts model
Yi Deng1, Qiong Yang, Xueyin Lin
1Tsinghua University, Beijing 100084, P.R. China. dengyi00@mails.tsinghua.edu.cn
Summary
This study introduces a novel patch-based correspondence model that improves image matching accuracy, particularly in challenging areas like occlusions and untextured regions. The new method enhances disparity estimation by segmenting images and using patches to handle visibility changes.
Area of Science:
- Computer Vision
- Image Processing
- Machine Learning
Background:
- Existing segment-based correspondence methods struggle with occlusions due to strict discontinuity assumptions.
- Untextured pixels and segment boundaries are often handled with hard constraints, limiting performance in complex scenes.
Purpose of the Study:
- To develop a novel patch-based correspondence model that addresses limitations of current methods, especially near occlusions.
- To improve disparity estimation accuracy in untextured areas and near discontinuities.
Main Methods:
- The proposed model performs color segmentation on both images and further divides segments into patches.
- It allows for varying visibility of patches within a segment and uses a segment-level uniqueness constraint for occlusion computation.
- An energy minimization framework with graph-cuts is employed for global optimization of disparities and occlusions.
Main Results:
- The patch-based approach significantly enhances performance in occluded and untextured regions.
- Superior disparity estimation accuracy is demonstrated, especially near discontinuities and in areas with partial visibility.
Conclusions:
- The novel patch-based correspondence model offers superior performance compared to existing methods.
- The approach effectively handles occlusions and untextured areas, advancing the field of image correspondence.
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