Stereo matching with nonparametric smoothness priors in feature space

We propose a novel formulation of stereo matching that considers each pixel as a feature vector. Under this view, matching two or more images can be cast as matching point clouds in feature space. We build a nonparametric depth smoothness model in this space that correlates the image features and de...

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Bibliographic Details
Published in2009 IEEE Conference on Computer Vision and Pattern Recognition pp. 485 - 492
Main Authors Smith, Brandon M, Li Zhang, Hailin Jin
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2009
Subjects
Clouds
Computer vision
Image converters
Image segmentation
Layout
Pixel
Robustness
Shape
Stereo vision
Videos
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Summary:We propose a novel formulation of stereo matching that considers each pixel as a feature vector. Under this view, matching two or more images can be cast as matching point clouds in feature space. We build a nonparametric depth smoothness model in this space that correlates the image features and depth values. This model induces a sparse graph that links pixels with similar features, thereby converting each point cloud into a connected network. This network defines a neighborhood system that captures pixel grouping hierarchies without resorting to image segmentation. We formulate global stereo matching over this neighborhood system and use graph cuts to match pixels between two or more such networks. We show that our stereo formulation is able to recover surfaces with different orders of smoothness, such as those with high-curvature details and sharp discontinuities. Furthermore, compared to other single-frame stereo methods, our method produces more temporally stable results from videos of dynamic scenes, even when applied to each frame independently.
ISBN:1424439922
9781424439928
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2009.5206793