A layered stereo algorithm using image segmentation and global visibility constraints

We propose a new stereo algorithm which uses colour segmentation to allow the handling of large untextured regions and precise localization of depth boundaries. Each segment is modelled as a plane. Robustness of the depth representation is achieved by the use of a layered model. Layers are extracted...

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Bibliographic Details
Main Authors: Bleyer, M., Gelautz, M.
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Artificial intelligence
Clustering algorithms
Computer science
control theory
systems
Convergence
Cost function
Exact sciences and technology
Greedy algorithms
Image reconstruction
Image segmentation
Layout
Pattern recognition. Digital image processing. Computational geometry
Robustness
Surface reconstruction
Surface treatment
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Description
Summary:We propose a new stereo algorithm which uses colour segmentation to allow the handling of large untextured regions and precise localization of depth boundaries. Each segment is modelled as a plane. Robustness of the depth representation is achieved by the use of a layered model. Layers are extracted by mean-shift-based clustering of depth planes. For layer assignment a global cost function is defined. The quality of the disparity map is measured by warping the reference image to the second view and compares it with the real image. Z-buffering enforces visibility and allows the explicit detection of occlusions. An efficient greedy algorithm searches for a local minimum of the cost function. Layer extraction and assignment are alternately applied. Results obtained for benchmark and self-recorded images indicate that the proposed algorithm can compete with the state-of-the-art.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2004.1421743