Depth assisted object segmentation in multi-view video

Date

2008-01-01

Author

Cigla, Cevahir
Alatan, Abdullah Aydın

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In this work, a novel and unified approach for multi-view video (MVV) object segmentation is presented. In the first stage, a region-based graph-theoretic color segmentation algorithm is proposed, in which the popular Normalized Cuts segmentation method is improved with some modifications on its graph structure. Segmentation is obtained by recursive bi-partitioning of a weighted graph of an initial over-segmentation mask. The available segmentation mask is also utilized during dense depth map estimation step, based on a novel modified plane- and angle-sweeping strategy for each of these regions. Dense depth estimation is achieved by region-wise planarity assumption for the whole scene, in which depth models are estimated for sub-regions. Finally, the multi-view image segmentation algorithm is extended to object segmentation in MVV by the additional optical flow information. The required motion field is obtained via region-based matching that has consistent parameterization with color segmentation and dense depth map estimation algorithms. Experimental results indicate that proposed approach segments semantically meaningful objects in MVV with high precision.

Subject Keywords

Graph-theoretic image segmentation, Dense depth map estimation, Multi-view video object segmentation

URI

https://hdl.handle.net/11511/36476

DOI

https://doi.org/10.1109/3dtv.2008.4547839

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

C. Cigla and A. A. Alatan, “Depth assisted object segmentation in multi-view video,” 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36476.