Please use this identifier to cite or link to this item: https://doi.org/10.1109/CVPR.2012.6247688
Title: A learning-based framework for depth ordering
Authors: Jia Z.
Gallagher A.
Chang Y.-J.
Chen T. 
Issue Date: 2012
Citation: Jia Z., Gallagher A., Chang Y.-J., Chen T. (2012). A learning-based framework for depth ordering. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition : 294-301. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPR.2012.6247688
Abstract: Depth ordering is instrumental for understanding the 3D geometry of an image. Humans are surprisingly good at depth ordering even with abstract 2D line drawings. In this paper we propose a learning-based framework for depth ordering inference. Boundary and junction characteristics are important clues for this task, and we have developed new features based on these attributes. Although each feature individually can produce reasonable depth ordering results, each still has limitations, and we can achieve better performance by combining them. In practice, local depth ordering inferences can be contradictory. Therefore, we propose a Markov Random Field model with terms that are more global than previous work, and use graph optimization to encourage a globally consistent ordering. In addition, to produce better object segmentation for the task of depth ordering, we propose to explicitly enforce closed loops and long edges for the occlusion boundary detection. We collect a new depth-order dataset for this problem, including more than a thousand human-labeled images with various daily objects and configurations. The proposed algorithm shows promising performance over conventional methods on both synthetic and real scenes.
Source Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/146131
ISBN: 9781467312264
ISSN: 10636919
DOI: 10.1109/CVPR.2012.6247688
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