Proceedings ArticleDOI
Constant Time Weighted Median Filtering for Stereo Matching and Beyond
Ziyang Ma,Kaiming He,Yichen Wei,Jian Sun,Enhua Wu +4 more
- pp 49-56
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TLDR
It is discovered that with this refinement, even the simple box filter aggregation achieves comparable accuracy with various sophisticated aggregation methods (with the same refinement), revealing that the previously overlooked refinement can be at least as crucial as aggregation.Abstract:
Despite the continuous advances in local stereo matching for years, most efforts are on developing robust cost computation and aggregation methods. Little attention has been seriously paid to the disparity refinement. In this work, we study weighted median filtering for disparity refinement. We discover that with this refinement, even the simple box filter aggregation achieves comparable accuracy with various sophisticated aggregation methods (with the same refinement). This is due to the nice weighted median filtering properties of removing outlier error while respecting edges/structures. This reveals that the previously overlooked refinement can be at least as crucial as aggregation. We also develop the first constant time algorithm for the previously time-consuming weighted median filter. This makes the simple combination ``box aggregation + weighted median'' an attractive solution in practice for both speed and accuracy. As a byproduct, the fast weighted median filtering unleashes its potential in other applications that were hampered by high complexities. We show its superiority in various applications such as depth up sampling, clip-art JPEG artifact removal, and image stylization.read more
Citations
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Proceedings ArticleDOI
Confidence-based weighted median filter for effective disparity map refinement
TL;DR: The reliability of pixels is considered as an additional weighting strategy where the reliability is computed by conventional confidence measures to improve the robustness and accuracy of the weighted median filter (WMF).
Proceedings ArticleDOI
Disparity refinement with stability-based tree for stereo matching
TL;DR: The proposed disparity refinement method with stability-based tree achieves effective performance in removing outliers and improves the quality of raw disparity map in stereo matching, which makes the local methods results comparable to the global ones.
Journal ArticleDOI
Asymmetric occlusion detection using linear regression and weight-based filling for stereo disparity map estimation
T. Malathi,Manas Kamal Bhuyan +1 more
TL;DR: Experimental results show that the proposed occlusion detection method gives almost similar performance as that of the methods which use two disparity maps for detection, and the authors’ method can give better performance.
Dissertation
Identification and tracking of grains undergoing progressive breakage under mechanical loading with image analysis of 3D+t tomographic images
TL;DR: This thesis highlights computational approaches that augment the understanding of breakage and crushing phenomena in granular materials by deviating from a morphological watershed approach to segmentation of geomaterials, to a hierarchical approach that better captures apriori information from data sources.
Proceedings ArticleDOI
Adaptive Plane Fitting-Based Stereo Matching with Image Guided Disparity Refinement
TL;DR: Experimental results show that the proposed stereo matching method generates a high-quality disparity map which has the distinct boundary between objects compared to other methods.
References
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Journal ArticleDOI
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