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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Journal ArticleDOI
Analysis of Disparity Error for Stereo Autofocus
TL;DR: This paper gives an analytical treatment of this fundamental issue of disparity-based autofocus by examining the relation between image sharpness and disparity error and provides a theoretical backbone for the empirical observation that, regardless of the initial lens position, disparity- based aut ofocus can bring the lens to the hill zone of the focus profile in one movement.
Journal ArticleDOI
Yet Another Cost Aggregation Over Models
Ouk Choi,Hyun Sung Chang +1 more
TL;DR: A mixture-of-experts model is proposed, which applies a heterogeneous set of filters on the cost volume and adaptively combines the results, and employs supervised learning to estimate per-pixel mixing coefficients, which are used to adaptively control the weight of the filter responses.
Journal ArticleDOI
A Robust Edge-Preserving Stereo Matching Method for Laparoscopic Images
TL;DR: In this article , a robust edge-preserving stereo matching method for laparoscopic images is proposed, comprising an efficient sparse-dense feature matching step, left and right image illumination equalization, and refined disparity optimization.
Book ChapterDOI
Stereo Matching for Wireless Capsule Endoscopy Using Direct Attenuation Model
TL;DR: A robust approach to estimate depth maps designed for stereo camera-based wireless capsule endoscopy, using the direct attenuation model to estimate a depth map up to a scale factor is proposed.
Journal ArticleDOI
Guided filtering based data fusion for light field depth estimation with L0 gradient minimization
Qihui Han,Cheolkon Jung +1 more
TL;DR: Experimental results on both synthetic and real light field datasets show that the proposed method achieves clearer edge and less error in depth than state-of-the-arts.
References
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Journal ArticleDOI
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