Kolmogorov and Zabih’s Graph Cuts Stereo Matching Algorithm

Kolmogorov, Vladimir; Monasse, Pascal; Tan, Pauline

doi:10.5201/ipol.2014.97

Cited by 29 publications

(44 citation statements)

References 7 publications

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“…However, in cross-spectral stereo matching, the crucial problem is the spectral difference. Techniques such as [18], [19], [33] do not work well because of the intensity consistency assumption. Although radiometric inconsistencies that are handled in [34], [35] are related to spectral difference, they have very different properties.…”

Section: Feature Descriptorsmentioning

confidence: 99%

“…Many stereo techniques have been proposed [37], including local methods [19], semi-global methods [35], and global methods [33]. More recently, these techniques have been adapted for LFs.…”

Section: Light Field Stereo Matchingmentioning

confidence: 99%

“…E unique enforces uniqueness of disparities between image pairs. The last three terms are same as those in [33].…”

Section: Disparity Estimatementioning

confidence: 99%

“…The disparity maps for all image pairs (with vertical or horizontal neighbors) are computed using Graph Cuts [33]. These disparity maps are then merged to produce a single one denoted as f (s,t) .…”

Section: Disparity Estimatementioning

confidence: 99%

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Hyperspectral Light Field Stereo Matching

Zhu

Xue²,

et al. 2019

IEEE Trans. Pattern Anal. Mach. Intell.

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In this paper, we describe how scene depth can be extracted using a hyperspectral light field capture (H-LF) system. Our H-LF system consists of a array of cameras, with each camera sampling a different narrow band in the visible spectrum. There are two parts to extracting scene depth. The first part is our novel cross-spectral pairwise matching technique, which involves a new spectral-invariant feature descriptor and its companion matching metric we call bidirectional weighted normalized cross correlation (BWNCC). The second part, namely, H-LF stereo matching, uses a combination of spectral-dependent correspondence and defocus cues that rely on BWNCC. These two new cost terms are integrated into a Markov Random Field (MRF) for disparity estimation. Experiments on synthetic and real H-LF data show that our approach can produce high-quality disparity maps. We also show that these results can be used to produce the complete plenoptic cube in addition to synthesizing all-focus and defocused color images under different sensor spectral responses.

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Section: Feature Descriptorsmentioning

confidence: 99%

“…Many stereo techniques have been proposed [37], including local methods [19], semi-global methods [35], and global methods [33]. More recently, these techniques have been adapted for LFs.…”

Section: Light Field Stereo Matchingmentioning

confidence: 99%

“…E unique enforces uniqueness of disparities between image pairs. The last three terms are same as those in [33].…”

Section: Disparity Estimatementioning

confidence: 99%

Section: Disparity Estimatementioning

confidence: 99%

See 2 more Smart Citations

Hyperspectral Light Field Stereo Matching

Zhu

Xue²,

et al. 2019

IEEE Trans. Pattern Anal. Mach. Intell.

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show abstract

“…We chose R = 9. In all cases, the disparity estimation is im- 2 We used the code found in [17]. Table 2.…”

Section: Occlusion Fillingmentioning

confidence: 99%

Occlusion detection in dense stereo estimation with convex optimization

Tan¹,

Chambolle²,

Monasse³

2017

2017 IEEE International Conference on Image Processing (ICIP)

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In this paper, we propose a dense two-frame stereo algorithm which handles occlusion in a variational framework. Our method is based on a new regularization model which includes both a constraint on the occlusion width and a visibility constraint in nonoccluded areas. The minimization of the resulting energy functional is done by convex relaxation. A post-processing then detects and fills the occluded regions. We also propose a novel dissimilarity measure that combines color and gradient comparison with a variable respective weight, to benefit from the robustness of the comparison based on local variations while avoiding the fattening effect it may generate.

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A dictionary‐based graph‐cut algorithm for MRI reconstruction

Pannetier

Raj

2020

NMR in Biomedicine

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Purpose: Compressive sensing (CS)-based image reconstruction methods have proposed random undersampling schemes that produce incoherent, noise-like aliasing artifacts, which are easier to remove. The denoising process is critically assisted by imposing sparsity-enforcing priors. Sparsity is known to be induced if the prior is in the form of the L p (0 ≤ p ≤ 1) norm. CS methods generally use a convex relaxation of these priors such as the L 1 norm, which may not exploit the full power of CS. An efficient, discrete optimization formulation is proposed, which works not only on arbitrary L p-norm priors as some non-convex CS methods do, but also on highly nonconvex truncated penalty functions, resulting in a specific type of edge-preserving prior. These advanced features make the minimization problem highly non-convex, and thus call for more sophisticated minimization routines. Theory and methods: The work combines edge-preserving priors with random undersampling, and solves the resulting optimization using a set of discrete optimization methods called graph cuts. The resulting optimization problem is solved by applying graph cuts iteratively within a dictionary, defined here as an appropriately constructed set of vectors relevant to brain MRI data used here. Results: Experimental results with in vivo data are presented. Conclusion: The proposed algorithm produces better results than regularized SENSE or standard CS for reconstruction of in vivo data. K E Y W O R D S compressive sensing, graph cuts, parallel imaging, SENSE 1 | INTRODUCTION Undersampled MRI acquisitions can speed up MRI scan time, but also introduce aliasing artifacts due to sub-Nyquist sampling in k-space, if reconstructed using a simple inverse FFT of zero-filled k-space data. Aliasing is removed during reconstruction using advanced methods such as parallel imaging (PI), which uses the redundancy provided by multiple receiver coils, and by compressive sensing (CS), which exploits the noise-like aliasing artifacts resulting from random acquisition schemes. Traditional PI reconstruction algorithms include sensitivity encoding (SENSE), 1-3 simultaneous acquisition of spatial harmonics (SMASH), 4,5 and generalized auto-calibrating partially parallel acquisitions (GRAPPA). 6 Although these methods can be adapted for the case of arbitrary undersampling patterns, their usual implementation is for uniform Cartesian undersampling. Such undersampling results in structured aliasing artifacts called image folding, whereby many shifted copies of the true image are folded on top of each other. Uniform Cartesian PI methods can allow for acceleration of up to two-to fourfold, but further acceleration is limited

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Kolmogorov and Zabih’s Graph Cuts Stereo Matching Algorithm

Cited by 29 publications

References 7 publications

Hyperspectral Light Field Stereo Matching

Hyperspectral Light Field Stereo Matching

Occlusion detection in dense stereo estimation with convex optimization

A dictionary‐based graph‐cut algorithm for MRI reconstruction

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