Local Density Encoding for Robust Stereo Matching

Nguyen, Vinh Dinh; Nguyen, Duc Dung; Lee, Sang Jun; Jeon, Jae Wook

doi:10.1109/tcsvt.2014.2334053

Cited by 17 publications

(1 citation statement)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These two methods tend to overuse piece-wise constant assumption, leading to producing poor results. LDESGM [41] proposes a new local binary encoding pattern based on the intensity relationship between pixels in horizontal, vertical and diagonal directions, and combines this metric with magnitude information to solidify matching cost, then adopts SGM in eight directions to aggregate cost. Our method outperforms LDESGM with a great margin on both metrics, and the average error rates in non-occluded and all regions are reduced by 1.05% and 1.52% respectively.…”

Section: Kitti Datasetmentioning

confidence: 99%

Omni-Directional Semi-Global Stereo Matching with Reliable Information Propagation

Tian

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

High efficiency and accuracy of semi-global matching (SGM) make it widely used in many stereo vision applications. However, SGM not only struggles in dealing with pixels in homogeneous area, but also suffers from streak artifacts. In this paper, we propose a novel omni-directional SGM (OmniSGM) with a cost volume update scheme to aggregate costs from paths along all directions and to encourage reliable information to propagate across entire image. Specifically, we perform SGM along four tree structures, namely trees in the left, right, top and bottom of root node, and then fuse the outputs to obtain final result. The contributions of pixels on each tree can be recursively computed from leaf nodes to root node, ensuring our method has linear time computational complexity. Moreover, An iterative cost volume update scheme is proposed using aggregated cost in the last pass to enhance the robustness of initial matching cost. Thus, useful information is more likely to propagate in a long distance to handle the ambiguities in low textural area. Finally, we present an efficient strategy to propagate disparities of stable pixels along the minimum spanning tree (MST) for disparity refinement. Extensive experiments in stereo matching on Middlebury and KITTI datasets demonstrate that our method outperforms typical traditional SGM-based cost aggregation methods.

show abstract

Section: Kitti Datasetmentioning

confidence: 99%

Omni-Directional Semi-Global Stereo Matching with Reliable Information Propagation

Tian

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

show abstract

Fast and robust multimodal image registration using a local derivative pattern

et al. 2017

View full text Add to dashboard Cite

Purpose: Deformable multimodal image registration, which can benefit radiotherapy and image guided surgery by providing complementary information, remains a challenging task in the medical image analysis field due to the difficulty of defining a proper similarity measure. This article presents a novel, robust and fast binary descriptor, the discriminative local derivative pattern (dLDP), which is able to encode images of different modalities into similar image representations. Methods: dLDP calculates a binary string for each voxel according to the pattern of intensity derivatives in its neighborhood. The descriptor similarity is evaluated using the Hamming distance, which can be efficiently computed, instead of conventional L1 or L2 norms. For the first time, we validated the effectiveness and feasibility of the local derivative pattern for multimodal deformable image registration with several multi-modal registration applications. Results: dLDP was compared with three state-of-the-art methods in artificial image and clinical settings. In the experiments of deformable registration between different magnetic resonance imaging (MRI) modalities from BrainWeb, between computed tomography and MRI images from patient data, and between MRI and ultrasound images from BITE database, we show our method outperforms localized mutual information and entropy images in terms of both accuracy and time efficiency. We have further validated dLDP for the deformable registration of preoperative MRI and three-dimensional intraoperative ultrasound images. Our results indicate that dLDP reduces the average mean target registration error from 4.12 mm to 2.30 mm. This accuracy is statistically equivalent to the accuracy of the state-of-the-art methods in the study; however, in terms of computational complexity, our method significantly outperforms other methods and is even comparable to the sum of the absolute difference. Conclusions: The results reveal that dLDP can achieve superior performance regarding both accuracy and time efficiency in general multimodal image registration. In addition, dLDP also indicates the potential for clinical ultrasound guided intervention.

show abstract