Hierarchical Stereo Matching in Two-Scale Space for Cyber-Physical System

Choi, Eunah; Lee, Sang Yoon; Hong, Hyun-Ki

doi:10.3390/s17071680

Cited by 4 publications

(4 citation statements)

References 30 publications

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“…As explored in the literature in [33,34], the best improvements for the multi-scale approach are visible across areas with different frequencies: in high-frequencies areas high-resolution images can obtain the best results, while in low-textured and low-frequency areas a lower resolution leads towards more robust estimation.…”

Section: Depth Map Calculationmentioning

confidence: 99%

Robust Depth Estimation for Light Field Microscopy

Palmieri

Scrofani

Incardona

et al. 2019

Sensors

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Light field technologies have seen a rise in recent years and microscopy is a field where such technology has had a deep impact. The possibility to provide spatial and angular information at the same time and in a single shot brings several advantages and allows for new applications. A common goal in these applications is the calculation of a depth map to reconstruct the three-dimensional geometry of the scene. Many approaches are applicable, but most of them cannot achieve high accuracy because of the nature of such images: biological samples are usually poor in features and do not exhibit sharp colors like natural scene. Due to such conditions, standard approaches result in noisy depth maps. In this work, a robust approach is proposed where accurate depth maps can be produced exploiting the information recorded in the light field, in particular, images produced with Fourier integral Microscope. The proposed approach can be divided into three main parts. Initially, it creates two cost volumes using different focal cues, namely correspondences and defocus. Secondly, it applies filtering methods that exploit multi-scale and super-pixels cost aggregation to reduce noise and enhance the accuracy. Finally, it merges the two cost volumes and extracts a depth map through multi-label optimization.

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Section: Depth Map Calculationmentioning

confidence: 99%

Robust Depth Estimation for Light Field Microscopy

Palmieri

Scrofani

Incardona

et al. 2019

Sensors

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“…SGM with surface orientation priories is used for stereo matching (Daniel et al, Scharstein, 2017). Eunah et al (2017) proposes a hierarchical stereo matching approach for low resolution images. Andreas et al (2010) uses triangulation on a set of support points for robust stereo matching.…”

Section: Related Workmentioning

confidence: 99%

“…Hirschmuller (2008) Ramin and Woodfill (1994) used non-parametric approach for stereo matching. Eunah et al (2017) proposes a hierarchical stereo matching approach in twoscale space for low resolution images. Andreas et al (2010) uses triangulation on a set of support points for robust stereo matching.…”

Section: Performance Evaluationmentioning

confidence: 99%

Radiometric Invariant Dense Disparity Estimation for Real Time Stereo Correspondence

Deepambika¹,

Rahiman²

2019

Journal of Computer Science

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Computer stereo vision tries to mimic human vision by grabbing multiple views of the same scene and cognizing it. The stereo correspondence will find out the matching pixels between the two views based on the Lambertian criteria, which results in disparity. The distance of the objects from the camera can be calculated using this disparity. But in the real world scenario, this Lambertian assumption may not work always due to the radiometric variation between the image pairs and the conventional approaches results in erroneous disparity. In this work, for doing the radiometric invariant stereo matching, the simple local binary pattern is used. The correspondence is done by using semi global block matching method, which can handle the depth changes of curved surfaces and slanting surfaces by adding suitable penalty terms. The performance evaluation of the proposed shows lesser error rate in the range of 0.14%-0.3883% and run time requirement of 0.20 milliseconds only. This radiometric invariant stereo correspondence attains accuracy as that of global method with run time speed as that of local method and is suitable for most of the real time stereo vision applications.

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“…According to the workflow in [1], a typical stereo matching process consists of four main steps, namely the matching cost computation, cost aggregation, disparity optimization, and disparity refinement. Each step in this pipeline has been extensively studied, and several advanced methods have been proposed [2][3][4][5][6]. Although this workflow performs well, the stepwise pipeline lacks an overall objective function for global optimization, and thus may suffer errors in each step [7].…”

Section: Introductionmentioning

confidence: 99%

Dual Guided Aggregation Network for Stereo Image Matching

Wang

Lin

2022

Sensors

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Stereo image dense matching, which plays a key role in 3D reconstruction, remains a challenging task in photogrammetry and computer vision. In addition to block-based matching, recent studies based on artificial neural networks have achieved great progress in stereo matching by using deep convolutional networks. This study proposes a novel network called a dual guided aggregation network (Dual-GANet), which utilizes both left-to-right and right-to-left image matchings in network design and training to reduce the possibility of pixel mismatch. Flipped training with a cost volume consistentization is introduced to realize the learning of invisible-to-visible pixel matching and left–right consistency matching. In addition, suppressed multi-regression is proposed, which suppresses unrelated information before regression and selects multiple peaks from a disparity probability distribution. The proposed dual network with the left–right consistent matching scheme can be applied to most stereo matching models. To estimate the performance, GANet, which is designed based on semi-global matching, was selected as the backbone with extensions and modifications on guided aggregation, disparity regression, and loss function. Experimental results on the SceneFlow and KITTI2015 datasets demonstrate the superiority of the Dual-GANet compared to related models in terms of average end-point-error (EPE) and pixel error rate (ER). The Dual-GANet with an average EPE performance = 0.418 and ER (>1 pixel) = 5.81% for SceneFlow and average EPE = 0.589 and ER (>3 pixels) = 1.76% for KITTI2005 is better than the backbone model with the average EPE performance of = 0.440 and ER (>1 pixel) = 6.56% for SceneFlow and average EPE = 0.790 and ER (>3 pixels) = 2.32% for KITTI2005.

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Hierarchical Stereo Matching in Two-Scale Space for Cyber-Physical System

Cited by 4 publications

References 30 publications

Robust Depth Estimation for Light Field Microscopy

Robust Depth Estimation for Light Field Microscopy

Radiometric Invariant Dense Disparity Estimation for Real Time Stereo Correspondence

Dual Guided Aggregation Network for Stereo Image Matching

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