BetaSAC: A New Conditional Sampling For RANSAC

Meler, Antoine; Decrouez, Marion; Crowley, James L.

doi:10.5244/c.24.42

Cited by 11 publications

(3 citation statements)

References 14 publications

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“…Because the value selected for n is high to avoid mismatching, the RANSAC algorithm is time consuming [ 63 ] and has a high computational complexity when coupled with the SIFT algorithm [ 65 ]. In addition, as RANSAC is a non-deterministic algorithm [ 66 , 67 ], it does not guarantee the return of an optimal solution [ 68 ], resulting in different results for different runs [ 69 ]. Furthermore, when computed with few SIFT-derived keypoints, it can be sensitive to initial conditions [ 70 ].…”

Section: Methodsmentioning

confidence: 99%

Automatic Coregistration Algorithm to Remove Canopy Shaded Pixels in UAV-Borne Thermal Images to Improve the Estimation of Crop Water Stress Index of a Drip-Irrigated Cabernet Sauvignon Vineyard

Poblete

Ortega-Farías

Ryu

2018

Sensors

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Water stress caused by water scarcity has a negative impact on the wine industry. Several strategies have been implemented for optimizing water application in vineyards. In this regard, midday stem water potential (SWP) and thermal infrared (TIR) imaging for crop water stress index (CWSI) have been used to assess plant water stress on a vine-by-vine basis without considering the spatial variability. Unmanned Aerial Vehicle (UAV)-borne TIR images are used to assess the canopy temperature variability within vineyards that can be related to the vine water status. Nevertheless, when aerial TIR images are captured over canopy, internal shadow canopy pixels cannot be detected, leading to mixed information that negatively impacts the relationship between CWSI and SWP. This study proposes a methodology for automatic coregistration of thermal and multispectral images (ranging between 490 and 900 nm) obtained from a UAV to remove shadow canopy pixels using a modified scale invariant feature transformation (SIFT) computer vision algorithm and Kmeans++ clustering. Our results indicate that our proposed methodology improves the relationship between CWSI and SWP when shadow canopy pixels are removed from a drip-irrigated Cabernet Sauvignon vineyard. In particular, the coefficient of determination (R2) increased from 0.64 to 0.77. In addition, values of the root mean square error (RMSE) and standard error (SE) decreased from 0.2 to 0.1 MPa and 0.24 to 0.16 MPa, respectively. Finally, this study shows that the negative effect of shadow canopy pixels was higher in those vines with water stress compared with well-watered vines.

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Section: Methodsmentioning

confidence: 99%

Automatic Coregistration Algorithm to Remove Canopy Shaded Pixels in UAV-Borne Thermal Images to Improve the Estimation of Crop Water Stress Index of a Drip-Irrigated Cabernet Sauvignon Vineyard

Poblete

Ortega-Farías

Ryu

2018

Sensors

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“…However, the texture of a SIFT feature is susceptible to the change of viewpoint and illumination. In BaySAC & SimSAC [8] and BetaSAC [9], the false matches are identified according to the verification of the historical samples. However, inaccurate historical samples would lead to wrong identification results.…”

Section: Introductionmentioning

confidence: 99%

An improved RANSAC based on the scale variation homogeneity

Wang

Zheng

et al. 2016

Journal of Visual Communication and Image Representation

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For random sample consensus (RANSAC) method, high levels of contamination may slow down its convergence and impede the acquisition of the optimal model. In this paper, an improved RANSAC algorithm based on SIFT matches is proposed, which could provide robust performance in high levels of contamination and with low computational complexity. The improved RANSAC algorithm is particularly tailored to planar homography, by appropriately exploring feature scales. Firstly, a new feature scale constraint, i.e., the scale variation homogeneity (SVH), is proposed by exploiting the fact that the feature scale ratio of a correct match is approximate to the actual scale variation of two matched images. As a result, the potential correct and false matches can be effectively determined by SVH. Secondly, each model is scored by the number of partial identified false matches instead of all correct matches, which will speed up the evaluation process. Finally, fast converge can be obtained by adaptive selection of the test set for determining termination criterion according to the levels of contamination. Experimental results have demonstrated that the proposed SVH-RANSAC algorithm can perform very well even in the scenario of 90% contamination level. The number of iterations could be decreased by at least 35% and computational complexity for the evaluation procedure could be decreased by at least 2.5% compared with the LO +-RANSAC.

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“…Different approaches have been proposed to enhance RANSAC by improving the cost function [11,29] and the sampling strategy [3,4,13,15,18,19,23,25], or to deal with degenerate configurations [5,8] and allow faster model checks [14,20,22], but also other similar approaches exist [6,24,31].…”

Section: Introductionmentioning

confidence: 99%

noRANSAC for fundamental matrix estimation

Tegolo

Bellavia

2011

Procedings of the British Machine Vision Conference 2011

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The estimation of the fundamental matrix from a set of corresponding points is a relevant topic in epipolar stereo geometry [10]. Due to the high amount of outliers between the matches, RANSAC-based approaches [7,13,29] have been used to obtain the fundamental matrix. In this paper two new contributes are presented: a new normalized epipolar error measure which takes into account the shape of the features used as matches [17] and a new strategy to compare fundamental matrices. The proposed error measure gives good results and it does not depend on the image scale. Moreover, the new evaluation strategy describes a valid tool to compare different RANSAC-based methods because it does not rely on the inlier ratio, which could not correspond to the best allowable fundamental matrix estimated model, but it makes use of a reference ground truth fundamental matrix obtained by a set of corresponding points given by the user.

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BetaSAC: A New Conditional Sampling For RANSAC

Cited by 11 publications

References 14 publications

Automatic Coregistration Algorithm to Remove Canopy Shaded Pixels in UAV-Borne Thermal Images to Improve the Estimation of Crop Water Stress Index of a Drip-Irrigated Cabernet Sauvignon Vineyard

Automatic Coregistration Algorithm to Remove Canopy Shaded Pixels in UAV-Borne Thermal Images to Improve the Estimation of Crop Water Stress Index of a Drip-Irrigated Cabernet Sauvignon Vineyard

An improved RANSAC based on the scale variation homogeneity

noRANSAC for fundamental matrix estimation

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