Robust Stereo Image Matching for Spaceborne Imagery

Radhika, V N; Kartikeyan, B.; Krishna, B. Gopala; Chowdhury, S.; Srivastava, Prakhar

doi:10.1109/tgrs.2007.898238

Cited by 32 publications

(25 citation statements)

References 19 publications

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“…Accordingly, the inlier detection methods based on robust epipolar geometry estimation are more general than the methods discussed in this subsection; because they can be used for various types of images without being concerned about their photometric characteristics. Radhika et al (2009) suggest detecting the outliers as the matches that have disparity values noticeably far from the mode of the disparity values of their local neighbours. Determining a reliable threshold and size of the local neighborhood is a concerning factor in this approach.…”

Section: Inlier Detection Based On Other Spatial Characteristicsmentioning

confidence: 99%

Robust Sparse Matching and Motion Estimation Using Genetic Algorithms

Shahbazi

Sohn

Théau

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper, we propose a robust technique using genetic algorithm for detecting inliers and estimating accurate motion parameters from putative correspondences containing any percentage of outliers. The proposed technique aims to increase computational efficiency and modelling accuracy in comparison with the state-of-the-art via the following contributions: i) guided generation of initial populations for both avoiding degenerate solutions and increasing the rate of useful hypotheses, ii) replacing random search with evolutionary search, iii) possibility of evaluating the individuals of every population by parallel computation, iv) being performable on images with unknown internal orientation parameters, iv) estimating the motion model via detecting a minimum, however more than enough, set of inliers, v) ensuring the robustness of the motion model against outliers, degeneracy and poorperspective camera models, vi) making no assumptions about the probability distribution of inliers and/or outliers residuals from the estimated motion model, vii) detecting all the inliers by setting the threshold on their residuals adaptively with regard to the uncertainty of the estimated motion model and the position of the matches. The proposed method was evaluated both on synthetic data and real images. The results were compared with the most popular techniques from the state-of-the-art, including RANSAC, MSAC, MLESAC, Least Trimmed Squares and Least Median of Squares. Experimental results proved that the proposed approach perform better than others in terms of accuracy of motion estimation, accuracy of inlier detection and the computational efficiency.

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Section: Inlier Detection Based On Other Spatial Characteristicsmentioning

confidence: 99%

Robust Sparse Matching and Motion Estimation Using Genetic Algorithms

Shahbazi

Sohn

Théau

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…These algorithms present important problems in terms of time performance, around hours in 3 GHz CPU for two images matching (Radhika et al, 2007).…”

Section: The Matching Problemmentioning

confidence: 99%

Depth Estimation - An Introduction

Sanz¹,

Ruíz‐Mezcua²,

Sánchez‐Pena³

2012

Current Advancements in Stereo Vision

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“…Figure 1 presents the summary of the outlier detection techniques that are discussed in Section 2. In addition to the techniques using epipolar geometry, several methods are based on other spatial criteria, such as the distribution of parallax values [10,11], spatial patterns of outliers [12], orientations of the lines connecting inliers [13] and two-way spatial order differences [14]. However, such methods use spatial consistency measures that are less general and valid for specific imaging configurations.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection

2017

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Abstract:In this paper, a robust technique based on a genetic algorithm is proposed for estimating two-view epipolar-geometry of uncalibrated perspective stereo images from putative correspondences containing a high percentage of outliers. The advantages of this technique are three-fold: (i) replacing random search with evolutionary search applying new strategies of encoding and guided sampling; (ii) robust and fast estimation of the epipolar geometry via detecting a more-than-enough set of inliers without making any assumptions about the probability distribution of the residuals; (iii) determining the inlier-outlier threshold based on the uncertainty of the estimated model. The proposed method was evaluated both on synthetic data and real images. The results were compared with the most popular techniques from the state-of-the-art, including RANSAC (random sample consensus), MSAC, MLESAC, Cov-RANSAC, LO-RANSAC, StaRSAC, Multi-GS RANSAC and least median of squares (LMedS). Experimental results showed that the proposed approach performed better than other methods regarding the accuracy of inlier detection and epipolar-geometry estimation, as well as the computational efficiency for datasets majorly contaminated by outliers and noise.

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Robust Stereo Image Matching for Spaceborne Imagery

Cited by 32 publications

References 19 publications

Robust Sparse Matching and Motion Estimation Using Genetic Algorithms

Robust Sparse Matching and Motion Estimation Using Genetic Algorithms

Depth Estimation - An Introduction

Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection

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