Epipolar geometry estimation based on evolutionary agents

Hu, Mingxing; McMenemy, Karen; Ferguson, Stuart; Dodds, G.; Yuan, Baozong

doi:10.1016/j.patcog.2007.06.016

Cited by 18 publications

(12 citation statements)

References 18 publications

(14 reference statements)

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“…This is due to the fact that it reduces the number of potential inliers by thresholding the proposal probabilities that are, themselves, dependent on the robust estimation of normalized residuals. Several studies have attempted to apply evolutionary algorithms instead of the random search [29,30]. Although promising results were achieved, several limitations were not addressed yet.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection

2017

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“…This implies a large number of linear or iterative estimations, but on a limited number of correspondences. Following (Hu et al, 2008) the best known robust methods are LMedS (Least Median of Squares) (Zhang, 1998), RANSAC (RANdom SAmple Consensus) (Torr & Murray, 1997), MLESAC (Maximum Likelihood Estimation SAmple Consensus) (Torr & Zisserman, 2000) and MAPSAC (Maximum A Posteriori SAmple Consensus) (Torr, 2002). LMedS and RANSAC randomly sample some subset of seven matching points in order to estimate, with a linear approach, the model parameters and use additional statistical methods to derive a minimal number of samples needed since all possible subset can not be considered to save time; the difference between the two is the technique used to determine the best result: on one side the median distance between points and epipolar lines on the other the number of inliers.…”

Section: State Of the Artmentioning

confidence: 99%

“…Besides these classical algorithms, more recently a philosophically different approach has been proposed. Several authors (Chai & De Ma, 1998;Hu et al, 2002;2008) have employed a genetic computing paradigm to estimate epipolar geometry. The main idea is to employ an evolutionary approach in order to choose, among the available correspondences, the optimal, or sub-optimal, set of eight points by which the epipolar geometry estimation can be carried out with minimal error.…”

Section: State Of the Artmentioning

confidence: 99%

Evolutionary Approach to Epipolar Geometry Estimation

Taraglio¹,

Chiesa²

2011

Advances in Theory and Applications of Stereo Vision

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“…EAs are a stochastic search method which is inspired by natural selection and the principles of evolution [12,13]. EAs have received a great deal of attention in the recent past and are widely used in diverse areas of image processing [3,6,17,18,21,22]. This paper is organized as follows: In section 2, we briefly describe the background of epipolar geometry which is used for the calibration methods.…”

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confidence: 99%

Evolutionary-based 3D reconstruction using an uncalibrated stereovision system: application of building a panoramic object view

Koch

Dipandà

Bourgeois-Republique

2010

Multimed Tools Appl

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In this paper, we propose an original evolutionary-based method for 3D panoramic reconstruction from an uncalibrated stereovision system (USS). The USS is composed of five cameras located on an arc of a circle around the object to be analyzed. The main originality of this work concerns the process of the calculation of the 3D information. Actually, with our method, 3D coordinates are directly obtained without any prior estimation of the fundamental matrix. The method operates in two steps. Firstly, points of interest are detected in pairs of images acquired by two consecutive cameras of the USS are matched. And secondly, using evolutionary algorithms, we jointly compute the transformed matrix between the two images and the respective depth of the points of interest. The accuracy of the proposed method is validated through a comparison with the depth values obtained using a traditional method. In order to perform 3D panoramic object reconstruction, the process is repeated for all the pairs of consecutive cameras. The 3D points thus obtained throughout the successive steps of the process which correspond to the different points of interest, are combined in order to obtain a set of 3D points all around the analyzed object.Keywords Panoramic 3D reconstruction . Evolutionary algorithm . Uncalibrated system IntroductionPanoramic 3D reconstruction is a recent area of research which began about 10 years ago. It is used in various domains such as medicine [32], security [30], virtual reality or robotics Multimed Tools Appl (2012) 57:565-586

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Epipolar geometry estimation based on evolutionary agents

Cited by 18 publications

References 18 publications

Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection

Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection

Evolutionary Approach to Epipolar Geometry Estimation

Evolutionary-based 3D reconstruction using an uncalibrated stereovision system: application of building a panoramic object view

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