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1

Joon Hee Han and Jong Seung Park. "Contour matching using epipolar geometry." IEEE Transactions on Pattern Analysis and Machine Intelligence 22, no. 4 (April 2000): 358–70. http://dx.doi.org/10.1109/34.845378.

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2

Goldstein, Amit, and Raanan Fattal. "Video stabilization using epipolar geometry." ACM Transactions on Graphics 31, no. 5 (August 6, 2012): 1–10. http://dx.doi.org/10.1145/2231816.2231824.

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3

Brandt, Sami S. "On the probabilistic epipolar geometry." Image and Vision Computing 26, no. 3 (March 2008): 405–14. http://dx.doi.org/10.1016/j.imavis.2006.12.002.

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4

Hosseinyalamdary, S., and A. Yilmaz. "Motion Vector Field Estimation Using Brightness Constancy Assumption and Epipolar Geometry Constraint." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-1 (November 7, 2014): 9–16. http://dx.doi.org/10.5194/isprsannals-ii-1-9-2014.

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In most Photogrammetry and computer vision tasks, finding the corresponding points among images is required. Among many, the Lucas-Kanade optical flow estimation has been employed for tracking interest points as well as motion vector field estimation. This paper uses the IMU measurements to reconstruct the epipolar geometry and it integrates the epipolar geometry constraint with the brightness constancy assumption in the Lucas-Kanade method. The proposed method has been tested using the KITTI dataset. The results show the improvement in motion vector field estimation in comparison to the Lucas-Kanade optical flow estimation. The same approach has been used in the KLT tracker and it has been shown that using epipolar geometry constraint can improve the KLT tracker. It is recommended that the epipolar geometry constraint is used in advanced variational optical flow estimation methods.
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5

Gong, D., Y. Han, and L. Zhang. "QUANTITATIVE ASSESSMENT OF THE PROJECTION TRAJECTORY-BASED EPIPOLARITY MODEL AND EPIPOLAR IMAGE RESAMPLING FOR LINEAR-ARRAY SATELLITE IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-1-2020 (August 3, 2020): 89–94. http://dx.doi.org/10.5194/isprs-annals-v-1-2020-89-2020.

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Abstract. Epipolar geometry rectification is one of the critical issues in photogrammetry, which is a strong corresponding searching constraint in dense image matching for 3D reconstruction. In this paper, the properties of the projection trajectory-based epipolarity model are analyzed quantitatively, and the approximate straight line and parallelism property of the epipolar curve are discussed comprehensively using the linear pushbroom satellite images, i.e. IKONOS, GeoEye images. Based on the analysis of the epipolar line properties, a practical method for epipolar resampling developed. In this method, the pixelwise relationship is established between the original and the epipolar images. The experiments on TH-1 images show that quasi rigorous epipolar images can be resampled using our proposed method for both along-track images. After epipolar geometry rectification, the vertical parallaxes at checkpoints can achieve sub-pixel level accuracy, thus demonstrating the correctness and applicability of the proposed method.
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6

Benosman, R., Sio-Hoï Ieng, P. Rogister, and C. Posch. "Asynchronous Event-Based Hebbian Epipolar Geometry." IEEE Transactions on Neural Networks 22, no. 11 (November 2011): 1723–34. http://dx.doi.org/10.1109/tnn.2011.2167239.

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7

PIAO, Y., and J. SATO. "Computing Epipolar Geometry from Unsynchronized Cameras." IEICE Transactions on Information and Systems E91-D, no. 8 (August 1, 2008): 2171–78. http://dx.doi.org/10.1093/ietisy/e91-d.8.2171.

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8

Kalisperakis, I., G. Karras, and E. Petsa. "A EUCLIDEAN FORMULATION OF INTERIOR ORIENTATION COSTRAINTS IMPOSED BY THE FUNDAMENTAL MATRIX." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 3, 2016): 75–82. http://dx.doi.org/10.5194/isprsannals-iii-3-75-2016.

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Epipolar geometry of a stereopair can be expressed either in 3D, as the relative orientation (i.e. translation and rotation) of two bundles of optical rays in case of calibrated cameras or, in case of unclalibrated cameras, in 2D as the position of the epipoles on the image planes and a projective transformation that maps points in one image to corresponding epipolar lines on the other. The typical coplanarity equation describes the first case; the Fundamental matrix describes the second. It has also been proven in the Computer Vision literature that 2D epipolar geometry imposes two independent constraints on the parameters of camera interior orientation. In this contribution these constraints are expressed directly in 3D Euclidean space by imposing the equality of the dihedral angle of epipolar planes defined by the optical axes of the two cameras or by suitably chosen corresponding epipolar lines. By means of these constraints, new closed form algorithms are proposed for the estimation of a variable or common camera constant value given the fundamental matrix and the principal point position of a stereopair.
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9

Kalisperakis, I., G. Karras, and E. Petsa. "A EUCLIDEAN FORMULATION OF INTERIOR ORIENTATION COSTRAINTS IMPOSED BY THE FUNDAMENTAL MATRIX." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 3, 2016): 75–82. http://dx.doi.org/10.5194/isprs-annals-iii-3-75-2016.

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Epipolar geometry of a stereopair can be expressed either in 3D, as the relative orientation (i.e. translation and rotation) of two bundles of optical rays in case of calibrated cameras or, in case of unclalibrated cameras, in 2D as the position of the epipoles on the image planes and a projective transformation that maps points in one image to corresponding epipolar lines on the other. The typical coplanarity equation describes the first case; the Fundamental matrix describes the second. It has also been proven in the Computer Vision literature that 2D epipolar geometry imposes two independent constraints on the parameters of camera interior orientation. In this contribution these constraints are expressed directly in 3D Euclidean space by imposing the equality of the dihedral angle of epipolar planes defined by the optical axes of the two cameras or by suitably chosen corresponding epipolar lines. By means of these constraints, new closed form algorithms are proposed for the estimation of a variable or common camera constant value given the fundamental matrix and the principal point position of a stereopair.
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10

Boufama, Boubakeur S., and Roger Mohr. "A Stable and Accurate Algorithm for Computing Epipolar Geometry." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 06 (September 1998): 817–40. http://dx.doi.org/10.1142/s0218001498000452.

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This paper addresses the problem of computing the fundamental matrix which describes a geometric relationship between a pair of stereo images: the epipolar geometry. In the uncalibrated case, epipolar geometry captures all the 3D information available from the scene. It is of central importance for problems such as 3D reconstruction, self-calibration and feature tracking. Hence, the computation of the fundamental matrix is of great interest. The existing classical methods14 use two steps: a linear step followed by a nonlinear one. However, in some cases, the linear step does not yield a close form solution for the fundamental matrix, resulting in more iterations for the nonlinear step which is not guaranteed to converge to the correct solution. In this paper, a novel method based on virtual parallax is proposed. The problem is formulated differently; instead of computing directly the 3 × 3 fundamental matrix, we compute a homography with one epipole position, and show that this is equivalent to computing the fundamental matrix. Simple equations are derived by reducing the number of parameters to estimate. As a consequence, we obtain an accurate fundamental matrix with a stable linear computation. Experiments with simulated and real images validate our method and clearly show the improvement over the classical 8-point method.
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11

Dazhi, Zhang, Wang Yongtao, and Tao Wenbing. "Epipolar Geometry Estimation for Wide Baseline Stereo." International Journal of Engineering and Manufacturing 2, no. 3 (June 29, 2012): 38–45. http://dx.doi.org/10.5815/ijem.2012.03.06.

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12

Takimoto, Rogério Yugo, André Challella das Neves, Thiago de Castro Martins, Fábio Kawaoka Takase, and Marcos de Sales Guerra Tsuzuki. "Automatic Epipolar Geometry Recovery Using Two Images." IFAC Proceedings Volumes 44, no. 1 (January 2011): 3980–85. http://dx.doi.org/10.3182/20110828-6-it-1002.01656.

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13

Zhou, Jun. "Epipolar Geometry Estimation Using Improved LO-RANSAC." Advanced Materials Research 213 (February 2011): 255–59. http://dx.doi.org/10.4028/www.scientific.net/amr.213.255.

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The estimation of the epipolar geometry is of great interest for a number of computer vision and robotics tasks, and which is especially difficult when the putative correspondences include a low percentage of inliers correspondences or a large subset of the inliers is consistent with a degenerate configuration of the epipolar geometry that is totally incorrect. The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation, primarily due to its ability to tolerate a tremendous fraction of outliers. In this paper, we propose an approach for improve of locally optimized RANSAC (LO-RANSAC) that has the benefit of offering fast and accurate RANSAC. The resulting algorithm when tested on real images with or without degenerate configurations gives quality estimations and achieves significant speedups compared to the LO-RANSAC algorithms.
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14

Chai, Jinxiang, and Song De Ma. "Robust epipolar geometry estimation using genetic algorithm." Pattern Recognition Letters 19, no. 9 (July 1998): 829–38. http://dx.doi.org/10.1016/s0167-8655(98)00032-4.

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15

Negahdaripour, S. "Epipolar Geometry of Opti-Acoustic Stereo Imaging." IEEE Transactions on Pattern Analysis and Machine Intelligence 29, no. 10 (October 2007): 1776–88. http://dx.doi.org/10.1109/tpami.2007.1092.

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16

Hu, Mingxing, Karen McMenemy, Stuart Ferguson, Gordon Dodds, and Baozong Yuan. "Epipolar geometry estimation based on evolutionary agents." Pattern Recognition 41, no. 2 (February 2008): 575–91. http://dx.doi.org/10.1016/j.patcog.2007.06.016.

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17

Mendonca, P. R. S., K. Y. K. Wong, and R. Cipolla. "Epipolar geometry from profiles under circular motion." IEEE Transactions on Pattern Analysis and Machine Intelligence 23, no. 6 (June 2001): 604–16. http://dx.doi.org/10.1109/34.927461.

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18

Cleju, Ioan, and Dietmar Saupe. "Evaluation of texture registration by epipolar geometry." Visual Computer 26, no. 11 (March 12, 2010): 1407–20. http://dx.doi.org/10.1007/s00371-010-0427-0.

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19

Shapiro, Larry S., Andrew Zisserman, and Michael Brady. "3D Motion recovery via affine Epipolar geometry." International Journal of Computer Vision 16, no. 2 (October 1995): 147–82. http://dx.doi.org/10.1007/bf01539553.

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20

Ortín, D., and J. M. M. Montiel. "Indoor robot motion based on monocular images." Robotica 19, no. 3 (April 25, 2001): 331–42. http://dx.doi.org/10.1017/s0263574700003143.

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The estimation of the 2D relative motion of an indoor robot using monocular vision is presented. The camera calibration is known, and its motion is limited to be planar. These constraints are included in the robust regression of epipolar geometry from point matches. Motion is derived from the epipolar geometry. A sequence of 54 real images is used to test the algorithm. Accurate motion, both in rotation and translation angles of 0.4 and 1.7 deg, is successfully derived.
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21

Yuan, Jian Ying, Xian Yong Liu, and Zhi Qiang Qiu. "A Robust Feature Points Matching Algorithm in 3D Optical Measuring System." Advanced Materials Research 383-390 (November 2011): 5193–99. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.5193.

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In optical measuring system with a handheld digital camera, image points matching is very important for 3-dimensional(3D) reconstruction. The traditional matching algorithms are usually based on epipolar geometry or multi-base lines. Mistaken matching points can not be eliminated by epipolar geometry and many matching points will be lost by multi-base lines. In this paper, a robust algorithm is presented to eliminate mistaken matching feature points in the process of 3D reconstruction from multiple images. The algorithm include three steps: (1) pre-matching the feature points using constraints of epipolar geometry and image topological structure firstly; (2) eliminating the mistaken matching points by the principle of triangulation in multi-images; (3) refining camera external parameters by bundle adjustment. After the external parameters of every image refined, repeat step (1) to step (3) until all the feature points been matched. Comparative experiments with real image data have shown that mistaken matching feature points can be effectively eliminated, and nearly no matching points have been lost, which have a better performance than traditonal matching algorithms do.
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22

Kupervasser, O. "Recovering epipolar geometry from images of smooth surfaces." Pattern Recognition and Image Analysis 23, no. 2 (June 2013): 236–57. http://dx.doi.org/10.1134/s1054661813020107.

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23

KHALED, NEHAL, ELSAYED E. HEMAYED, and MAGDA B. FAYEK. "A GA-BASED APPROACH FOR EPIPOLAR GEOMETRY ESTIMATION." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 08 (December 2013): 1355014. http://dx.doi.org/10.1142/s0218001413550148.

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In this paper, a genetic algorithm (GA)-based approach to estimate the fundamental matrix is presented. The aim of the proposed GA-based algorithm is to reduce the effect of noise and outliers in the corresponding points which affect the accuracy of the estimated fundamental matrix. Although in the proposed approach the GA is allowed to select the significant among all detected points, on the average half of the matched points have been determined to give optimum estimation of the fundamental matrix. Experiments with synthetic and real data show that the proposed approach is accurate especially in the presence of a high percentage of outliers. The proposed GA can always obtain good results in both high and low detailed images. Even for low detailed images which have a small number of matched points available to estimate the fundamental matrix, the proposed GA outperformed other methods.
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24

Cui, Xiaoyu, Heyu Fan, Hongsheng Chen, Shuo Chen, Yue Zhao, and Kahbin Lim. "Epipolar geometry for prism-based single-lens stereovision." Machine Vision and Applications 28, no. 3-4 (January 30, 2017): 313–26. http://dx.doi.org/10.1007/s00138-017-0822-x.

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25

Goldman, Yehonatan, Ehud Rivlin, and Ilan Shimshoni. "Robust epipolar geometry estimation using noisy pose priors." Image and Vision Computing 67 (November 2017): 16–28. http://dx.doi.org/10.1016/j.imavis.2017.09.006.

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26

Tatar, N., M. Saadatseresht, H. Arefi, and A. Hadavand. "QUASI-EPIPOLAR RESAMPLING OF HIGH RESOLUTION SATELLITE STEREO IMAGERY FOR SEMI GLOBAL MATCHING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 707–12. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-707-2015.

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Semi-global matching is a well-known stereo matching algorithm in photogrammetric and computer vision society. Epipolar images are supposed as input of this algorithm. Epipolar geometry of linear array scanners is not a straight line as in case of frame camera. Traditional epipolar resampling algorithms demands for rational polynomial coefficients (RPCs), physical sensor model or ground control points. In this paper we propose a new solution for epipolar resampling method which works without the need for these information. In proposed method, automatic feature extraction algorithms are employed to generate corresponding features for registering stereo pairs. Also original images are divided into small tiles. In this way by omitting the need for extra information, the speed of matching algorithm increased and the need for high temporal memory decreased. Our experiments on GeoEye-1 stereo pair captured over Qom city in Iran demonstrates that the epipolar images are generated with sub-pixel accuracy.
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27

Luber, A., D. Rueß, K. Manthey, and R. Reulke. "CALIBRATION AND EPIPOLAR GEOMETRY OF GENERIC HETEROGENOUS CAMERA SYSTEMS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B5 (July 30, 2012): 363–68. http://dx.doi.org/10.5194/isprsarchives-xxxix-b5-363-2012.

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28

Yoshikawa, Yuichiro, Minoru Asada, and Koh Hosoda. "Imitation based on Demonstrator's View Recovery Utilizing Epipolar Geometry." Journal of the Robotics Society of Japan 22, no. 1 (2004): 68–74. http://dx.doi.org/10.7210/jrsj.22.68.

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29

Kushnir, Maria, and Ilan Shimshoni. "Epipolar Geometry Estimation for Urban Scenes with Repetitive Structures." IEEE Transactions on Pattern Analysis and Machine Intelligence 36, no. 12 (December 1, 2014): 2381–95. http://dx.doi.org/10.1109/tpami.2014.2339862.

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30

Zhou, Huiyu, Patrick R. Green, and Andrew M. Wallace. "Estimation of epipolar geometry by linear mixed-effect modelling." Neurocomputing 72, no. 16-18 (October 2009): 3881–90. http://dx.doi.org/10.1016/j.neucom.2009.04.018.

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31

Habib, Ayman F., Michel Morgan, Soo Jeong, and Kyung-Ok Kim. "Analysis of Epipolar Geometry in Linear Array Scanner Scenes." Photogrammetric Record 20, no. 109 (March 2005): 27–47. http://dx.doi.org/10.1111/j.1477-9730.2005.00303.x.

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32

Bjorkman, M., and J. O. Eklundh. "Real-time epipolar geometry estimation of binocular stereo heads." IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no. 3 (March 2002): 425–32. http://dx.doi.org/10.1109/34.990147.

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33

Hata, Koichi, and Minoru Eto. "Epipolar geometry estimation method based on maximizing image correlation." Electronics and Communications in Japan (Part II: Electronics) 86, no. 3 (February 6, 2003): 73–81. http://dx.doi.org/10.1002/ecjb.10137.

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34

Liu, Kai, Kangkang Zhang, Jinghe Wei, Jianwen Song, Daniel L. Lau, Ce Zhu, and Bin Xu. "Extending epipolar geometry for real-time structured light illumination." Optics Letters 45, no. 12 (June 11, 2020): 3280. http://dx.doi.org/10.1364/ol.390212.

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35

Izquierdo, E., and V. Guerra. "Improving efficiency of linear techniques to estimate epipolar geometry." Electronics Letters 37, no. 15 (2001): 952. http://dx.doi.org/10.1049/el:20010675.

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36

Oskarsson, Magnus. "Two-View Orthographic Epipolar Geometry: Minimal and Optimal Solvers." Journal of Mathematical Imaging and Vision 60, no. 2 (July 27, 2017): 163–73. http://dx.doi.org/10.1007/s10851-017-0753-1.

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37

Wu, Hsien-Huang P., Meng-Tu Lee, Ping-Kuo Weng, and Soon-Lin Chen. "Epipolar geometry of catadioptric stereo systems with planar mirrors." Image and Vision Computing 27, no. 8 (July 2009): 1047–61. http://dx.doi.org/10.1016/j.imavis.2008.09.007.

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38

Wang, Guijin, Chenchen Feng, Xiaowei Hu, Hang Wang, and Huazhong Yang. "Epipolar Geometry Guided Highly Robust Structured Light 3D Imaging." IEEE Signal Processing Letters 28 (2021): 887–91. http://dx.doi.org/10.1109/lsp.2021.3073266.

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39

Akilan, A., D. Sudheer Reddy, V. Nagasubramanian, P. V. Radhadevi, and G. Varadan. "Epipolar Rectification for CARTOSAT-1 Stereo Images Using SIFT and RANSAC." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 1095–98. http://dx.doi.org/10.5194/isprsarchives-xl-8-1095-2014.

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Cartosat-1 provides stereo images of spatial resolution 2.5 m with high fidelity of geometry. Stereo camera on the spacecraft has look angles of +26 degree and -5 degree respectively that yields effective along track stereo. Any DSM generation algorithm can use the stereo images for accurate 3D reconstruction and measurement of ground. Dense match points and pixel-wise matching are prerequisite in DSM generation to capture discontinuities and occlusions for accurate 3D modelling application. Epipolar image matching reduces the computational effort from two dimensional area searches to one dimensional. Thus, epipolar rectification is preferred as a pre-processing step for accurate DSM generation. In this paper we explore a method based on SIFT and RANSAC for epipolar rectification of cartosat-1 stereo images.
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40

Maas, H. G. "A MODULAR GEOMETRIC MODEL FOR UNDERWATER PHOTOGRAMMETRY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5/W5 (April 9, 2015): 139–41. http://dx.doi.org/10.5194/isprsarchives-xl-5-w5-139-2015.

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Underwater applications of photogrammetric measurement techniques usually need to deal with multimedia photogrammetry aspects, which are characterized by the necessity of handling optical rays that are broken at interfaces between optical media with different refrative indices according to Snell’s Law. This so-called multimedia geometry has to be incorporated into geometric models in order to achieve correct measurement results. <br><br> The paper shows a flexible yet strict geometric model for the handling of refraction effects on the optical path, which can be implemented as a module into photogrammetric standard tools such as spatial resection, spatial intersection, bundle adjustment or epipolar line computation. The module is especially well suited for applications, where an object in water is observed by cameras in air through one or more plane parallel glass interfaces, as it allows for some simplifications here.
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41

Shahbazi, Mozhdeh, Gunho Sohn, and Jérôme Théau. "Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection." Algorithms 10, no. 3 (July 27, 2017): 87. http://dx.doi.org/10.3390/a10030087.

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42

Chi-Keung Tang, G. Medioni, and Mi-Suen Lee. "N-dimensional tensor voting and application to epipolar geometry estimation." IEEE Transactions on Pattern Analysis and Machine Intelligence 23, no. 8 (2001): 829–44. http://dx.doi.org/10.1109/34.946987.

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43

Li, Chao, and Yue Zhao. "Approach of Camera Relative Pose Estimation Based on Epipolar Geometry." Information Technology Journal 11, no. 9 (August 15, 2012): 1202–10. http://dx.doi.org/10.3923/itj.2012.1202.1210.

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44

Adachi, Jun, and Jun Sato. "Uncalibrated 3D visual servoing from epipolar geometry and projective invariants." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 90, no. 12 (2007): 50–58. http://dx.doi.org/10.1002/ecjc.20361.

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45

Lai, Taotao, Hanzi Wang, Yan Yan, Guobao Xiao, and David Suter. "Efficient guided hypothesis generation for multi-structure epipolar geometry estimation." Computer Vision and Image Understanding 154 (January 2017): 152–65. http://dx.doi.org/10.1016/j.cviu.2016.10.003.

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46

Liu, Deyang, Yan Huang, Qiang Wu, Ran Ma, and Ping An. "Multi-Angular Epipolar Geometry Based Light Field Angular Reconstruction Network." IEEE Transactions on Computational Imaging 6 (2020): 1507–22. http://dx.doi.org/10.1109/tci.2020.3037413.

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47

Wei, Ning, Chao Hu, Qing He, Wei Liu, and Q. H. Meng. "Angle Constraints for Point Correspondence in Multi-Ocular Vision." Applied Mechanics and Materials 58-60 (June 2011): 1384–89. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1384.

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Geometric constraints have an essential significance for point correspondence in computer vision systems. Traditional epipolar constraint in bi-ocular system faces two main problems: threshold-setting and corresponding ambiguities. This paper describes a collinear epipolar plane model and proposes a novel criterion for bi-ocular corresponding which allows setting a uniform threshold. Furthermore, it proposes the concept tri-correspondence units, proves their specificity against ambiguities, and discusses the merging of them.
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48

DAO, Vinh Ninh, and Masanori SUGIMOTO. "A Dynamic Geometry Reconstruction Technique for Mobile Devices Using Adaptive Checkerboard Recognition and Epipolar Geometry." IEICE Transactions on Information and Systems E94-D, no. 2 (2011): 336–48. http://dx.doi.org/10.1587/transinf.e94.d.336.

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49

Duan, Jia, Yuan Yan Tang, Zhen Chao Zhang, Chu Yu Guo, and Chi Fang. "Camera Calibration and Improved Computation of Fundamental Matrix in Epipolar Geometry." Applied Mechanics and Materials 347-350 (August 2013): 3624–28. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3624.

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In this paper, firstly we try to look for ways to avoid the camera parameters in order to reconstruct 3D model. We attempt to use the parallel stereo visual system and carry out the mathematical derivation of argumentation. Then we use epipolar geometry to solve this problem. And compare the computation algorithms of fundamental matrix. Then for the algorithm, we propose some improvement to compute the fundament matrix more precisely so that the algorithm is more stable and the robustness is stronger.
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50

Goyal, Vasundhara, and Dan Schonfeld. "Generation of Stereoscopic Image Sequences from Monocular Videos Using Epipolar Geometry." Electronic Imaging 2018, no. 2 (January 28, 2018): 265–1. http://dx.doi.org/10.2352/issn.2470-1173.2018.2.vipc-265.

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