Automatic Homographic Registration of a Pair of Images, with A Contrario Elimination of Outliers

Moisan, Lionel; Moulon, Pierre; Monasse, Pascal

doi:10.5201/ipol.2012.mmm-oh

Cited by 131 publications

(125 citation statements)

References 6 publications

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“…VLD is incorporated to SM (2 nd -order method) and HGM. For calibration, we used the IPOL implementation of ORSA [22,23], which is a parameterless, state-of-art RANSAC variant.…”

Section: Discussionmentioning

confidence: 99%

Virtual Line Descriptor and Semi-Local Graph Matching Method for Reliable Feature Correspondence

Liu¹,

Marlet²

2012

Procedings of the British Machine Vision Conference 2012

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Finding reliable correspondences between sets of feature points in two images remains challenging in case of ambiguities or strong transformations. In this paper, we define a photometric descriptor for virtual lines that join neighbouring feature points. We show that it can be used in the second-order term of existing graph matchers to significantly improve their accuracy. We also define a semi-local matching method based on this descriptor. We show that it is robust to strong transformations and more accurate than existing graph matchers for scenes with significant occlusions, including for very low inlier rates. Used as a preprocessor to filter outliers from match candidates, it significantly improves the robustness of RANSAC and reduces camera calibration errors.

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“…VLD is incorporated to SM (2 nd -order method) and HGM. For calibration, we used the IPOL implementation of ORSA [22,23], which is a parameterless, state-of-art RANSAC variant.…”

Section: Discussionmentioning

confidence: 99%

Virtual Line Descriptor and Semi-Local Graph Matching Method for Reliable Feature Correspondence

Liu¹,

Marlet²

2012

Procedings of the British Machine Vision Conference 2012

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“…-Threshold priors through MaxConsensus and RANSAC (RANdom SAmple Consensus) [26] -Threshold free with Leat Median of Squares (LMedS) and a contrario-RANSAC [27,28].…”

Section: Robust Estimationmentioning

confidence: 99%

OpenMVG: Open Multiple View Geometry

Moulon

Monasse

Perrot

et al. 2017

Lecture Notes in Computer Science

Self Cite

300

251

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Abstract. The OpenMVG C++ library provides a vast collection of multipleview geometry tools and algorithms to spread the usage of computer vision and structure-from-motion techniques. Close to the state-of-the-art in its domain, it provides an easy access to common tools used in 3D reconstruction from images. Following the credo "Keep it simple, keep it maintainable" the library is designed as a modular collection of algorithms, libraries and binaries that can be used independently or as bricks to build larger systems. Thanks to its strict test driven development, the library is packaged with unit-test code samples that make the library easy to learn, modify and use. Since its first release in 2013 under the MPL2 license, OpenMVG has gathered an active community of users and contributors from many fields, spanning hobbyists, students, computer vision experts, and industry members.

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“…were kept. In our case, as all tests were based on planar transformations, the ORSA homography detector [41] (a parameterless variant of RANSAC) was applied to filter out matches not compatible with the dominant homography.…”

Section: Experimental Validationmentioning

confidence: 99%

Covering the Space of Tilts. Application to Affine Invariant Image Comparison

Rodríguez¹,

Delon²,

Morel³

2018

SIAM J. Imaging Sci.

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We propose a mathematical method to analyze the numerous algorithms performing Image Matching by Affine Simulation (IMAS). To become affine invariant they apply a discrete set of affine transforms to the images, previous to the comparison of all images by a Scale Invariant Image Matching (SIIM), like SIFT . Obviously this multiplication of images to be compared increases the image matching complexity. Three questions arise: a) what is the best set of affine transforms to apply to each image to gain full practical affine invariance? b) what is the lowest attainable complexity for the resulting method? c) how to choose the underlying SIIM method? We provide an explicit answer and a mathematical proof of quasi-optimality of the solution to the first question. As an answer to b) we find that the near-optimal complexity ratio between full affine matching and scale invariant matching is more than halved, compared to the current IMAS methods. This means that the number of key points necessary for affine matching can be halved, and that the matching complexity is divided by four for exactly the same performance. This also means that an affine invariant set of descriptors can be associated with any image. The price to pay for full affine invariance is that the cardinality of this set is around 6.4 times larger than for a SIIM.

show abstract

Automatic Homographic Registration of a Pair of Images, with A Contrario Elimination of Outliers

Cited by 131 publications

References 6 publications

Virtual Line Descriptor and Semi-Local Graph Matching Method for Reliable Feature Correspondence

Virtual Line Descriptor and Semi-Local Graph Matching Method for Reliable Feature Correspondence

OpenMVG: Open Multiple View Geometry

Covering the Space of Tilts. Application to Affine Invariant Image Comparison

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