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                    "codemeta:author": [
                        {
                            "codemeta:affiliation": "LIGM (UMR 8049), Ecole des Ponts ParisTech, UPE, Marne-la-Vall\u00e9e, France",
                            "codemeta:name": "Laura F. Juli\u00e0"
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                        {
                            "codemeta:affiliation": "LIGM (UMR 8049), Ecole des Ponts ParisTech, UPE, Marne-la-Vall\u00e9e, France",
                            "codemeta:name": "Pascal Monasse"
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                        {
                            "codemeta:affiliation": "IGN/LOEMI, Universit\u00e9 Paris-Est, France",
                            "codemeta:name": "Marc Pierrot-Deseilligny"
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                    "codemeta:dateCreated": "2019-07-01",
                    "codemeta:datePublished": "2019-09-05",
                    "codemeta:description": "The Orthographic Projection Model for Pose Calibration of Long Focal Images",
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                        "codemeta:name": "Image Processing On Line (IPOL)",
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                    "codemeta:keywords": [
                        "pose estimation",
                        "calibration",
                        "camera model"
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                        "codemeta:abstract": "Most stereovision and Structure from Motion (SfM) methods rely on the pinhole camera model based on perspective projection. From this hypothesis the fundamental matrix and the epipolar constraints are derived, which are the milestones of pose estimation. In this article we present a method based on the matrix factorization due to Tomasi and Kanade that relies on a simpler camera model, resulting in orthographic projection. This method can be used for the pose estimation of perspective cameras in configurations where other methods fail, in particular, when using cameras with long focal length lenses. We show this projection is an approximation of the pinhole camera model when the camera is far away from the scene. The performance of our implementation of this pose estimation method is compared to that given by the perspective-based methods for several configurations using both synthetic and real data. We show through some examples and experiments that the accuracy achieved and the robustness of this method make it worth considering in any SfM procedure.",
                        "codemeta:identifier": "https://doi.org/10.5201/ipol.2019.248",
                        "codemeta:name": "The Orthographic Projection Model for Pose Calibration of Long Focal Images",
                        "codemeta:url": "http://www.ipol.im/pub/art/2019/248/article.pdf"
                    },
                    "codemeta:relatedLink": "http://ipolcore.ipol.im/demo/clientApp/demo.html?id=248",
                    "codemeta:releaseNotes": "This code implements the algorithm(s) published in the IPOL paper \"The Orthographic Projection Model for Pose Calibration of Long Focal Images\"",
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