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                "url": "https://doi.org/10.5201/ipol.2013.21"
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                    "codemeta:author": [
                        {
                            "codemeta:affiliation": "CTIM, University of Las Palmas de Gran Canaria, Spain",
                            "codemeta:name": "Javier S\u00e1nchez P\u00e9rez"
                        },
                        {
                            "codemeta:affiliation": "CTIM, University of Las Palmas de Gran Canaria, Spain",
                            "codemeta:name": "Nelson Monz\u00f3n L\u00f3pez"
                        },
                        {
                            "codemeta:affiliation": "CTIM, University of Las Palmas de Gran Canaria, Spain",
                            "codemeta:name": "Agust\u00edn Salgado de la Nuez"
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                    "codemeta:dateCreated": "2012-06-20",
                    "codemeta:datePublished": "2013-11-06",
                    "codemeta:description": "Robust Optical Flow Estimation",
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                        "codemeta:identifier": "ISSN: 2105-1232 DOI: 10.5201/ipol",
                        "codemeta:name": "Image Processing On Line (IPOL)",
                        "codemeta:type": "Journal"
                    },
                    "codemeta:keywords": [
                        "optical flow",
                        "motion estimation",
                        "variational techniques",
                        "PDE"
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                    "codemeta:license": {
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                        "codemeta:abstract": "In this work, we describe an implementation of the variational method proposed by Brox et\nal. in 2004, which yields accurate optical flows with low running times. It has several benefits\nwith respect to the method of Horn and Schunck: it is more robust to the presence of outliers,\nproduces piecewise-smooth flow fields and can cope with constant brightness changes. This\nmethod relies on the brightness and gradient constancy assumptions, using the information of\nthe image intensities and the image gradients to find correspondences. It also generalizes the\nuse of continuous L1 functionals, which help mitigate the effect of outliers and create a Total\nVariation (TV) regularization. Additionally, it introduces a simple temporal regularization\nscheme that enforces a continuous temporal coherence of the flow fields.",
                        "codemeta:identifier": "https://doi.org/10.5201/ipol.2013.21",
                        "codemeta:name": "Robust Optical Flow Estimation",
                        "codemeta:url": "http://www.ipol.im/pub/art/2013/21/article.pdf"
                    },
                    "codemeta:relatedLink": "http://demo.ipol.im/demo/21/",
                    "codemeta:releaseNotes": "This code implements the algorithm(s) published in the IPOL paper \"Robust Optical Flow Estimation\"",
                    "codemeta:url": "http://www.ipol.im/pub/art/2013/21/",
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