Parametric model

Results: 583



#Item
1Dense, Accurate Optical Flow Estimation with Piecewise Parametric Model (Supplementary Material) Jiaolong Yang1,2 and Hongdong Li2,3 1  2

Dense, Accurate Optical Flow Estimation with Piecewise Parametric Model (Supplementary Material) Jiaolong Yang1,2 and Hongdong Li2,3 1 2

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Source URL: jlyang.org

Language: English - Date: 2018-07-27 04:11:09
    216  CPAdd Magnetization Transfer Ratio to Image Model Component Semantics for Parametric Maps 1

    16 CPAdd Magnetization Transfer Ratio to Image Model Component Semantics for Parametric Maps 1

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    Source URL: dicom.nema.org

    - Date: 2015-11-10 11:33:00
      326  CPAdd MR Diffusion Model Quantities and Parameters for Parametric Maps and ROI Measurements​ 1

      26 CPAdd MR Diffusion Model Quantities and Parameters for Parametric Maps and ROI Measurements​ 1

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      Source URL: dicom.nema.org

      - Date: 2017-05-03 18:36:54
        4EDA221 – Introduction to Computer Graphics, 2013  Assignment 2 – Tessellation and Interpolation In this assignment you will tessellate your own model from a parametric equation. This task involves setting up appropri

        EDA221 – Introduction to Computer Graphics, 2013 Assignment 2 – Tessellation and Interpolation In this assignment you will tessellate your own model from a parametric equation. This task involves setting up appropri

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        Source URL: fileadmin.cs.lth.se

        - Date: 2013-09-19 06:26:31
          5EDA221 – Introduction to Computer Graphics, 2016  Assignment 2 – Tessellation and Interpolation In this assignment you will tessellate your own model from a parametric equation. This task involves setting up appropri

          EDA221 – Introduction to Computer Graphics, 2016 Assignment 2 – Tessellation and Interpolation In this assignment you will tessellate your own model from a parametric equation. This task involves setting up appropri

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          Source URL: fileadmin.cs.lth.se

          - Date: 2016-09-15 06:51:02
            6A parametric family of model distribution functions for hybrid MHD-GK numerical simulations of energetic particle induced collective effects in FAST scenarios with ICRH and NNBI C. Di Troia, S. Briguglio, G. Fogaccia, G.

            A parametric family of model distribution functions for hybrid MHD-GK numerical simulations of energetic particle induced collective effects in FAST scenarios with ICRH and NNBI C. Di Troia, S. Briguglio, G. Fogaccia, G.

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            Source URL: www.afs.enea.it

            - Date: 2013-10-07 10:03:59
              719 20 CPAdd Magnetic Susceptibility to Image Model Component Semantics for Quantitative Susceptibilty Parametric Maps

              19 20 CPAdd Magnetic Susceptibility to Image Model Component Semantics for Quantitative Susceptibilty Parametric Maps

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              Source URL: dicom.nema.org

              - Date: 2016-05-25 11:10:00
                8Chapter 5 Local Regress ion Trees In this chapter we explore the hypothesis of improving the accuracy of regression trees by using smoother models at the tree leaves. Our proposal consists of using local regression model

                Chapter 5 Local Regress ion Trees In this chapter we explore the hypothesis of improving the accuracy of regression trees by using smoother models at the tree leaves. Our proposal consists of using local regression model

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                Source URL: www.dcc.fc.up.pt

                Language: English - Date: 2012-12-13 10:18:43
                9Illumination Planning for 0bject Recognit ion in Structured Environments * Shree K . Nayar Hiroshi Murase NTT Basic Research Labs

                Illumination Planning for 0bject Recognit ion in Structured Environments * Shree K . Nayar Hiroshi Murase NTT Basic Research Labs

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                Source URL: www.cs.columbia.edu

                Language: English - Date: 2005-07-11 01:36:46
                10Technical Note: Analyzing land cover change with logistic regression in R D G Rossiter∗ Analía Loza† Version 2.4; May 14, 2016

                Technical Note: Analyzing land cover change with logistic regression in R D G Rossiter∗ Analía Loza† Version 2.4; May 14, 2016

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                Source URL: www.css.cornell.edu

                Language: English - Date: 2016-05-14 10:53:21