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@InProceedings{TorrećoFern:2005:SiShDe,
               author = "Torre{\~a}o, Jos{\'e} Ricardo de Almeida and Fernandes, 
                         Jo{\~a}o Luiz",
          affiliation = "Instituto de Computa{\c{c}}{\~a}o, Universidade Federal 
                         Fluminense",
                title = "Single-image shape from defocus",
            booktitle = "Proceedings...",
                 year = "2005",
               editor = "Rodrigues, Maria Andr{\'e}ia Formico and Frery, Alejandro 
                         C{\'e}sar",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 18. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "shape from defocus, shape from shading.",
             abstract = "The limited depth of field causes scene points at various 
                         distances from a camera to be imaged with different amounts of 
                         defocus. If images captured under different aperture settings are 
                         available, the defocus measure can be estimated and used for 3D 
                         scene reconstruction. Usually, defocusing is modeled by gaussian 
                         convolution over local image patches, but the estimation of a 
                         defocus measure based on that is hampered by the spurious 
                         high-frequencies introduced by windowing. Here we show that this 
                         can be ameliorated by the use of unnormalized gaussians, which 
                         allow defocus estimation from the zero-frequency Fourier component 
                         of the image patches, thus avoiding spurious high frequencies. As 
                         our main contribution, we also show that the modified shape from 
                         defocus approach can be extended to shape estimation from single 
                         shading inputs. This is done by simulating an aperture change, via 
                         gaussian convolution, in order to generate the second image 
                         required for defocus estimation. As proven here, the 
                         gaussian-blurred image carries an explicit depth-dependent blur 
                         component - which is missing from an ideal shading input -, and 
                         thus allows depth estimation as in the multi-image case.",
  conference-location = "Natal",
      conference-year = "9-12 Oct. 2005",
             language = "en",
           targetfile = "torreaoj_defocus.pdf",
        urlaccessdate = "2020, Dec. 05"
}


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