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@InProceedings{MesquitaNascCamp:2015:SiEsSu,
               author = "Mesquita, Daniel Balbino de and Nascimento, Erickson Rangel do and 
                         Campos, Mario Fernando Montenegro",
          affiliation = "{Universidade Federal de Minas Gerais} and {Universidade Federal 
                         de Minas Gerais} and {Universidade Federal de Minas Gerais}",
                title = "Simultaneously Estimation of Super-Resolution Images and Depth 
                         Maps from Low Resolution Sensors",
            booktitle = "Proceedings...",
                 year = "2015",
               editor = "Papa, Jo{\~a}o Paulo and Sander, Pedro Vieira and Marroquim, 
                         Ricardo Guerra and Farrell, Ryan",
         organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Super-resolution, convex optimization, RGB-D data, 3D 
                         reconstruction, computer vision.",
             abstract = "The emergence of low cost sensors capable of providing texture and 
                         depth information of a scene is enabling the deployment of several 
                         applications such as gesture and object recognition and 
                         three-dimensional reconstruction of environments. However, 
                         commercially available sensors output low resolution data, which 
                         may not be suitable when more detailed information is necessary. 
                         With the purpose of increasing data resolution, at the same time 
                         reducing noise and filling the holes in the depth maps, in this 
                         work we propose a method that combines depth fusion and image 
                         reconstruction in a super-resolution framework. By joining 
                         low-resolution intensity images and depth maps in an optimization 
                         process, our methodology creates new images and depth maps of 
                         higher resolution and, at the same time, minimizes issues related 
                         with the absence of information (holes) in the depth map. Our 
                         experiments show that the proposed approach has increased the 
                         resolution of the images and depth maps without significant 
                         spawning of artifacts. Considering three different evaluation 
                         metrics, our methodology outperformed other three techniques 
                         commonly used to increase the resolution of combined images and 
                         depth maps acquired with low resolution, commercially available 
                         sensors.",
  conference-location = "Salvador",
      conference-year = "Aug. 26-29, 2015",
             language = "en",
           targetfile = "PID3771777.pdf",
        urlaccessdate = "2021, Dec. 03"
}


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