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		<identifier>8JMKD3MGPBW34M/3K2HFE8</identifier>
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		<citationkey>MesquitaCampNasc:2015:MeObSu</citationkey>
		<title>A Methodology for Obtaining  Super-Resolution Images and Depth Maps from RGB-D Data</title>
		<format>On-line</format>
		<year>2015</year>
		<secondarytype>Master's Work</secondarytype>
		<numberoffiles>1</numberoffiles>
		<size>2803 KiB</size>
		<author>Mesquita, Daniel Balbino de,</author>
		<author>Campos, Mario Fernando Montenegro,</author>
		<author>Nascimento, Erickson Rangel do,</author>
		<affiliation>Universidade Federal de Minas Gerais</affiliation>
		<affiliation>Universidade Federal de Minas Gerais</affiliation>
		<affiliation>Universidade Federal de Minas Gerais</affiliation>
		<editor>Segundo, Maurício Pamplona,</editor>
		<editor>Faria, Fabio Augusto,</editor>
		<e-mailaddress>balbson@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 28 (SIBGRAPI)</conferencename>
		<conferencelocation>Salvador</conferencelocation>
		<date>Aug. 26-29, 2015</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Master's or Doctoral Work</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>super-resolution, convex optimization, RGB-D data, 3D reconstruction, computer vision.</keywords>
		<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.</abstract>
		<language>en</language>
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		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi/2015/08.06.20.16</url>
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