Neural Networks applied to 3D Object Depth Recovery
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| Format: | Artículo científico |
| Sprache: | en |
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Instituto Politécnico Nacional
2004
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| _version_ | 1876459199112675328 |
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| author | Francisco Javier Cuevas de la Rosa |
| author_facet | Francisco Javier Cuevas de la Rosa |
| contents | Neural Networks applied to 3D Object Depth Recovery Francisco Javier Cuevas de la Rosa Manuel Servin Guirado Computación softcomputing depth recovery Neural networks computer vision optical metrology In this work the application of neural networks (NNs) in tridimensional object depth recovery and structured light projection system calibration tasks is presented. In a first approach, a NN using radial basis functions (RBFNN) is proposed to carry out fringe projection system calibration. In this case the RBFNN is modeled to fit the phase information (obtained from fringe images) to the real physical measurements. In a second approach, a Multilayer Perceptron Neural Network (MPNN) is applied to phase and depth recovery from the fringe patterns. A scanning window is used as the MPNN input and the phase or depth gradient measurements is obtained at the MPNN output. Experiments considering real object depth measurement are presented. 2004 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61570407 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.4 Vol.7 |
| format | Artículo científico |
| id | redalyc_61570407 |
| institution | Redalyc |
| language | en |
| publishDate | 2004 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | Neural Networks applied to 3D Object Depth Recovery Francisco Javier Cuevas de la Rosa Computación softcomputing depth recovery Neural networks computer vision optical metrology Neural Networks applied to 3D Object Depth Recovery Francisco Javier Cuevas de la Rosa Manuel Servin Guirado Computación softcomputing depth recovery Neural networks computer vision optical metrology In this work the application of neural networks (NNs) in tridimensional object depth recovery and structured light projection system calibration tasks is presented. In a first approach, a NN using radial basis functions (RBFNN) is proposed to carry out fringe projection system calibration. In this case the RBFNN is modeled to fit the phase information (obtained from fringe images) to the real physical measurements. In a second approach, a Multilayer Perceptron Neural Network (MPNN) is applied to phase and depth recovery from the fringe patterns. A scanning window is used as the MPNN input and the phase or depth gradient measurements is obtained at the MPNN output. Experiments considering real object depth measurement are presented. 2004 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61570407 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.4 Vol.7 |
| title | Neural Networks applied to 3D Object Depth Recovery |
| topic | Computación softcomputing depth recovery Neural networks computer vision optical metrology |
| url | https://www.redalyc.org/articulo.oa?id=61570407 |