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Computación y Sistemas

On-line version ISSN 2007-9737Print version ISSN 1405-5546

Comp. y Sist. vol.7 n.4 Ciudad de México Apr./Jun. 2004


Resumen de tesis doctoral


Neural Networks applied to 3D Object Depth Recovery


Aplicación de redes neuronales en la reconstrucción tridimensional de objetos


Graduated: Francisco Javier Cuevas de la Rosa
Centro de Investigaciones en Óptica, A.C.
Loma del Bosque 115, León, Guanajuato, México
CP. 37150


Advisor: Manuel Servin Guirado
Centro de Investigaciones en Óptica, A.C.
Loma del Bosque 115, León, Guanajuato, México
CP. 37150



Graduated on August 21, 2000



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.

Keywords: Neural networks, structured light projection systems, softcomputing, computer vision, optical metrology, fringe demodulation, depth recovery, phase measurement.



En este trabajo se presenta la aplicación de redes neuronales (RNs) en la reconstrucción tridimensional de objetos y su utilización en tareas de calibración en sistemas de proyección de luz estructurada. En una primer propuesta, se establece una red neuronal que utiliza funciones de base radial (RNFBR) útil para calibrar un sistema de proyección de franjas. En este caso la RNFBR es modelada para ajustar la información de fase, obtenida de los imágenes de franjas a mediciones físicas reales. Se propone una segunda técnica que utiliza una red neuronal multicapas de perceptrones (RNMP) para la recuperación de fase y profundidad a partir de los patrones de franjas. En esta técnica se utiliza una ventana de análisis conteniendo subimágenes de los patrones de franjas. Esta subimagen es utilizada como entrada de la RNMP, obteniendo como salida las mediciones de los gradientes de fase o profundidad. Se presentan experimentos que aplican las técnicas propuestas para medir un objeto real.

Palabras Clave: Redes neuronales, sistemas de proyección de luz estructurada, computación suave, visión por computadora, metrología óptica, demodulación de franjas, recuperación de profundidad, medición de fase.





The author would like to thank Dr. Jose Luis Marroquin, Dr. Fernando Mendoza, Dr. Ramón Rodriguez Vera, Dr. Orestes Stavroudis, and Raymundo Mendoza for the invaluable technical and scientific support in the development of this work. We also acknowledge the support of the Consejo Nacional de Ciencia y Tecnología de México, Consejo de Ciencia y Tecnología del Estado de Guanajuato, and Centro de Investigaciones en Óptica, A.C.



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