Distance Measurement System using Images to Determine the Position of a Sphere using the XBOX Kinect Sensor

Omar Rodríguez Zalapa, Antonio Hernández Zavala, Jorge Adalberto Huerta Ruelas

Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada C.I.C.A.T.A., Unidad Querétaro, México (correos: omar.rodriguez.2013@ieee.org, anhernandezz@ipn.mx, jhuertar@ipn.mx)

Manuscrito recibido el 11 de marzo de 2014 ]]> Aceptado para la publicación el 30 de mayo del 2012.

Resumen

En este documento se presenta un método para medir la distancia del centroide de un objeto segmentado en una imagen de color con respecto a un punto de referencia fijo. El algoritmo se probó mediante una secuencia de imágenes de color, analizando más de 100 posiciones verticales diferentes de una esfera alojada en el interior de una columna cilíndrica transparente de acrílico con diámetro y longitud constante. El algoritmo propuesto integra técnicas de corrección por balance de blancos y de calibración de la cámara con sus parámetros intrínsecos, además, se prueba un nuevo método de segmentación en color utilizado para calcular distancias del mundo real a partir de imágenes en color RGB. Los resultados obtenidos reflejan una alta confiabilidad ya que el 100% de las mediciones realizadas tuvo un error menor a 1.64% con un nivel de precisión más alto que el instrumento utilizado de referencia, en un rango de distancia de 0 a 1340 mm.

Palabras clave: Fotogrametría, medición de distancias en imágenes, sensor visual de distancias, metrología visual de simple vista, segmentación, seguimiento de objetos.

Abstract

This paper presents a method to measure the distance from the centroid of a segmented object in a color image with respect to a fixed reference point into the image. The algorithm was tested using a color image sequence by analyzing over 100 different vertical positions of a ball housed inside a transparent acrylic cylindrical column of constant diameter and length. The proposed algorithm integrates technics of correction by white balance and calibration of the camera with its intrinsic parameters; in addition, a new color segmentation method is tested to calculate real-world distances into color images RGB. The results show high reliability because 100% of measurements had a relative error in percentage less than 1.64%, with a higher level of precision than the reference instrument used in a distance range from 0 to 1340 mm.

Key words: Photogrammetry, measuring distances into images, visual sensor of distances, simple view metrology, segmentation, tracking objects.

Referencias

[1] H. M. Dee, S. A. Velastin, "How close are we to solving the problem of automated visual surveillance?," Machine Vision and Applications, vol. 19, no. 5-6, 2008, pp. 329-343.         [ Links ]

[2] P. Vera, D. Zenteno, J. Salas, "Counting Pedestrians in Bidirectional Scenarios Using Zenithal Depth Images," Proceedings of 5th Mexican Conference, MCPR, Querétaro, Mexico, junio 26-29, 2013, pp. 84-93.         [ Links ]

[3] R. T. Chin, C. A. Harlow, "Automated visual inspection: A survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, 1982, pp. 557-573.         [ Links ]

[4] E. Royer et al., "Monocular vision for mobile robot localization and autonomous navigation," International Journal of Computer Vision, vol. 74, no. 3, 2007, pp. 237-260.         [ Links ]

[5] A. Criminisi, I. Reid, A. Zisserman, "A plane measuring device," Image and Vision Computing, vol. 17, no. 8, 1999, pp. 625-634.         [ Links ]

[6] Y. Wan et al., "A Study in 3D-Reconstruction Using Kinect Sensor," 8th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), IEEE, 2012. pp. 1-7.         [ Links ]

[7] T. Bucher, "Measurement of distance and height in images based on easy attainable calibration parameters," Proceedings of the IEEE in Intelligent Vehicles Symposium, IV, 2000. pp. 314-319.         [ Links ]

[8] J. L. Lázaro et al., "Sensor for distance estimation using FFT of images." Sensors, 2009, vol. 9, no. 12, p. 10434-10446.         [ Links ]

[9] A. Criminisi, Accurate visual metrology from single and multiple uncalibrated images. Springer, 2001.         [ Links ]

[10] P. Wright, "Single View Metrology: Measuring the Dimensions of Objects From a Single Image." University of York, Computer Science Department, 2006.         [ Links ]

[11] R. J. Moffat, "Describing the uncertainties in experimental results," Experimental thermal and fluid science, vol. 1, no. 1, 1988, pp. 3-17.         [ Links ]

[12] MSDN, Microsoft, http://msdn.microsoft.com/en-us/library/jj131033, consultado el 30/08/2013.         [ Links ]

[13] Kinect, http://wiki.ros.org/kinect, consultado el 23/01/2012.         [ Links ]

[14] Wikipedia, http://commons.wikimedia.org/wiki/File:Xbox-360-Kinect-Standalone.png, recuperado el 15/06/2013.         [ Links ]

[15] A. Criminisi, I. Reid, A. Zisserman, "A plane measuring device," Image and Vision Computing, vol. 17, no. 8, 1999, pp. 625-634.         [ Links ]

[16] J.-Y. Bouguet. "Camera calibration toolbox for Matlab", http://www.vision.caltech.edu/bouguetj/calib_doc/2004.         [ Links ]

[17] R. Ramanath et al., "Color image processing pipeline," Signal Processing Magazine, IEEE, vol. 22, no. 1, 2005, pp. 34-43.         [ Links ]

[18] S. Zeki, Semir. A Vision of the Brain. Blackwell scientific publications, 1993.         [ Links ]

[19] M. Ebner. Color constancy. Wiley, 2007.         [ Links ]

[20] Bosch DLE40. http://www.boschmexico.com.mx/telemetro-laser-dle-40-professional, recuperado el 01/04/2013.         [ Links ]

[21] R. Doskocil et al., "Measurement of distance by single visual camera at robot sensor systems," MECHATRONIKA, 15th International Symposium, IEEE, 2012.         [ Links ]