Depth object recovery using a light line and a regression neural network

• Autorzy: Muňoz Rodriguez J. A. , Rodriguez-Vera R. , Asundi A.
• Języki publikacji: EN

Abstrakty

EN

A technique for measuring the objects shape is presented. In this technique, the object is scanned using a light line. From the scanning a set of images is captured by a CCD camera. By processing these images, the object surface is recovered. To determine the surface dimensions, a regression neural network is applied. This network is built using data from images of a light line projected onto the objects, with known dimensions. The data are extracted from the images by applying Gaussian approximation. By using the neural network in this technique, the surface measurement is determined without using the parameters of the set-up. It improves the accuracy of the techniques of light line projection for shape detection, because errors of parameters of the set-up are not introduced to the system. This technique is tested in an experimental way and its results are verified with a contact method.

Słowa kluczowe

EN

shape detection; light line projection; regression neural network; Gaussian approximation

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2005
• Tom: Vol. 35, nr 2
• Strony: 295--309
• Opis fizyczny: Bibliogr. 16 poz., il., wykr.

Twórcy

autor

Muňoz Rodriguez J. A.

• Centra de Investigaciones en Optica, A.C., Leon, Gto, 37150 Mexico

autor

Rodriguez-Vera R.

• Nayang Technological University, School of Mechanical and Production Engineering, Nayang Avenue, Singapore 639798

autor

Asundi A.

• Nayang Technological University, School of Mechanical and Production Engineering, Nayang Avenue, Singapore 639798

Bibliografia

• [1] Cheng X.X., Su X.Y., Guo L.R., Automated measurement method for 360profilometryof3-D diffuse objects, Applied Optics 30(10), 1991, pp. 1274-8.
• [2] Thomas L.P., Gratton R., Marino B.M., Simon J.M., Measurements of free-surface profiles in transient flow by a simple light-slicing method, Applied optics 33(13), 1994, pp. 2455-8.
• [3] Tai W.Ch., Chang M., Non contact profilometric measurement of large form parts, optical Engineering 35(9), 1996, pp. 2730-5.
• [4] Baba M., Konishi T., Kobayashi N., A novel fast rangefinder with non-mechanical operation, Journal of optics 29(3), 1998, pp. 241-9.
• [5] Marokkey S.R., Tay Ch.J., Shang H.M., Asundi A.K., Time Delay an integration imaging for inspection andprofilometry of moving objects, optical Engineering 36(9), 1997, pp. 2573-8.
• [6] Sajan M.R., Tay C.J., Shang H.M., Asundi A., Improved spatial phase detection for profilometry using a TDIimager, optics Communications 150(1-6), 1998, pp. 66-70.
• [7] Asundi a., Zhou W., Mapping algorithm for 360-deg profilometry with time delay integration ^ma^ging, optical Engineering 38(2), 1999, pp. 339-44.
• [8] Munoz-Rodriguez J.A., Rodriguez-Vera R., Servin M., Direct object shape detection base on skeleton extraction of a light line, optical Engineering 39(9), 2000, pp. 2463-71.
• [9] Herzog W.D., unlu M.S., Goldberg B.B., Rhodes G.H., Harder C., Beam divergence and waist measurement of laser diodes by near field scanning optical microscopy, Applied Physics Letters 70(6), 1997, pp. 688-90.
• [10] Dixon W.J., Massey Jr. F.J., Introduction to Statistical Analysis, McGraw-Hill, New York 1969.
• [11] Montgomery D.C., Runger G.C., Applied Statistics and Probability for Engineers, Wiley, New York 2001.
• [12] Picton p., Neural Networks, Polgrave, u.S.A. 2000.
• [13] Press W.H., Flannery B.P., Teukolsky S.A., Vetterling W.T., Numerical Recipes in C, Cambridge university Press, Cambridge 1993.
• [14] Rojas I., Pomares H., Gonzalez J., Bernier J.L., Ros E., Pelayo F.J., Prieto A., Analysis of the functional block involved in the design of radial basic functions networks, Neural Processing Letters 12(1), 2000, pp. 1-17.
• [15] Leung H., Dubash N., Xie N., Detection of small objects in clutter using a GA-RBF neural network, IEEE Transactions on Aerospace and Electronic Systems 38(1), 2002, pp. 98-118.
• [16] Masters T., Practical Neural Network Recipes in C++, Academic Press, Boston 1993.