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Tytuł artykułu

Material Classification Method for Printed Circuit Boards Using a Spectral Imaging System

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper proposes a method for classifying object materials on a raw circuit board into element materials by means of surface-spectral reflectance. First, we develop a spectral imaging system for observing the minute details of the board and capturing their spectral data. Second, the surface-spectral reflectance functions of the board are estimated by a direct method using narrow band sensor outputs. We investigate the reflection properties of various objects on the board under different illumination directions. Third, we find key features of the body spectral reflectances for different materials, and present a rule for classifying the objects into six element materials. Finally, experiments are executed using a real circuit board. The observed spectral reflectance image is segmented into the element material areas. The performance and robustness of the proposed method are examined in detail in comparison with other methods.
Rocznik
Strony
233--250
Opis fizyczny
Bibliogr. 18 poz., il., wykr.
Twórcy
autor
  • Department of Information Science Graduate School of Advanced Integration Science Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan, shoji@faculty.chiba-u.jp
Bibliografia
  • [1] Thibadeau P. H.: Printed Circuit Board Inspection. Tech. Report CMU-RI-TR-81-08, Robotics Institute, CMU, 1981.
  • [2] Maloney L. T., Wandell B. A.: Color constancy: a method for recovering surface spectral reflectance. J. Opt. Soc. Am. A, 3, 29-33, 1986.
  • [3] Capson D. W., End S. K.: A tiered-color illumination approach for machine inspection of solder joints. IEEE Trans. on Pattern Analysis and Machine Intelligence, 10(3), 387-393, 1988.
  • [4] Healey G.: Using color for geometry-insensitive segmentation. J. of Optical Society of America A, 6(6), 920-937, 1989.
  • [5] Tominaga S.: Surface identification using the dichromatic reflection model. IEEE Trans. on Pattern Analysis and Machine Intelligence, 13, 658-70, 1991.
  • [6] Wandell B. A.: Foundations of Vision, Sinauer Associates. Sunderland, MA. 1995.
  • [7] Miller P. J., Hoyt C. C.: Multispectral imaging with a liquid crystal tunable filter. Proc. of SPIE, 2345, 354-365, 1995.
  • [8] Tominaga S.: Multichannel vision system for estimating surface and illumination functions. J. Opt. Soc. Am. A, 13, 2163-2173, 1996.
  • [9] Slawson R. W., Ninkov R. W., Iliott H. P.: Hyperspectral imaging: Wide-area spectrophotometry using a liquid-crystal tunable filter. The Publications of the Astronomical Society of the Pacific, 111(759), 621-626, 1999.
  • [10] Tominaga S.: Spectral imaging by a multi-channel camera. J. of Electronic Imaging, 8(4), 332-341, 1999.
  • [11] Tominaga S., Okajima R.: Object recognition by multi-spectral imaging with a liquid crystal filter. Proc. Int. Conf. on Pattern Recognition, 600-603, 2000.
  • [12] Gat N.: Imaging spectroscopy using tunable filters: A review. Proc. SPIE, 4056, 50-64, 2000.
  • [13] Duda R. O., Hart P. E., Stork D. G.: Pattern Classification. John Wiley & Sons, New York. 2001.
  • [14] Hardeberg J. Y., Schmitt F., Brettel H.: Multispectral color image capture using a liquid crystal tunable filter. Optical Engineering, 40(10), 2532-2548, 2002.
  • [15] Tominaga S., Okamoto S.: Reflectance-based material classification for printed circuit boards. Proc. 12th Int. Conf. on Image Analysis and Processing, 238-243, 2003.
  • [16] Ibrahim Z., Al-Attas S. A. R.: Wavelet-based printed circuit board inspection system. J. of Signal Processing, 1(2), 73-79, 2005.
  • [17] Rajwa B.: AOTF-based system for image cytometry. Proc. SPIE, 5694, 1-8, 2005.
  • [18] Abdulhalim I., Moses R., Sharon R.: Biomedical optical applications of liquid crystal devices. IS-COM07, 715-722, 2007.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWAD-0015-0014
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