PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Detection of human eye components on the basis of multispectral imaging

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper the methods for selecting of the most important parts of the human eyes are described. On the basis of the real 21 channel multispectral images the model of finding the lens and the spot are defined. These methods are based on the most popular algorithms of image processing. The approach to veins detection is still undefined but in the article the most important channels are pointed out and the channel difference between eyelash and the veins is also mentioned.
Rocznik
Tom
Strony
41--47
Opis fizyczny
Bibliogr. 12 poz., rys.
Twórcy
autor
  • Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
Bibliografia
  • [1] CAMPS-VALLS G., BRUZZONE L., Kernel-based methods for hyperspectral image classification, IEEE Transactions on Geoscience and Remote Sensing, 2005, Vol. 43, No. 6, pp. 1351–1362.
  • [2] DERRODE S., MERCIER G., PIECZYNSKI W., Unsupervised multicomponent image segmentation combining a vectorial HMC model and ICA, Int. Conf. on Image Processing, 2003, pp. 407–410.
  • [3] GAT N., Imaging spectroscopy using tunable filters: a review, Proceedings of SPIE - International Society for Optical Engineering, 2000, Vol. 4056, pp. 50–64.
  • [4] LAU D., VILLIS C., FURMAN S., LIVETT M., Multispectral and hyperspectral image analysis of elemental and micro-raman maps of cross-sections from a 16th century painting, Analytica Chimica Acta, 2008, Vol. 610, No. 1, pp. 15–24.
  • [5] MASOOD K., RAJPOOT N.M., Spatial analysis for colon biopsy classification from hyperspectral, Annals of the BMVA, 2008, Vol. 4, pp. 1–15.
  • [6] MERCIER G., DERRODE S., LENNON M., Hyperspectral image segmentation with markov chain model, In IEEE International Geoscience and Remote Sensing Symposium, 2003, pp. 3766 – 3768.
  • [7] MICHALAK M., ŚWITOŃSKI A., Spectrum evaluation on multispectral images by machine learning techniques, Lecture Notes in Computer Science, 2010, Vol. 6375, pp. 26–133.
  • [8] MICHALAK M., ŚWITOŃSKI A., Kernel postprocessing of multispectral images, Advances in Intelligent and Soft Computing, 2011, Vol. 95, pp. 395–401.
  • [9] MICHALAK M., ŚWITOŃSKI A., STAWARZ M., Selection of the most important components from multispectral images for detection of tumor tissue, Journal of Medical Informatics and Technologies, 2011, Vol. 17, pp. 303-307.
  • [10] RAJPOOT K., RAJPOOT N., SVM optimization for hyperspectral colon tissue cell classification, Lecture Notes in Computer Science, 2004, Vol. 3217, pp. 829–837.
  • [11] ŚWITOŃSKI A., BŁACHOWICZ T., ZIELIŃSKI M., MISIUK-HOJTO M., WOJCIECHOWSKI K., Ophthalmic diagnosis based on multispectral imaging, Electrical Review, 2011, Vol. 12B, pp. 165–168.
  • [12] ŚWITOŃSKI A., MICHALAK M., JOSIŃSKI H., WOJCIECHOWSKI K., Detection of tumor tissue based on the multispectral imaging, Lecture Notes in Computer Science, 2010, Vol. 6375, pp. 325-333.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0026-0004
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.