PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Fast and Efficient Colour Inspection Using Sets of Ellipsoidal Regions

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper addresses inspection of d-dimensional data using ellipsoidal decision reg: for problems in automated visual inspection. For the special case of d = 3, the proposed met is researched in detail with respect to efficient real-time operation and compact storage of ellipi parameters. In order to reduce storage requirements, a method based on clustering the parame describing the shapes of ellipsoids is proposed. Results are presented for colour images used in quality analysis step in banknote printing. Additionally, estimations of computational effort and storage requirements are provided.
Rocznik
Strony
345--361
Opis fizyczny
Bibliogr. 25 poz., wykr.
Twórcy
autor
autor
autor
autor
  • Austrian Research Centers GmbH - ARC, Smart Systems Division, Business Unit High Performance Image Processing 2444 Seibersdorf, Austria
Bibliografia
  • [1] Kullback, S. and R. A. Leibler (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79-86.
  • [2] Chin, R.T. (1982). Automated visual inspection: a survey. IEEE Trans. Pat. Anal. Mach. Intell. 4(6), 557-573.
  • [3] Olafsson, S. and Y.S. Abu-Mostafa (1988). The capacity of multilevel threshold functions. IEEE Trans. Pat. Anal. Mach. Intell. 10(2), 277-281.
  • [4] Rousseeuw, P.J., and B.C. van Zomeren (1990). Unmasking multivariate outliers and leverage points. J. American Stat. Assoc. 85, 633-639.
  • [5] Newman, T.S. and A.K. Jain (1995). A survey of automated visual inspection. Computer Vision and Image Understanding 61(2), 231-262.
  • [6] Thomas, A.D.H., M.G. Rodd, J.H. Holt and C.J. Neill (1995). Real-time industrial visual inspection: a review. Real-Time Imaging 1(2), 139-158.
  • [7] Barber, C.B., D.P. Dobkin and H. Huhdanpaa (1996). The quickhull algorithm for convex hulls, ACM Trans, on Mathematical Software, 22(4), 469-483.
  • [8] Devroye, L., L. Gyrfi and G. Lugosi (1996). A Probabilistic Theory of Pattern Recognition Springer.
  • [9] Khachiyan, L.G. (1996). Rounding of polytopes in the real number model of computation. Math. Over. Res. 21, 307-320.
  • [10] Abe, S., R. Thawonmas and M. Kayam (1999). A fuzzy classifier with ellipsoidal regions for diagnosis problems. IEEE Trans. Syst. Man Cybernet. - Part C: Applications and Reviews 29(1), 140-149.
  • [11] Demant, C., B. Streicher-Abel and P. Waszkewitz (1999). Industrial Image Processing: Visual Quality Control in Manufacturing. Springer, Heidelberg.
  • [12] Kosinski, A. (1999). A procedure for the detection of multivariate outliers. Computational Statistics & Data Analysis, 2, 145-161.
  • [13] Rousseeuw, P. J. and K. van Driessen. (1999). A fast algorithm for the minimum covariance deter minant estimator. Technometrics 41(3), 212-223.
  • [14] Coldani, G., Cotrino, L., Danese, G., Leporati, F. and Maneri, M. (2000). Notacheck: a parallel DSP-based architecture for real time high-resolution inspection of banknotes. Proc. of IEEE Intl. Workshop on Computer Architectures for Machine Perception, 163-169.
  • [15] Calafiore, G. (2002). Approximation of n-dimensional data using sperical and ellipsoidal primitives. IEEE Trans. Syst. Man Cybernet. - Part A: Systems and Humans 32(2), 269-278.
  • [16] Ramanath, R., W. Snyder, G. Bilbro and W. Sander (2002). Demosaicking methods for bayer colour arrays. Journal of Electronic Imaging 11(3), 306-315.
  • [17] Schlesinger, M.I. and V. Hlavac (2002). Ten Lectures on Statistical and Structural Pattern Recognition (Computational Imaging & Vision). Kluwer Academic Publishers.
  • [18] Derganc, J., Likar, B., Bernard, R., Tomazevic and F. Perus (2003). Real-time automated visual inspection of color tablets in pharmaceutical blisters. Real-Time Imaging, 9(2), 113-124.
  • [19] Myrvoll, T.A. and F.K. Soong (2003). Optimal clustering of multivariate normal distributions using divergence and its application to HMM adaptation. Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing Vol. I, 552-555.
  • [20] Lee, K.K. and W.C. Yoon (2004). Adaptive classification with ellipsoidal regions for multidimensional pattern classification problems. Pattern Recognition Letters 26(9), 1232-1243.
  • [21] Bergman, L., A. Verikas, and M. Bacauskiene (2005) Unsupervised colour image segmentation applied to printing quality assessment. Image and Vision Computing 23(4), 417-425.
  • [22] Kumar, P. and and E.A. Yildirim (2005). Minimum volume enclosing ellipsoids and core sets. J. Optimization Theory and Applications 126(1), 1-21.
  • [23] Ruz, G.A., Estevez, P.A. and C.A. Perez (2005). A neurofuzzy color image segmentation method for wood surface defect detection. Forest Products Journal 55(4), 52-58.
  • [24] Huber-Mork, R., H. Ramoser, H. Penz, K. Mayer, D. Heiss and A. Vrabl (2007). Region based matching for print process identification. Pattern Recognition Letters, 28(15), 2037-2045.
  • [25] Hardin, W. (2008). Money in the bank, Vision Systems Design, 13(1).
  • [26] [Cheng et al. ()] Cheng, H.D., X.H. Jiang, Y. Sun and J. Wang (2001). Colour image segmentation: advances and prospects. Pattern Recognition, 34(12), 2259-2281.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWA9-0032-0006
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.