Image Processing Reference
In-Depth Information
53.
Tou, J.T. and Gonzalez, R.C. (1974). Pattern Recognition Principles. Number 7
in Applied Mathematics and Computation, Addison-Wesley, Reading, MA.
54.
Ruspini, E.H. (1969). A new approach to clustering. Inf. Control . 15: 22-32.
Ruspini, E.H. (1970). Numerical methods for fuzzy clustering. Inf. Sci. 2:
319-350.
55.
Zadeh, L.A. (1965). Fuzzy sets. Inf. Control . 8: 338-353.
56.
Bezdek, J.C. (1981). Pattern Recognition with Fuzzy Objective Function Algo-
rithms . New York. Plenum Press.
57.
Bezdek, J.C., Ehrlich, R., and Full, W. (1984). FCM: the fuzzy c-means clustering
algorithm. Comput. Geosci. 10: 191-203.
58.
Kohonen, T. (1995). Self-Organizing Maps . New York: Springer-Verlag.
59.
Hotelling, H. (1933). Analysis of a complex of statistical variables into principal
components. J. Educ. Psychol. , 24: 417-441.
60.
Pearson, K. (1901). On lines and planes of closest fit to systems of points in space.
The London, Edinburgh and Dublin Philosophical Magazine and Journal of Sci-
ence , 2: 559-572.
61.
Friston, K.J., Frith, C.D., and Frackowiak, R.S.J. (1993). Time-dependent
changes in effective connectivity measured with PET. Human Brain Mapping
1: 69-80.
62.
Friston, K.J., Frith, C.D., Liddle, P.F., and Frackowiak, S.J. (1993). Functional
connectivity: the principal-component analysis of large (PET) data sets. J. Cereb.
Blood Flow Metab . 13: 5-14.
63.
Golub, G.H. and Van Loan, C.F. (1991). Matrix Computations . (2nd ed.). The
Johns Hopkins University Press: Baltimore and London, pp. 241-248.
64.
Friston, K.J., Williams, S., Howard, R., Frackowiak, S.J., Turner, R. (1996).
Movement-related effects in fMRI time-series. Magn. Reson. Imaging . 35:
346-355.
65.
Akaike, H. (1969). Fitting autoregressive models for regression. Annula Institute
Statist. Math. 21: 243-247.
66.
Rissanen, J. (1978). Modeling by shortest data description. Automatica. 14:
465-471.
67.
Minka, T. P. (2000). Automatic Choice of Dimensionality for PCA. Technical
Report 54. MIT Media Laboratory. Perceptual Computing Section, Cambridge.
68.
Jackson, J. E. (1991). A User's Guide to Principal Components . New York: Wiley.
69.
Comon, P. (1994). Independent component analysis, a new concept? Signal
Process . 36: 287-314.
70.
Hyvärinen, A., Karhunen, J., and Oja, E. (2001). Independent Component Anal-
ysis . New York: Wiley.
71.
Stone, J.V. (2002). Independent component analysis: an introduction. Trend. Cogn.
Scis . 6(2): 59-64.
72.
Cardoso, J.-F. (1989). Blind identification of independent signals. in Proc. Work-
shop on Higher Order Spectral Analysis . Vail, CO.
73.
Jutten, C. and Hérault, J. (1991). Blind separation of sources, part I: an adaptive
algorithm based on neuromimetic architecture. Signal Process . 24: 1-10.
74.
Herault, J. and Jutten, C. (1986). Space or time adaptive signal processing by
neural network models, in Denker, J.S. (Ed.), Neural Networks for Computing:
AIP Conference Proceedings 11, American Institute for Physics, New York.
75.
Hansen, L.K., Nielsen, F.Å., Strother, S.C., and Lange, N.L. (2001). Consensus
inference in neuroimaging. Neuroimage . 13: 1212-1218.
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