Parametric classification of segments in ocular nystagmus

1991 ◽  
Vol 38 (2) ◽  
pp. 142-148 ◽  
Author(s):  
C.G. Rey ◽  
H.L. Galiana
Keyword(s):  
Parametric Classification ◽  
Ocular Nystagmus

Semi-parametric classification of noisy curves

2003 ◽  
Vol 36 (1) ◽  
pp. 35-44 ◽  
Author(s):  
R.H. Glendinning ◽  
A.J. Goode
Keyword(s):  
Parametric Classification

Non-Parametric Classification of Remotely Sensed Multispectral Image Data by Means of Matrix Representation of Multidimensional Histograms

1994 ◽  
Vol 6 (1) ◽  
pp. 42-50
Author(s):  
Minoru Inamura ◽  
Keyword(s):  
Parametric Method ◽  
Image Data ◽  
Remotely Sensed ◽  
Multispectral Image ◽  
Likelihood Method ◽  
Classification Rate ◽  
The Matrix ◽  
Parametric Classification ◽  
Non Parametric

The computer framing of land use maps using remotely sensed multispectral image data is identical with pattern classification for spectral reflectance of objects on earth's surface. In particular, the classification by the maximum likelihood method is the most popular method because it theoretically gives the highest correct classification rate on the condition that the statistical distribution of the image data be normal. However, the histogram of real image data is not a normal distribution. Actual histograms show the proper distributions to classes. This fact means that a histogram gives a spatial property of the class statistically. This paper described a newly developed non-parametric method by means of the matrix representations of multidimensional histograms and subimages.

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