DocumentCode :
276172
Title :
A new class of classification and thresholding algorithms for image processing based on entropy-related measures
Author :
van der Lubbe, J.C.A. ; Borger, J.B.
Author_Institution :
Delft Univ. of Technol., Netherlands
fYear :
1992
fDate :
7-9 Apr 1992
Firstpage :
335
Lastpage :
340
Abstract :
A parametric mutual certainty measure is introduced, which is closely related to entropy. By relating mutual certainty to the structure of decision trees as well as to the Bayesian probability of error, a hierachical classification algorithm is developed, which can be used for the classification of digital imagery on the basis of their feature domain. By means of the parameter values the characteristics of the decision tree can be influenced. Furthermore, in the case of some stopping criterion the partitioning until then guarantees a minimization of the probability of error on the average. The algorithm can be used in itself or in combination with other algorithms, e.g. local refined boundaries in the case of application of `box´-classifiers. Furthermore, by taking the inverse of the decision criterion, an algorithm is obtained which is useful for gray level thresholding of digital imagery. Attention is paid to the observed duality of classification in the feature space and gray level thresholding of histograms
Keywords :
entropy; pattern recognition; picture processing; probability; stochastic processes; Bayesian probability of error; decision trees; digital imagery; entropy; feature domain; gray level thresholding; hierachical classification algorithm; histograms; image processing; minimization; parametric mutual certainty measure; stopping criterion; thresholding algorithms;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Image Processing and its Applications, 1992., International Conference on
Conference_Location :
Maastricht
Print_ISBN :
0-85296-543-5
Type :
conf
Filename :
146806
Link To Document :
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