Nonparametric optimal binarization

Binarization is reduced to function approximation in terms of quantization which is overlaid either with a recursion scheme or with discrete scale-space filtering. Approximations are solved to optimality not requiring any parametric input except that there be two segment classes of the image.

[1]  J. Andrew Bangham,et al.  Scale-Space From Nonlinear Filters , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Andrew P. Witkin,et al.  Scale-space filtering: A new approach to multi-scale description , 1984, ICASSP.

[3]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[4]  Thomas Kämpke,et al.  Discrete signal quantization , 1999, Pattern Recognit..

[5]  Farzin Mokhtarian,et al.  Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Allen Gersho,et al.  Vector quantization and signal compression , 1991, The Kluwer international series in engineering and computer science.

[7]  Rudolf Kober,et al.  Gray-Scale ALIAS , 1992, IEEE Trans. Knowl. Data Eng..

[8]  Anil K. Jain,et al.  Goal-Directed Evaluation of Binarization Methods , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Sargur N. Srihari,et al.  Document Image Binarization Based on Texture Features , 1997, IEEE Trans. Pattern Anal. Mach. Intell..