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Practical approaches to statistical pattern recognition

Jóźwik A.

Biocybernetics and Biomedical Engineering

2002

Vol. 22, no. 2-3

207-221

The paper describes new approaches to statistical pattern recognition. All presented methods are based on a distance function. The properties of these methods and their usefulness are illustrated on real problems. Some tasks with small and very large training sets are described to shown an effectiveness of the proposed approaches. There is no one universal method that would be satisfactory for all object classification problems. That's why several methods have been demonstrated.

Morphological detection and feature-based classification of cracked regions in ferrites

Nieniewski M., Chmielewski L., Jóźwik A., Skłodowski M.

Machine Graphics and Vision

1999

Vol. 8, No. 4

699-712

Automatic quality inspection of ferrite products is difficult as thier surfaces are dark and in many cases coverred with traces of grinding. A two-stage vision system for detection and measurement of crack regions was devised. In the first stage the regions with strong evidence for cracks are found using a morphological detector of irregular grightness changes with subsequent morphological reconstruction. In the second stage the feature-based k-Nearest Neighbors classifier analyzes the pixel indicated in the first stage. The classifier is optimized by using procedures of reclassification and replacement carried out on the reference set of pattern pixels to achieve a low error rate and a maximum speed of computation.

Automatic generation of nearly optimal decision trees for handwritten character recognition

Szkodzik A., Materka A.

Machine Graphics and Vision

1999

Vol. 8, No. 1

117-128

This paper present results of applying decision tree to printed and handwritten character recognition. An automatic feature generation method was employed during the construction of the tree, which improved the recognition rate for the testing set. This learning technique significantly reduces the drawback of the tree classifiers that is thier rapid error accumulation with depth, while it does not influence the size of trees. It was shown that the proposed approach gives better results thsn increasing the size of the training sets used for construction of the trees. The recognition rate above 97% was obtained by means of a parallel classifier built of multiple decision trees despite no advanced preprocessing of input characters (like skeletonization or slant reduction) was performed.