A parallel net of (1-NN, K-NN) classifiers for optical inspection of surface defects in ferrites

• Autorzy: Jóźwik A. , Chmielewski L. , Skłodowski M. , Cudny W.
• Konferencja: Conference on Computer Graphics and Image Processing, GKPO' 98 (5 ; 18-22.05.1998 ; Borki, Poland)
• Języki publikacji: EN

Abstrakty

EN

The challenging task of optical inspection of surface defects in ferrite cores has been successfully approached with a set of methods. In this paper the attention is paid to the k Nearest Neighbours classifier developed for the system. A parallel net of two-decision classifiers is presented. The combination of the 1-NN and k-NN rules reduces the training time. A great part of computations is restricted to the class overlap area. The classification quality is significantly improved if a separate feature selection for each of the component classifiers is done. A dramatic improvement of classification speed obtained by reference patterns sets reduction for component classifiers is vital, as in the considered task the classifier is used for recognition of pixels. The proposed modifications of the classifier are of general usefulness for pattern recognition. The presented quality inspection system can be applied to various defect detection tasks.

Słowa kluczowe

EN

statistical pattern recognition; nonparametric methods; k-NN rules; parallel classifiers; quality inspection; ferrite cores

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 1998
• Tom: Vol. 7, No. 1/2
• Strony: 99--112
• Opis fizyczny: Bibliogr. 16 poz.

Twórcy

autor

Jóźwik A.

autor

Chmielewski L.

autor

Skłodowski M.

autor

Cudny W.