Fault detection using mathematical morphology and clustering

• Autorzy: Iwanowski M.
• Konferencja: Conference on Computer Graphics and Image Processing, GKPO' 2000 (6 ; 15-19.05.2000 ; Polesie, Poland)
• Języki publikacji: EN

Abstrakty

EN

This paper describes an approach to the problem of fault detection. Fault detection is considered hare as a segmentation task. One need to segment the initial image in order ti find areas of a particular kind - failts. The approach presented combines the mathematical morphology and clustering. The morphological treatment is based on morphological filtering by reconstruction but it includes also some other tools like top-hat transformation and contrast improvement. Clustering is performed by the k-means algorith. Three methods of fault detection are presented here. Each of them is destined to the segmentation of the images of different kind.

Słowa kluczowe

EN

fault detection; clustering; k-means; mathematical morphology; reconstruction; image segmentation

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2000
• Tom: Vol. 9, No. 1/2
• Strony: 215--220
• Opis fizyczny: Bibliogr. 10 poz.

Twórcy

autor

Iwanowski M.

• Institute of Control and Industrial Electronics, Warsaw University of Technology ul. Koszykowa 75, 00-662 Warszawa,