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Bones detection from chicken breast meat using a competitive hopfield neural network and fuzzy filtering
2001
European Society for Fuzzy Logic and Technology
Preserved Fulltext
The contaminant detection process of a food product is an important stage of a modem food production factory. The automatic detection of bones in raw chicken breast meat is a major problem. This paper presents a fuzzy reasoning algorithm for post-processing the segmented images of dual-band X-ray images of raw chicken breast. The results show that bone detection rates are considerably improved over non-fuzzy or crisp methods without increasing the false negative rate. Further work is proposed and recommendations made for improving the method.
dblp:conf/eusflat/AmzaI01
fatcat:u3udqt6b5bfilanznp3rukkefe