Paper
22 October 1993 Foreign object detection via texture recognition and a neural classifier
Devesh Patel, I. Hannah, E. R. Davies
Author Affiliations +
Proceedings Volume 2094, Visual Communications and Image Processing '93; (1993) https://doi.org/10.1117/12.157886
Event: Visual Communications and Image Processing '93, 1993, Cambridge, MA, United States
Abstract
It is rate to find pieces of stone, wood, metal, or glass in food packets, but when they occur, these foreign objects (FOs) cause distress to the consumer and concern to the manufacturer. Using x-ray imaging to detect FOs within food bags, hard contaminants such as stone or metal appear darker, whereas soft contaminants such as wood or rubber appear slightly lighter than the food substrate. In this paper we concentrate on the detection of soft contaminants such as small pieces of wood in bags of frozen corn kernels. Convolution masks are used to generate textural features which are then classified into corresponding homogeneous regions on the image using an artificial neural network (ANN) classifier. The separate ANN outputs are combined using a majority operator, and region discrepancies are removed by a median filter. Comparisons with classical classifiers showed the ANN approach to have the best overall combination of characteristics for our particular problem. The detected boundaries are in good agreement with the visually perceived segmentations.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Devesh Patel, I. Hannah, and E. R. Davies "Foreign object detection via texture recognition and a neural classifier", Proc. SPIE 2094, Visual Communications and Image Processing '93, (22 October 1993); https://doi.org/10.1117/12.157886
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Cited by 3 scholarly publications.
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KEYWORDS
Image segmentation

Image filtering

Convolution

Digital filtering

Artificial neural networks

Metals

Image classification

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