Brovey transform based image fusion for impurities segmentation and detection on edible bird’s nest
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Date
2019-06
Authors
Kee, Seow Pei
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Abstract
Edible bird’s nest (EBN) is one of the most important products in food and agricultural industry in South East Asia. In Malaysia, the production of EBN soaring because of the exportation of EBN to meet the demand of overseas market. Assurance of cleanliness is one of the major difficulties faced in processing the EBN. Current cleaning method of EBN is labour dependency, time consuming and not cost effective. Automated inspection was introduced but still continues to exist as a challenging field of development as there is no effective algorithms for impurities segmentation. Some impurities have similar colour as EBN features which increase the complexity of image processing. In this study, Brovey transform based image fusion is used to highlight the impurities in EBN and ease the segmentation process. Various types of Multispectral (MS) reference images were considered in image fusion process. Comparison was made to obtain the MS reference image with highest accuracy of segmented region. The performances of fused images are evaluated based on segmentation rate, precision, accuracy, error rate and dice similarity index (DSI). The optimal performances were achieved by the green light without erosion MS reference image with an overall segmentation rate of 49.96%, precision of 48.78%, accuracy of 40.00%, error rate of 60% and DSI of 0.571.