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Intelligent Bimodal Segmentation Applied on CFA Images of Food Products

Author

Listed:
  • Ousman Boukar

    (The University of Ngaoundere, Cameroon.)

  • Gamraïkréo Djaowe

    (The University of Ngaoundere, Cameroon.)

  • Alban Ngatchou

    (The University of Ngaoundere, Cameroon.)

  • Laurent Bitjoka

    (The University of Ngaoundere, Cameroon.)

Abstract

Images segmentation is an essential step in the non-destructive analysis of food products by image analysis. It participates upstream in the operations of classification, identification or extraction of image attributes. To implement our segmentation approach, we extracted from each CFA image The R, G, B (for the RGB color space), and H, S, V (for the HSV color space). We then calculate the histogram of each of the color components. The component S for which the bimodal histogram was the most pronounced was selected and the binary mask was created from this component. The created binary mask has been filtered using morphological closing and opening operations. The application of each binary mask to its image CFA host, allowed us to separate with success the pixels representing the food products from the pixels of the background of the image CFA.

Suggested Citation

  • Ousman Boukar & Gamraïkréo Djaowe & Alban Ngatchou & Laurent Bitjoka, 2019. "Intelligent Bimodal Segmentation Applied on CFA Images of Food Products," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 3(5), September.
    • Handle: RePEc:epw:ejece0:v:3:y:2019:i:5:id:19116
    DOI: 10.24018/ejece.2019.3.5.116
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