• DocumentCode
    3306112
  • Title

    Image-based date fruit classification

  • Author

    Haidar, Azzam ; Haiwei Dong ; Mavridis, Nikolaos

  • Author_Institution
    Massachusetts Inst. of Technol., Cambridge, MA, USA
  • fYear
    2012
  • fDate
    3-5 Oct. 2012
  • Firstpage
    357
  • Lastpage
    363
  • Abstract
    Date fruits are small fruits that are abundant and popular in the Middle East, and have growing international presence. There are many different types of dates, each with different features. Sorting of dates is a key process in the date industry, and can be a tedious job. In this paper, we present a method for automatic classification of date fruits based on computer vision and pattern recognition. The method was implemented, and empirically tested on an image data spanning seven different categories of dates. In our method, an appropriately crafted mixture of fifteen different visual features was extracted, and then, multiple methods of classification were tried out, until satisfactory performance was achieved. Top accuracies ranged between 89% and 99%.
  • Keywords
    computer vision; feature extraction; food processing industry; food products; image classification; production engineering computing; automatic classification; computer vision; date industry; dates category; dates sorting; image data; image-based date fruit classification; middle east; pattern recognition; visual feature extraction; Accuracy; Feature extraction; Image color analysis; Shape; Standards; Training; Visualization; automated sorting; computer vision; date feature extraction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 2012 4th International Congress on
  • Conference_Location
    St. Petersburg
  • ISSN
    2157-0221
  • Print_ISBN
    978-1-4673-2016-0
  • Type

    conf

  • DOI
    10.1109/ICUMT.2012.6459693
  • Filename
    6459693