A machine vision system was developed to inspect roses, and assign grades similar to those used by human graders. Illumination and image processing techniques were developed to extract the main features used by human vision. These features were identified as stem length, diameter and straightness, and bud color and maturity (i.e., openness). Illumination techniques include the design of an inspection chamber and the choice of proper light source. Image analysis includes the development of algorithms for image understanding. Supervised learning was used to classify roses.


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    Title :

    Machine vision techniques for rose grading


    Contributors:

    Conference:

    Vision, Sensors, and Control for Automated Manufacturing Systems ; 1993 ; Boston,MA,United States


    Published in:

    Publication date :

    1993-11-29





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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