• DocumentCode
    3668665
  • Title

    A new method for the segmentation of algae images using retinex and support vector machine

  • Author

    Kyle Dannemiller;Kaveh Ahmadi;Ezzatollah Salari

  • Author_Institution
    Department of Electrical Engineering and Computer Science, University of Toledo, Toledo, Ohio, USA 43606
  • fYear
    2015
  • fDate
    5/1/2015 12:00:00 AM
  • Firstpage
    361
  • Lastpage
    364
  • Abstract
    Bodies of freshwater act as home to many different types of organisms, including algae. These algae can cause harm when something called a harmful algal bloom takes place, and as such it is desired to classify algae in micro-image samples from the freshwater bodies before a bloom occurs. This paper presents a novel method for improving the quality of the algae micro-image and segmenting the algae in the micro-image, two of the steps involved in the automatic recognition and classification of algae in images. First, the algae image quality was improved through the use of the Retinex enhancement technique. Then, the algae in the improved quality image was segmented from the background using a support vector machine. Experimental results indicate that the detection rate of the proposed method is over 95%.
  • Keywords
    "Algae","Support vector machines","Image segmentation","Image quality","Classification algorithms","Feature extraction","Training"
  • Publisher
    ieee
  • Conference_Titel
    Electro/Information Technology (EIT), 2015 IEEE International Conference on
  • Electronic_ISBN
    2154-0373
  • Type

    conf

  • DOI
    10.1109/EIT.2015.7293369
  • Filename
    7293369