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Genetic Programming for algae detection in river images

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conference contribution
posted on 06.10.2020, 22:10 by Andrew Lensen, Harith Al-Sahaf, Mengjie Zhang, B Verma
© 2015 IEEE. Genetic Programming (GP) has been applied to a wide range of image analysis tasks including many real-world segmentation problems. This paper introduces a new biological application of detecting Phormidium algae in rivers of New Zealand using raw images captured from the air. In this paper, we propose a GP method to the task of algae detection. The proposed method synthesises a set of image operators and adopts a simple thresholding approach to segmenting an image into algae and non-algae regions. Furthermore, the introduced method operates directly on raw pixel values with no human assistance required. The method is tested across seven different images from different rivers. The results show good success on detecting areas of algae much more efficiently than traditional manual techniques. Furthermore, the result achieved by the proposed method is comparable to the hand-crafted ground truth with a F-measure fitness value of 0.64 (where 0 is best, 1 is worst) on average on the test set. Issues such as illumination, reflection and waves are discussed.

History

Preferred citation

Lensen, A., Al-Sahaf, H., Zhang, M. & Verma, B. (2015, September). Genetic Programming for algae detection in river images. In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings 2015 IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan (pp. 2468-2475). Online: IEEE. https://doi.org/10.1109/CEC.2015.7257191

Conference name

2015 IEEE Congress on Evolutionary Computation (CEC)

Conference Place

Sendai, Japan

Conference start date

25/05/2015

Conference finish date

28/05/2015

Title of proceedings

2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Series

IEEE Congress on Evolutionary Computation

Contribution type

Published Paper

Publication or Presentation Year

14/09/2015

Pagination

2468-2475

Publisher

IEEE

Publication status

Published

Place of publication

Online