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Genetic programming for algae detection in river images

conference contribution
posted on 06.12.2017, 00:00 by A Lensen, H Al-Sahaf, M Zhang, Brijesh Verma
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

Start Page

2468

End Page

2475

Number of Pages

8

Start Date

01/01/2015

Finish Date

01/01/2015

ISSN

1089-778X

ISBN-13

9781479974924

Location

Sendai, Japan

Publisher

IEEE

Place of Publication

Piscataway, NJ.

Peer Reviewed

Yes

Open Access

No

Era Eligible

Yes

Name of Conference

Congress on Evolutionary Computation