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Binary Masks of Concentric Rings as a Method to Approximate Identification of Diatoms Using Images
- Source :
- IEEE Access, Vol 8, Pp 141497-141510 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Diatoms are currently being used in different fields, and their application as bioindicators is one of their main benefits. Their identification is carried out by specialists, the main difficulty being the wide variety of existing species and the great similarity between some of them. In recent years attempts have been made to create an automatic identification system with the collaboration of experts in areas of pattern recognition. In this research we analyzed the use of binary masks of rings in images as a method to automatically identify diatoms, or render an approximative identification. Among the advantages of the proposed method is its invariance regarding the different positions in which the diatoms can be found. Invariance to illumination changes is achieved by means of different transforms, such as the power law transform. The image databases used to develop the algorithms were diatom images taken from samples collected at springs in Lake Patzcuaro, Michoacan, Mexico, at the San Lazaro basin, Baja California Sur, Mexico and from the public image database of ADIAC. In morphological and textural descriptors, the background of the image must be eliminated in order to classify the diatoms, whereas in the proposed methodology this is not necessary. The obtained results reach over than 90% even when the diatoms in the images are broken.
- Subjects :
- 0106 biological sciences
Similarity (geometry)
General Computer Science
Computer science
Binary number
automatic classification
01 natural sciences
Concentric ring
Image (mathematics)
03 medical and health sciences
General Materials Science
030304 developmental biology
Diatoms
0303 health sciences
biology
business.industry
010604 marine biology & hydrobiology
General Engineering
Pattern recognition
biology.organism_classification
TK1-9971
Identification (information)
Diatom
Pattern recognition (psychology)
Fourier transform
Artificial intelligence
automatic recognition
Electrical engineering. Electronics. Nuclear engineering
business
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....760e104e0377a7dfcdacd1d4a66b3696