Over het archief
Het OWA, het open archief van het Waterbouwkundig Laboratorium heeft tot doel alle vrij toegankelijke onderzoeksresultaten van dit instituut in digitale vorm aan te bieden. Op die manier wil het de zichtbaarheid, verspreiding en gebruik van deze onderzoeksresultaten, alsook de wetenschappelijke communicatie maximaal bevorderen.
Dit archief wordt uitgebouwd en beheerd volgens de principes van de Open Access Movement, en het daaruit ontstane Open Archives Initiative.
Basisinformatie over ‘Open Access to scholarly information'.
Automated Artemia length measurement using U-shaped fully convolutional networks and second-order anisotropic Gaussian kernels
Wang, G.; Van Stappen, G.; De Baets, B. (2020). Automated Artemia length measurement using U-shaped fully convolutional networks and second-order anisotropic Gaussian kernels. Comput. Electron. Agric. 168: 105102. https://hdl.handle.net/10.1016/j.compag.2019.105102
In: Computers and Electronics in Agriculture. Elsevier: Amsterdam. ISSN 0168-1699; e-ISSN 1872-7107, meer
|  |
| Trefwoord |
Artemia Leach, 1819 [WoRMS]
|
| Author keywords |
Anemia length measurement; U-shaped neural networks; Second-order anisotropic Gaussian kernel; Mathematical morphology |
| Abstract |
The brine shrimp Artemia, a small crustacean zooplankton organism, is universally used as live prey for larval fish and shrimps in aquaculture. In Artemia studies, it would be highly desired to have access to automated techniques to obtain the length information from Artemia images. However, this problem has so far not been addressed in literature. Moreover, conventional image-based length measurement approaches cannot be readily transferred to measure the Artemia length, due to the distortion of non-rigid bodies, the variation over growth stages and the interference from the antennae and other appendages. To address this problem, we compile a dataset containing 250 images as well as the corresponding label maps of length measuring lines. We propose an automated Artemia length measurement method using U-shaped fully convolutional networks (UNet) and second-order anisotropic Gaussian kernels. For a given Artemia image, the designed UNet model is used to extract a length measuring line structure, and, subsequently, the second-order Gaussian kernels are employed to transform the length measuring line structure into a thin measuring line. For comparison, we also follow conventional fish length measurement approaches and develop a non-learning-based method using mathematical morphology and polynomial curve fitting. We evaluate the proposed method and the competing methods on 100 test images taken from the dataset compiled. Experimental results show that the proposed method can accurately measure the length of Artemia objects in images, obtaining a mean absolute percentage error of 1.16%. |
IMIS is ontwikkeld en wordt gehost door het VLIZ.
