Automated identification of benthic epifauna with computer vision | Zendy

Nils Piechaud | Zendy; Charles B. Hunt | Zendy; Phil Culverhouse | Zendy; Nicola L. Foster | Zendy; Kerry L. Howell | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Automated identification of benthic epifauna with computer vision

Author(s) -

Nils Piechaud,

Charles B. Hunt,

Phil Culverhouse,

Nicola L. Foster,

Kerry L. Howell

Publication year - 2019

Publication title -

marine ecology progress series

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.151

H-Index - 188

eISSN - 1616-1599

pISSN - 0171-8630

DOI - 10.3354/meps12925

Subject(s) - benthic zone , bottleneck , identification (biology) , benthic habitat , computer science , citizen science , underwater , artificial intelligence , ecology , oceanography , biology , geology , botany , embedded system

Figure S1: Pair-wise permutation-based analysis of variance of differences in sensitivity (upper left triangle of the matrix) and precision (lower right triangle of the matrix) between each treatment. The numbers in central cells indicates sensitivity (left) and precision (right) of corresponding treatments on the axis. Significance level indicate at which alpha threshold the two treatments are significantly different in percentages of maximal value (i.e. 1). No dif. indicates a p-value above 0.05.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research