- author = "Simone Marini and Ernesto Azzurro and Salvatore Coco and Joaquin {Del Rio} and Sergio Enguidanos and Emanuela Fanelli and Marc Nogueras and Valerio Sbragaglia and Daniel Toma and Jacopo Aguzzi",
- title = "Automatic fish counting from underwater video images: performance estimation and evaluation",
- booktitle = "7th International Workshop on Marine Technologies (MARTECH 2016)",
- year = "2016",
- editor = "Juan Jose Danobeitia",
- series = "ID. 23",
- address = "Instituto de Ciencias del Mar, Barcelona, Spain",
- month = "26-27 " # oct,
- organisation = "CSIC and UPC",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
https://upcommons.upc.edu/handle/2117/99939",
-
URL = "
http://www.upc.edu/cdsarti/martech/usb_2016/papers/23.pdf",
- size = "4 pages",
- abstract = "Cabled observatories offer new opportunities to monitor species abundances at frequencies and durations never attained before. When nodes bear cameras, these may be transformed into the first sensor capable of quantifying biological activities at individual, population, species, and community levels, if automation image processing can be sufficiently implemented. Here, we developed a binary classifier for the fish automated recognition based on Genetic Programming tested on the images provided by OBSEA EMSO testing site platform located at 20 m of depth off Vilanova i la Gertru (Spain). The performance evaluation of the automatic classifier resulted in a 78percent of accuracy compared with the manual counting. Considering the huge dimension of data provided by cabled observatories and the difficulty of manual processing, we consider this result highly promising also in view of future implementation of the methodology to increase the accuracy.",
-
notes = "Broken Feb 2022
http://www.upc.edu/cdsarti/martech/usb_2016/index.html
broken Nov 2017 martech-workshop.org",
