Automatic fish counting from underwater video images: performance estimation and evaluation

Created by W.Langdon from gp-bibliography.bib Revision:1.3989

@InProceedings{Marini2016Martech,
  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 =        "http://www.upc.edu/cdsarti/martech/usb_2016/index.html

                 broken Nov 2017 martech-workshop.org",
}

Genetic Programming entries for Simone Marini Ernesto Azzurro Salvatore Coco Joaquin Del Rio Sergio Enguidanos Emanuela Fanelli Marc Nogueras Cervera Valerio Sbragaglia Daniel Toma Jacopo Aguzzi

Citations