Publicacións

@article{Rodriguez2013445,

title = {Automatic fish segmentation on vertical slot fishways using SOM neural networks},

author = {Álvaro Rodríguez and Juan R. Rabuñal-Dopico and María Bermúdez and Alejandro Pazos},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880077800&doi=10.1007%2f978-3-642-38679-4_44&partnerID=40&md5=6120c419a7282f1056715d7b8c1fd619},

doi = {10.1007/978-3-642-38679-4_44},

issn = {03029743},

year  = {2013},

date = {2013-01-01},

urldate = {2013-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {7902 LNCS},

number = {PART 1},

pages = {445-452},

abstract = {Vertical slot fishways are hydraulic structures which allow the upstream migration of fish through obstructions in rivers. The appropriate design of these should consider the behavior and biological variables of the target fish species and currently existing mechanisms to measure the behavior of the fish in these assays, such as direct observation or placement of sensors on the specimens, are impractical or unduly affect the animal behavior. This paper studies the application of Artificial Neural Networks to the problem of automatic fish segmentation in vertical slot fishways. In particular, SOM Neural Networks have been used to detect fishes using visual information sampled by an underwater camera system. A ground true dataset was designed with experts and different approaches were tested providing promising results. © 2013 Springer-Verlag Berlin Heidelberg.},

note = {cited By 0},

keywords = {ANN; Appropriate designs; Biological variables; Fish-Detection; Placement of sensors; SOM; Som neural networks; Vertical slot fishways, Fish, Fisheries; Fishways; Image segmentation; Neural networks},

pubstate = {published},

tppubtype = {article}

}