Papers

@article{Rico-Díaz20201,

title = {An application of fish detection based on eye search with artificial vision and artificial neural networks},

author = {Ángel J. Rico-Díaz and Juan R. Rabuñal-Dopico and Marcos Gestal and Omar A. Mures and Jerónimo Puertas-Agudo},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095963830&doi=10.3390%2fw12113013&partnerID=40&md5=5574ba04a0071c55f7fab253aaec24b3},

doi = {10.3390/w12113013},

issn = {20734441},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Water (Switzerland)},

volume = {12},

number = {11},

pages = {1-20},

publisher = {MDPI AG},

abstract = {A fish can be detected by means of artificial vision techniques, without human intervention or handling the fish. This work presents an application for detecting moving fish in water by artificial vision based on the detection of a fish′ s eye in the image, using the Hough algorithm and a Feed-Forward network. In addition, this method of detection is combined with stereo image recording, creating a disparity map to estimate the size of the detected fish. The accuracy and precision of this approach has been tested in several assays with living fish. This technique is a non-invasive method working in real-time and it can be carried out with low cost. Furthermore, it could find application in aquariums, fish farm management and to count the number of fish which swim through a fishway. In a fish farm it is important to know how the size of the fish evolves in order to plan the feeding and when to be able to catch fish. Our methodology allows fish to be detected and their size and weight estimated as they move underwater, engaging in natural behavior. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.},

note = {cited By 4},

keywords = {Accuracy and precision; Disparity map; Feed-forward network; Fish farms; Hough algorithms; Human intervention; Noninvasive methods; Real time, agricultural technology; algorithm; artificial neural network; body size; catchability; computer vision; detection method; digital mapping; precision; stereo image, Feedforward neural networks; Fisheries; Image recording; Noninvasive medical procedures; Stereo image processing; Technology transfer; Vision, Fish},

pubstate = {published},

tppubtype = {article}

}

@article{Rodriguez2015419,

title = {Fish monitoring and sizing using computer vision},

author = {Álvaro Rodríguez and Ángel J. Rico-Díaz and Juan R. Rabuñal-Dopico and Jerónimo Puertas-Agudo and Luis Pena-Mosquera},

editor = {Adeli H. Paz Lopez F. Alvarez-Sanchez J.R.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937682467&doi=10.1007%2f978-3-319-18833-1_44&partnerID=40&md5=fabdeb31995aca27678a9b2c3968240a},

doi = {10.1007/978-3-319-18833-1_44},

issn = {03029743},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

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

volume = {9108},

pages = {419-428},

publisher = {Springer Verlag},

abstract = {This paper proposes an image processing algorithm, based in a non invasive 3D optical stereo system and the use of computer vision techniques, to study fish in fish tanks or pools. The proposed technique will allow to study biological variables of different fish species in underwater environments. This knowledge, may be used to replace traditional techniques such as direct observation, which are impractical or affect the fish behavior, in task such as aquarium and fish farm management or fishway evaluation. The accuracy and performance of the proposed technique has been tested, conducting different assays with living fishes, where promising results were obtained. © Springer International Publishing Switzerland 2015},

note = {cited By 14},

keywords = {Algorithms; Computer vision; Fisheries; Image processing; Image segmentation; Optical data processing; Stereo image processing; Stereo vision, Biological variables; Computer vision techniques; Direct observations; Fish behavior; Image processing algorithm; Stereo system; Traditional techniques; Underwater environments, Fish},

pubstate = {published},

tppubtype = {article}

}

@article{Rodriguez2013404,

title = {Detection of fishes in turbulent waters based on image analysis},

author = {Álvaro Rodríguez and Juan R. Rabuñal-Dopico and María Bermudez and Jerónimo Puertas-Agudo},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885723957&doi=10.1007%2f978-3-642-38622-0_42&partnerID=40&md5=cae9579dc359a89dd7a283e975f894b4},

doi = {10.1007/978-3-642-38622-0_42},

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 = {7931 LNCS},

number = {PART 2},

pages = {404-412},

abstract = {This paper analyses the automatic fish segmentation problem in turbulent waters. To this end, a SOM neural network is used to detect fishes in images from an underwater camera system built in a vertical slot fishway, an hydraulic structure built in obstructions in rivers to allow the upstream migration of fishes. This technique allows the study of real fish behavior and may help to understand biological variables and swimming limitations of the fish species in high speed environments. This knowledge, may be used to replace traditional techniques such as direct observation or placement of sensors on the specimens, which are impractical or affect the fish behavior. To test the proposed technique, a ground true dataset was designed with experts and a series of assays have been performed where the results obtained with the proposed technique were compared with different segmentation techniques. © 2013 Springer-Verlag.},

note = {cited By 2},

keywords = {Biological variables; Fish-Detection; Placement of sensors; Segmentation techniques; SOM; Traditional techniques; Turbulent water; Vertical slot fishways, Fish, Fisheries; Image segmentation; Medical applications; Statistical tests},

pubstate = {published},

tppubtype = {article}

}

@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}

}