Matches in SemOpenAlex for { <https://semopenalex.org/work/W3204679239> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W3204679239 endingPage "9039" @default.
- W3204679239 startingPage "9039" @default.
- W3204679239 abstract "Individual fish identification and recognition is an important step in the conservation and management of fisheries. One of most frequently used methods involves capturing and tagging fish. However, these processes have been reported to cause tissue damage, premature tag loss, and decreased swimming capacity. More recently, marine video recordings have been extensively used for monitoring fish populations. However, these require visual inspection to identify individual fish. In this work, we proposed an automatic method for the identification of individual brown trouts, Salmo trutta. We developed a deep convolutional architecture for this purpose. Specifically, given two fish images, multi-scale convolutional features were extracted to capture low-level features and high-level semantic components for embedding space representation. The extracted features were compared at each scale for capturing representation for individual fish identification. The method was evaluated on a dataset called NINA204 based on 204 videos of brown trout and on a dataset TROUT39 containing 39 brown trouts in 288 frames. The identification method distinguished individual fish with 94.6% precision and 74.3% recall on a NINA204 video sequence with significant appearance and shape variation. The identification method takes individual fish and is able to distinguish them with precision and recall percentages of 94.6% and 74.3% on NINA204 for a video sequence with significant appearance and shape variation." @default.
- W3204679239 created "2021-10-11" @default.
- W3204679239 creator A5043925072 @default.
- W3204679239 creator A5088359880 @default.
- W3204679239 date "2021-09-28" @default.
- W3204679239 modified "2023-09-25" @default.
- W3204679239 title "Photo Identification of Individual Salmo trutta Based on Deep Learning" @default.
- W3204679239 cites W1966967479 @default.
- W3204679239 cites W1990684576 @default.
- W3204679239 cites W2006210275 @default.
- W3204679239 cites W2016164851 @default.
- W3204679239 cites W2023014101 @default.
- W3204679239 cites W2065788615 @default.
- W3204679239 cites W2065929128 @default.
- W3204679239 cites W2086719072 @default.
- W3204679239 cites W2087985533 @default.
- W3204679239 cites W2099976952 @default.
- W3204679239 cites W2115388023 @default.
- W3204679239 cites W2120809392 @default.
- W3204679239 cites W2125979699 @default.
- W3204679239 cites W2141207183 @default.
- W3204679239 cites W2147449554 @default.
- W3204679239 cites W2161920802 @default.
- W3204679239 cites W2258572241 @default.
- W3204679239 cites W2266428003 @default.
- W3204679239 cites W2316842340 @default.
- W3204679239 cites W2751825910 @default.
- W3204679239 cites W2790996547 @default.
- W3204679239 cites W2890186834 @default.
- W3204679239 cites W2941123315 @default.
- W3204679239 cites W2947929494 @default.
- W3204679239 cites W3047343195 @default.
- W3204679239 cites W3047713023 @default.
- W3204679239 cites W3151882058 @default.
- W3204679239 cites W3169512507 @default.
- W3204679239 doi "https://doi.org/10.3390/app11199039" @default.
- W3204679239 hasPublicationYear "2021" @default.
- W3204679239 type Work @default.
- W3204679239 sameAs 3204679239 @default.
- W3204679239 citedByCount "3" @default.
- W3204679239 countsByYear W32046792392022 @default.
- W3204679239 countsByYear W32046792392023 @default.
- W3204679239 crossrefType "journal-article" @default.
- W3204679239 hasAuthorship W3204679239A5043925072 @default.
- W3204679239 hasAuthorship W3204679239A5088359880 @default.
- W3204679239 hasBestOaLocation W32046792391 @default.
- W3204679239 hasConcept C116834253 @default.
- W3204679239 hasConcept C153180895 @default.
- W3204679239 hasConcept C154945302 @default.
- W3204679239 hasConcept C18903297 @default.
- W3204679239 hasConcept C2777940460 @default.
- W3204679239 hasConcept C2909208804 @default.
- W3204679239 hasConcept C2993170677 @default.
- W3204679239 hasConcept C41008148 @default.
- W3204679239 hasConcept C505870484 @default.
- W3204679239 hasConcept C81363708 @default.
- W3204679239 hasConcept C86803240 @default.
- W3204679239 hasConceptScore W3204679239C116834253 @default.
- W3204679239 hasConceptScore W3204679239C153180895 @default.
- W3204679239 hasConceptScore W3204679239C154945302 @default.
- W3204679239 hasConceptScore W3204679239C18903297 @default.
- W3204679239 hasConceptScore W3204679239C2777940460 @default.
- W3204679239 hasConceptScore W3204679239C2909208804 @default.
- W3204679239 hasConceptScore W3204679239C2993170677 @default.
- W3204679239 hasConceptScore W3204679239C41008148 @default.
- W3204679239 hasConceptScore W3204679239C505870484 @default.
- W3204679239 hasConceptScore W3204679239C81363708 @default.
- W3204679239 hasConceptScore W3204679239C86803240 @default.
- W3204679239 hasIssue "19" @default.
- W3204679239 hasLocation W32046792391 @default.
- W3204679239 hasOpenAccess W3204679239 @default.
- W3204679239 hasPrimaryLocation W32046792391 @default.
- W3204679239 hasRelatedWork W141628535 @default.
- W3204679239 hasRelatedWork W1523506726 @default.
- W3204679239 hasRelatedWork W1977402222 @default.
- W3204679239 hasRelatedWork W2118489860 @default.
- W3204679239 hasRelatedWork W2145502679 @default.
- W3204679239 hasRelatedWork W2148529995 @default.
- W3204679239 hasRelatedWork W2233059721 @default.
- W3204679239 hasRelatedWork W2556837733 @default.
- W3204679239 hasRelatedWork W2936833100 @default.
- W3204679239 hasRelatedWork W63869769 @default.
- W3204679239 hasVolume "11" @default.
- W3204679239 isParatext "false" @default.
- W3204679239 isRetracted "false" @default.
- W3204679239 magId "3204679239" @default.
- W3204679239 workType "article" @default.