FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2747291751> ?p ?o ?g. }

• W2747291751 abstract "In this paper, we investigate the problem of counting rosette leaves from an RGB image, an important task in plant phenotyping. We propose a data-driven approach for this task generalized over different plant species and imaging setups. To accomplish this task, we use state-of-the-art deep learning architectures: a deconvolutional network for initial segmentation and a convolutional network for leaf counting. Evaluation is performed on the leaf counting challenge dataset at CVPPP-2017. Despite the small number of training samples in this dataset, as compared to typical deep learning image sets, we obtain satisfactory performance on segmenting leaves from the background as a whole and counting the number of leaves using simple data augmentation strategies. Comparative analysis is provided against methods evaluated on the previous competition datasets. Our framework achieves mean and standard deviation of absolute count difference of 1.62 and 2.30 averaged over all five test datasets." @default.
• W2747291751 created "2017-08-31" @default.
• W2747291751 creator A5003790536 @default.
• W2747291751 creator A5058630617 @default.
• W2747291751 date "2017-08-24" @default.
• W2747291751 modified "2023-09-27" @default.
• W2747291751 title "Leaf Counting with Deep Convolutional and Deconvolutional Networks" @default.
• W2747291751 cites W1506806321 @default.
• W2747291751 cites W1522301498 @default.
• W2747291751 cites W1594957066 @default.
• W2747291751 cites W1612399388 @default.
• W2747291751 cites W1686810756 @default.
• W2747291751 cites W1907110180 @default.
• W2747291751 cites W1923184257 @default.
• W2747291751 cites W2064675550 @default.
• W2747291751 cites W2069797029 @default.
• W2747291751 cites W2086330580 @default.
• W2747291751 cites W2096579040 @default.
• W2747291751 cites W2118246710 @default.
• W2747291751 cites W2118858186 @default.
• W2747291751 cites W2119821739 @default.
• W2747291751 cites W2124260943 @default.
• W2747291751 cites W2144499799 @default.
• W2747291751 cites W2229420909 @default.
• W2747291751 cites W2264784471 @default.
• W2747291751 cites W2278786050 @default.
• W2747291751 cites W2395611524 @default.
• W2747291751 cites W2412782625 @default.
• W2747291751 cites W2733608569 @default.
• W2747291751 cites W2949117887 @default.
• W2747291751 cites W2951527505 @default.
• W2747291751 cites W2951548327 @default.
• W2747291751 cites W2952637581 @default.
• W2747291751 cites W2962676885 @default.
• W2747291751 cites W2963881378 @default.
• W2747291751 cites W3208678520 @default.
• W2747291751 cites W636712700 @default.
• W2747291751 cites W753012316 @default.
• W2747291751 cites W7746136 @default.
• W2747291751 hasPublicationYear "2017" @default.
• W2747291751 type Work @default.
• W2747291751 sameAs 2747291751 @default.
• W2747291751 citedByCount "5" @default.
• W2747291751 countsByYear W27472917512017 @default.
• W2747291751 countsByYear W27472917512018 @default.
• W2747291751 countsByYear W27472917512020 @default.
• W2747291751 crossrefType "posted-content" @default.
• W2747291751 hasAuthorship W2747291751A5003790536 @default.
• W2747291751 hasAuthorship W2747291751A5058630617 @default.
• W2747291751 hasConcept C105795698 @default.
• W2747291751 hasConcept C108583219 @default.
• W2747291751 hasConcept C115961682 @default.
• W2747291751 hasConcept C153180895 @default.
• W2747291751 hasConcept C154945302 @default.
• W2747291751 hasConcept C162324750 @default.
• W2747291751 hasConcept C187736073 @default.
• W2747291751 hasConcept C22679943 @default.
• W2747291751 hasConcept C2780451532 @default.
• W2747291751 hasConcept C33923547 @default.
• W2747291751 hasConcept C41008148 @default.
• W2747291751 hasConcept C81363708 @default.
• W2747291751 hasConcept C82990744 @default.
• W2747291751 hasConcept C89600930 @default.
• W2747291751 hasConceptScore W2747291751C105795698 @default.
• W2747291751 hasConceptScore W2747291751C108583219 @default.
• W2747291751 hasConceptScore W2747291751C115961682 @default.
• W2747291751 hasConceptScore W2747291751C153180895 @default.
• W2747291751 hasConceptScore W2747291751C154945302 @default.
• W2747291751 hasConceptScore W2747291751C162324750 @default.
• W2747291751 hasConceptScore W2747291751C187736073 @default.
• W2747291751 hasConceptScore W2747291751C22679943 @default.
• W2747291751 hasConceptScore W2747291751C2780451532 @default.
• W2747291751 hasConceptScore W2747291751C33923547 @default.
• W2747291751 hasConceptScore W2747291751C41008148 @default.
• W2747291751 hasConceptScore W2747291751C81363708 @default.
• W2747291751 hasConceptScore W2747291751C82990744 @default.
• W2747291751 hasConceptScore W2747291751C89600930 @default.
• W2747291751 hasLocation W27472917511 @default.
• W2747291751 hasOpenAccess W2747291751 @default.
• W2747291751 hasPrimaryLocation W27472917511 @default.
• W2747291751 hasRelatedWork W1686810756 @default.
• W2747291751 hasRelatedWork W1907110180 @default.
• W2747291751 hasRelatedWork W2138983350 @default.
• W2747291751 hasRelatedWork W2194775991 @default.
• W2747291751 hasRelatedWork W2322011271 @default.
• W2747291751 hasRelatedWork W2752055492 @default.
• W2747291751 hasRelatedWork W2770754200 @default.
• W2747291751 hasRelatedWork W2884588043 @default.
• W2747291751 hasRelatedWork W2896259381 @default.
• W2747291751 hasRelatedWork W2951548327 @default.
• W2747291751 hasRelatedWork W2963754008 @default.
• W2747291751 hasRelatedWork W2968041196 @default.
• W2747291751 hasRelatedWork W2994639114 @default.
• W2747291751 hasRelatedWork W3081793526 @default.
• W2747291751 hasRelatedWork W3101973849 @default.
• W2747291751 hasRelatedWork W3103117538 @default.
• W2747291751 hasRelatedWork W3134306039 @default.
• W2747291751 hasRelatedWork W3163885127 @default.
• W2747291751 hasRelatedWork W3169387543 @default.
• W2747291751 hasRelatedWork W3200599879 @default.

• next