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Selection: with tag semantic-segmentation [4 articles] 

 

Segnet: a deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling

  
(2015)

Abstract

We propose a novel deep architecture, SegNet, for semantic pixel wise image labelling. SegNet has several attractive properties; (i) it only requires forward evaluation of a fully learnt function to obtain smooth label predictions, (ii) with increasing depth, a larger context is considered for pixel labelling which improves accuracy, and (iii) it is easy to visualise the effect of feature activation(s) in the pixel label space at any depth. SegNet is composed of a stack of encoders followed by a corresponding ...

 

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

  
(10 Oct 2016)

Abstract

We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network. The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the ...

 

Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images

  
Remote Sensing, Vol. 9, No. 4. (13 April 2017), 368, https://doi.org/10.3390/rs9040368

Abstract

Like computer vision before, remote sensing has been radically changed by the introduction of deep learning and, more notably, Convolution Neural Networks. Land cover classification, object detection and scene understanding in aerial images rely more and more on deep networks to achieve new state-of-the-art results. Recent architectures such as Fully Convolutional Networks can even produce pixel level annotations for semantic mapping. In this work, we present a deep-learning based segment-before-detect method for segmentation and subsequent detection and classification of several varieties ...

 

(INRMM-MiD internal record) List of keywords of the INRMM meta-information database - part 36

  
(February 2014)
Keywords: inrmm-list-of-tags   saraca-asoca   sassafras-albidum   sassafras-spp   satellites   saudi-arabia   savanna   savannas   saxifraga-rotundifolia   saxony   scalability   scale-free-network   scale-invariance   scale-vs-pixel   scandinavia   scaphoideus-titanus   scarcity   scavengers   scenario-analysis   schima-superba   schinopsis-balansae   schinus-molle   schinus-terebinthifolius   scholarly-poor   science-2-0   science-based-decision-making   science-ethics   science-history   science-literacy   science-policy-interface   science-society-interface   scientific-communication   scientific-community-self-correction   scientific-creativity   scientific-debate   scientific-knowledge-sharing   scientific-misconduct   scientific-software   scientific-topics-focus   scilab   scipy   scirrhia-pini   sclerophyllous   scolytinae   scolytus-intricatus   scolytus-multistriatus   scolytus-spp   scopus   scopus-indexed   scotland   scottnema-lindsayae   scrub   scrubland   sdm   sea   sea-level   second-order-science   secondary-metabolism   secondary-opportunistic-pest   secondary-production   sediment   sediment-flushing   sediment-retention   sediment-sluicing   sediment-transport   sediment-yield   seed-dispersal   seed-limitation   seed-orchard   seed-predation   seed-production   seed-sterility   seedling-production   seedling-recruitment   seedlings   seeds   seiridium-cardinale   seiridium-spp   seismicity   self-adaptive-systems   self-fertile   self-healing   self-organization   self-similarity   self-stabilisation   sell   semantic-array-programming   semantic-constraints   semantic-segmentation   semantic-web   semantically-enhanced-library-languages   semantics   semap   semi-natural-habitat   senecio-spp   senegal   sensitivity   separation-of-concerns   septoria-musiva   sequoia-abietina  

Abstract

List of indexed keywords within the transdisciplinary set of domains which relate to the Integrated Natural Resources Modelling and Management (INRMM). In particular, the list of keywords maps the semantic tags in the INRMM Meta-information Database (INRMM-MiD). [\n] The INRMM-MiD records providing this list are accessible by the special tag: inrmm-list-of-tags ( http://mfkp.org/INRMM/tag/inrmm-list-of-tags ). ...

This page of the database may be cited as:
Integrated Natural Resources Modelling and Management - Meta-information Database. http://mfkp.org/INRMM/tag/semantic-segmentation

Publication metadata

Bibtex, RIS, RSS/XML feed, Json, Dublin Core

Meta-information Database (INRMM-MiD).
This database integrates a dedicated meta-information database in CiteULike (the CiteULike INRMM Group) with the meta-information available in Google Scholar, CrossRef and DataCite. The Altmetric database with Article-Level Metrics is also harvested. Part of the provided semantic content (machine-readable) is made even human-readable thanks to the DCMI Dublin Core viewer. Digital preservation of the meta-information indexed within the INRMM-MiD publication records is implemented thanks to the Internet Archive.
The library of INRMM related pubblications may be quickly accessed with the following links.
Search within the whole INRMM meta-information database:
Search only within the INRMM-MiD publication records:
Full-text and abstracts of the publications indexed by the INRMM meta-information database are copyrighted by the respective publishers/authors. They are subject to all applicable copyright protection. The conditions of use of each indexed publication is defined by its copyright owner. Please, be aware that the indexed meta-information entirely relies on voluntary work and constitutes a quite incomplete and not homogeneous work-in-progress.
INRMM-MiD was experimentally established by the Maieutike Research Initiative in 2008 and then improved with the help of several volunteers (with a major technical upgrade in 2011). This new integrated interface is operational since 2014.