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dc.contributor.authorHusain, Syed Farzad
dc.contributor.authorDellen, Babette
dc.contributor.authorTorras, Carme
dc.contributor.otherInstitut de Robòtica i Informàtica Industrial
dc.date.accessioned2018-04-06T13:56:19Z
dc.date.available2018-04-06T13:56:19Z
dc.date.issued2017
dc.identifier.citationHusain, S., Dellen, B., Torras, C. Scene understanding using deep learning. A: "Handbook of neural computation". Elsevier, 2017, p. 373-382.
dc.identifier.isbn9780128113189
dc.identifier.urihttp://hdl.handle.net/2117/116046
dc.description© <year>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.abstractAutomation based on artificial intelligence becomes necessary when agents such as robots are deployed to perform complex tasks. Detailed representation of a scene makes robots better aware of their surroundings, thereby making it possible to accomplish different tasks in a successful and safe manner. Tasks that involve planning of actions and manipulation of objects require identification and localization of different surfaces in dynamic environments. The usage of structured-light-based depth-sensing devices has gained much attention in the past decade. This is because they are low-cost and capture data in the form of dense depth maps, in addition to color images. Convolutional Neural Networks (CNNs) provide a robust way to extract useful information from the data acquired using these devices. In this chapter we discuss the basic idea behind standard feedforward CNNs, and their application to semantic segmentation and action recognition.
dc.format.extent10 p.
dc.language.isoeng
dc.publisherElsevier
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.othercomputer vision
dc.subject.otherfeature extraction
dc.subject.otherlearning (artificial intelligence)
dc.subject.otherobject recognition
dc.subject.otherrobot vision
dc.subject.otherScene understanding
dc.subject.otherDeep learning
dc.subject.otherSemantic labeling
dc.subject.otherAction recognition
dc.titleScene understanding using deep learning
dc.typePart of book or chapter of book
dc.contributor.groupUniversitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI
dc.description.peerreviewedPeer Reviewed
dc.subject.inspecClassificació INSPEC::Pattern recognition
dc.relation.publisherversionhttps://www.elsevier.com/books/handbook-of-neural-computation/samui/978-0-12-811318-9
dc.rights.accessOpen Access
local.identifier.drac21859066
dc.description.versionPostprint (author's final draft)
local.citation.authorHusain, S.; Dellen, B.; Torras, C.
local.citation.publicationNameHandbook of neural computation
local.citation.startingPage373
local.citation.endingPage382


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Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain