Object Detection for Crime Scene Evidence Analysis Using Deep Learning View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017-10-13

AUTHORS

Surajit Saikia , E. Fidalgo , Enrique Alegre , Laura Fernández-Robles

ABSTRACT

Object detection is the key module in most visual-based surveillance applications and security systems. In crime scene analysis, the images and videos play a significant role in providing visual documentation of a scene. It allows police officers to recreate a scene for later analysis by detecting objects related to a specific crime. However, due to the presence of a large volume of data, the task of detecting objects of interest is very tedious for law enforcement agencies. In this work, we present a Faster R-CNN (Region-based Convolutional Neural Network) based real-time system, which automatically detects objects which might be found in an indoor environment. To test the effectiveness of the proposed system, we applied it to a subset of ImageNet containing 12 object classes and Karina dataset. We achieved an average accuracy of 74.33%, and the mean time taken to detect objects per image was 0.12 s in Nvidia-TitanX GPU. More... »

PAGES

14-24

References to SciGraph publications

  • 2010-06. The Pascal Visual Object Classes (VOC) Challenge in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2015. Evaluation of Clustering Configurations for Object Retrieval Using SIFT Features in PROJECT MANAGEMENT AND ENGINEERING
  • 2014. Learning a Deep Convolutional Network for Image Super-Resolution in COMPUTER VISION – ECCV 2014
  • 2014. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition in COMPUTER VISION – ECCV 2014
  • 2014. Neural Codes for Image Retrieval in COMPUTER VISION – ECCV 2014
  • 2015-12. ImageNet Large Scale Visual Recognition Challenge in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2014. Microsoft COCO: Common Objects in Context in COMPUTER VISION – ECCV 2014
  • Book

    TITLE

    Image Analysis and Processing - ICIAP 2017

    ISBN

    978-3-319-68547-2
    978-3-319-68548-9

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-68548-9_2

    DOI

    http://dx.doi.org/10.1007/978-3-319-68548-9_2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1092199466


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "University of Leon", 
              "id": "https://www.grid.ac/institutes/grid.4807.b", 
              "name": [
                "Department of Electrical, Systems and Automation, University of Le\u00f3n, Le\u00f3n, Spain", 
                "INCIBE (Spanish National Cybersecurity Institute), Le\u00f3n, Spain"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Saikia", 
            "givenName": "Surajit", 
            "id": "sg:person.013366561121.26", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013366561121.26"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Leon", 
              "id": "https://www.grid.ac/institutes/grid.4807.b", 
              "name": [
                "Department of Electrical, Systems and Automation, University of Le\u00f3n, Le\u00f3n, Spain", 
                "INCIBE (Spanish National Cybersecurity Institute), Le\u00f3n, Spain"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Fidalgo", 
            "givenName": "E.", 
            "id": "sg:person.012664070017.21", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012664070017.21"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Leon", 
              "id": "https://www.grid.ac/institutes/grid.4807.b", 
              "name": [
                "Department of Electrical, Systems and Automation, University of Le\u00f3n, Le\u00f3n, Spain", 
                "INCIBE (Spanish National Cybersecurity Institute), Le\u00f3n, Spain"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Alegre", 
            "givenName": "Enrique", 
            "id": "sg:person.016266057305.75", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016266057305.75"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Leon", 
              "id": "https://www.grid.ac/institutes/grid.4807.b", 
              "name": [
                "INCIBE (Spanish National Cybersecurity Institute), Le\u00f3n, Spain", 
                "Department of Mechanical, Informatics and Aerospace Engineering, University of Le\u00f3n, Le\u00f3n, Spain"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Fern\u00e1ndez-Robles", 
            "givenName": "Laura", 
            "id": "sg:person.010415303037.45", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010415303037.45"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1145/1553374.1553453", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004476131"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11263-015-0816-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009767488", 
              "https://doi.org/10.1007/s11263-015-0816-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11263-009-0275-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014796149", 
              "https://doi.org/10.1007/s11263-009-0275-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11263-009-0275-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014796149", 
              "https://doi.org/10.1007/s11263-009-0275-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neucom.2016.02.045", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019654874"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-10593-2_13", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022837810", 
              "https://doi.org/10.1007/978-3-319-10593-2_13"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-10578-9_23", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030406568", 
              "https://doi.org/10.1007/978-3-319-10578-9_23"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-12754-5_21", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038314702", 
              "https://doi.org/10.1007/978-3-319-12754-5_21"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-10602-1_48", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045321436", 
              "https://doi.org/10.1007/978-3-319-10602-1_48"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2647868.2654889", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052031051"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-10590-1_38", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052242190", 
              "https://doi.org/10.1007/978-3-319-10590-1_38"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tpami.2016.2577031", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061745117"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/isco.2015.7282292", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093258111"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/iccsp.2016.7754524", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093406850"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2015.7298594", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094291017"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/iros.2016.7759733", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094304014"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/csnt.2015.68", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094571309"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2014.81", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094727707"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/biocas.2015.7348442", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094913952"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/dicta.2016.7797026", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095295523"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/iccv.2015.169", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095573598"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/crv.2015.32", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095804116"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2017-10-13", 
        "datePublishedReg": "2017-10-13", 
        "description": "Object detection is the key module in most visual-based surveillance applications and security systems. In crime scene analysis, the images and videos play a significant role in providing visual documentation of a scene. It allows police officers to recreate a scene for later analysis by detecting objects related to a specific crime. However, due to the presence of a large volume of data, the task of detecting objects of interest is very tedious for law enforcement agencies. In this work, we present a Faster R-CNN (Region-based Convolutional Neural Network) based real-time system, which automatically detects objects which might be found in an indoor environment. To test the effectiveness of the proposed system, we applied it to a subset of ImageNet containing 12 object classes and Karina dataset. We achieved an average accuracy of 74.33%, and the mean time taken to detect objects per image was 0.12 s in Nvidia-TitanX GPU.", 
        "editor": [
          {
            "familyName": "Battiato", 
            "givenName": "Sebastiano", 
            "type": "Person"
          }, 
          {
            "familyName": "Gallo", 
            "givenName": "Giovanni", 
            "type": "Person"
          }, 
          {
            "familyName": "Schettini", 
            "givenName": "Raimondo", 
            "type": "Person"
          }, 
          {
            "familyName": "Stanco", 
            "givenName": "Filippo", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/978-3-319-68548-9_2", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": {
          "isbn": [
            "978-3-319-68547-2", 
            "978-3-319-68548-9"
          ], 
          "name": "Image Analysis and Processing - ICIAP 2017", 
          "type": "Book"
        }, 
        "name": "Object Detection for Crime Scene Evidence Analysis Using Deep Learning", 
        "pagination": "14-24", 
        "productId": [
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/978-3-319-68548-9_2"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "ab5781dd1ddd47e15dec5fb27ba881738fbf47cf9a1cf009c3c9cb4cb2b8c140"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1092199466"
            ]
          }
        ], 
        "publisher": {
          "location": "Cham", 
          "name": "Springer International Publishing", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/978-3-319-68548-9_2", 
          "https://app.dimensions.ai/details/publication/pub.1092199466"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-16T04:59", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000325_0000000325/records_100778_00000000.jsonl", 
        "type": "Chapter", 
        "url": "https://link.springer.com/10.1007%2F978-3-319-68548-9_2"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-68548-9_2'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-68548-9_2'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-68548-9_2'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-68548-9_2'


     

    This table displays all metadata directly associated to this object as RDF triples.

    173 TRIPLES      23 PREDICATES      47 URIs      19 LITERALS      8 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/978-3-319-68548-9_2 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N2fd3d20a5c2249c0bac7cfa55ec49edd
    4 schema:citation sg:pub.10.1007/978-3-319-10578-9_23
    5 sg:pub.10.1007/978-3-319-10590-1_38
    6 sg:pub.10.1007/978-3-319-10593-2_13
    7 sg:pub.10.1007/978-3-319-10602-1_48
    8 sg:pub.10.1007/978-3-319-12754-5_21
    9 sg:pub.10.1007/s11263-009-0275-4
    10 sg:pub.10.1007/s11263-015-0816-y
    11 https://doi.org/10.1016/j.neucom.2016.02.045
    12 https://doi.org/10.1109/biocas.2015.7348442
    13 https://doi.org/10.1109/crv.2015.32
    14 https://doi.org/10.1109/csnt.2015.68
    15 https://doi.org/10.1109/cvpr.2014.81
    16 https://doi.org/10.1109/cvpr.2015.7298594
    17 https://doi.org/10.1109/dicta.2016.7797026
    18 https://doi.org/10.1109/iccsp.2016.7754524
    19 https://doi.org/10.1109/iccv.2015.169
    20 https://doi.org/10.1109/iros.2016.7759733
    21 https://doi.org/10.1109/isco.2015.7282292
    22 https://doi.org/10.1109/tpami.2016.2577031
    23 https://doi.org/10.1145/1553374.1553453
    24 https://doi.org/10.1145/2647868.2654889
    25 schema:datePublished 2017-10-13
    26 schema:datePublishedReg 2017-10-13
    27 schema:description Object detection is the key module in most visual-based surveillance applications and security systems. In crime scene analysis, the images and videos play a significant role in providing visual documentation of a scene. It allows police officers to recreate a scene for later analysis by detecting objects related to a specific crime. However, due to the presence of a large volume of data, the task of detecting objects of interest is very tedious for law enforcement agencies. In this work, we present a Faster R-CNN (Region-based Convolutional Neural Network) based real-time system, which automatically detects objects which might be found in an indoor environment. To test the effectiveness of the proposed system, we applied it to a subset of ImageNet containing 12 object classes and Karina dataset. We achieved an average accuracy of 74.33%, and the mean time taken to detect objects per image was 0.12 s in Nvidia-TitanX GPU.
    28 schema:editor N99d74617ecfc4561954ebbfb8b143cdb
    29 schema:genre chapter
    30 schema:inLanguage en
    31 schema:isAccessibleForFree false
    32 schema:isPartOf N82d2f8f43c014383afdc50d46252a127
    33 schema:name Object Detection for Crime Scene Evidence Analysis Using Deep Learning
    34 schema:pagination 14-24
    35 schema:productId N22f50207d08c4e15b56ef99cbe46d458
    36 N67f919979d8f410eaa9ea3409f098049
    37 Naa866021bf864dc1853881981e889d58
    38 schema:publisher N3b87aab26644415db7591f50328d3a78
    39 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092199466
    40 https://doi.org/10.1007/978-3-319-68548-9_2
    41 schema:sdDatePublished 2019-04-16T04:59
    42 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    43 schema:sdPublisher N7ac588cb731c49a39c13673035e69a1d
    44 schema:url https://link.springer.com/10.1007%2F978-3-319-68548-9_2
    45 sgo:license sg:explorer/license/
    46 sgo:sdDataset chapters
    47 rdf:type schema:Chapter
    48 N09b87c79a030489ab035dc3cd29d1b93 rdf:first sg:person.010415303037.45
    49 rdf:rest rdf:nil
    50 N22f50207d08c4e15b56ef99cbe46d458 schema:name readcube_id
    51 schema:value ab5781dd1ddd47e15dec5fb27ba881738fbf47cf9a1cf009c3c9cb4cb2b8c140
    52 rdf:type schema:PropertyValue
    53 N2fd3d20a5c2249c0bac7cfa55ec49edd rdf:first sg:person.013366561121.26
    54 rdf:rest Nc92baca7703645aaa970a91ca6b0fd4a
    55 N3b87aab26644415db7591f50328d3a78 schema:location Cham
    56 schema:name Springer International Publishing
    57 rdf:type schema:Organisation
    58 N445877a868504541909171e788462578 schema:familyName Gallo
    59 schema:givenName Giovanni
    60 rdf:type schema:Person
    61 N4bfbc81fb1c441539d47a13695b44b28 rdf:first N5246526803504a7e9818cda78f8e97fa
    62 rdf:rest rdf:nil
    63 N5246526803504a7e9818cda78f8e97fa schema:familyName Stanco
    64 schema:givenName Filippo
    65 rdf:type schema:Person
    66 N67f919979d8f410eaa9ea3409f098049 schema:name dimensions_id
    67 schema:value pub.1092199466
    68 rdf:type schema:PropertyValue
    69 N71a826010c064da29cfb369de93b7a20 rdf:first sg:person.016266057305.75
    70 rdf:rest N09b87c79a030489ab035dc3cd29d1b93
    71 N78dc56c4787a427a8a7e0b51ae22a747 schema:familyName Schettini
    72 schema:givenName Raimondo
    73 rdf:type schema:Person
    74 N7ac588cb731c49a39c13673035e69a1d schema:name Springer Nature - SN SciGraph project
    75 rdf:type schema:Organization
    76 N82d2f8f43c014383afdc50d46252a127 schema:isbn 978-3-319-68547-2
    77 978-3-319-68548-9
    78 schema:name Image Analysis and Processing - ICIAP 2017
    79 rdf:type schema:Book
    80 N92a64d54037a467e8d18fe42c500435b schema:familyName Battiato
    81 schema:givenName Sebastiano
    82 rdf:type schema:Person
    83 N99d74617ecfc4561954ebbfb8b143cdb rdf:first N92a64d54037a467e8d18fe42c500435b
    84 rdf:rest Nc3d7ebefa104471c8d0ae07f082d3618
    85 Na5b685858f7f4e3181a57ba8c48c7c31 rdf:first N78dc56c4787a427a8a7e0b51ae22a747
    86 rdf:rest N4bfbc81fb1c441539d47a13695b44b28
    87 Naa866021bf864dc1853881981e889d58 schema:name doi
    88 schema:value 10.1007/978-3-319-68548-9_2
    89 rdf:type schema:PropertyValue
    90 Nc3d7ebefa104471c8d0ae07f082d3618 rdf:first N445877a868504541909171e788462578
    91 rdf:rest Na5b685858f7f4e3181a57ba8c48c7c31
    92 Nc92baca7703645aaa970a91ca6b0fd4a rdf:first sg:person.012664070017.21
    93 rdf:rest N71a826010c064da29cfb369de93b7a20
    94 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    95 schema:name Information and Computing Sciences
    96 rdf:type schema:DefinedTerm
    97 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    98 schema:name Artificial Intelligence and Image Processing
    99 rdf:type schema:DefinedTerm
    100 sg:person.010415303037.45 schema:affiliation https://www.grid.ac/institutes/grid.4807.b
    101 schema:familyName Fernández-Robles
    102 schema:givenName Laura
    103 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010415303037.45
    104 rdf:type schema:Person
    105 sg:person.012664070017.21 schema:affiliation https://www.grid.ac/institutes/grid.4807.b
    106 schema:familyName Fidalgo
    107 schema:givenName E.
    108 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012664070017.21
    109 rdf:type schema:Person
    110 sg:person.013366561121.26 schema:affiliation https://www.grid.ac/institutes/grid.4807.b
    111 schema:familyName Saikia
    112 schema:givenName Surajit
    113 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013366561121.26
    114 rdf:type schema:Person
    115 sg:person.016266057305.75 schema:affiliation https://www.grid.ac/institutes/grid.4807.b
    116 schema:familyName Alegre
    117 schema:givenName Enrique
    118 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016266057305.75
    119 rdf:type schema:Person
    120 sg:pub.10.1007/978-3-319-10578-9_23 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030406568
    121 https://doi.org/10.1007/978-3-319-10578-9_23
    122 rdf:type schema:CreativeWork
    123 sg:pub.10.1007/978-3-319-10590-1_38 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052242190
    124 https://doi.org/10.1007/978-3-319-10590-1_38
    125 rdf:type schema:CreativeWork
    126 sg:pub.10.1007/978-3-319-10593-2_13 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022837810
    127 https://doi.org/10.1007/978-3-319-10593-2_13
    128 rdf:type schema:CreativeWork
    129 sg:pub.10.1007/978-3-319-10602-1_48 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045321436
    130 https://doi.org/10.1007/978-3-319-10602-1_48
    131 rdf:type schema:CreativeWork
    132 sg:pub.10.1007/978-3-319-12754-5_21 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038314702
    133 https://doi.org/10.1007/978-3-319-12754-5_21
    134 rdf:type schema:CreativeWork
    135 sg:pub.10.1007/s11263-009-0275-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014796149
    136 https://doi.org/10.1007/s11263-009-0275-4
    137 rdf:type schema:CreativeWork
    138 sg:pub.10.1007/s11263-015-0816-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1009767488
    139 https://doi.org/10.1007/s11263-015-0816-y
    140 rdf:type schema:CreativeWork
    141 https://doi.org/10.1016/j.neucom.2016.02.045 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019654874
    142 rdf:type schema:CreativeWork
    143 https://doi.org/10.1109/biocas.2015.7348442 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094913952
    144 rdf:type schema:CreativeWork
    145 https://doi.org/10.1109/crv.2015.32 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095804116
    146 rdf:type schema:CreativeWork
    147 https://doi.org/10.1109/csnt.2015.68 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094571309
    148 rdf:type schema:CreativeWork
    149 https://doi.org/10.1109/cvpr.2014.81 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094727707
    150 rdf:type schema:CreativeWork
    151 https://doi.org/10.1109/cvpr.2015.7298594 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094291017
    152 rdf:type schema:CreativeWork
    153 https://doi.org/10.1109/dicta.2016.7797026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095295523
    154 rdf:type schema:CreativeWork
    155 https://doi.org/10.1109/iccsp.2016.7754524 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093406850
    156 rdf:type schema:CreativeWork
    157 https://doi.org/10.1109/iccv.2015.169 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095573598
    158 rdf:type schema:CreativeWork
    159 https://doi.org/10.1109/iros.2016.7759733 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094304014
    160 rdf:type schema:CreativeWork
    161 https://doi.org/10.1109/isco.2015.7282292 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093258111
    162 rdf:type schema:CreativeWork
    163 https://doi.org/10.1109/tpami.2016.2577031 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061745117
    164 rdf:type schema:CreativeWork
    165 https://doi.org/10.1145/1553374.1553453 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004476131
    166 rdf:type schema:CreativeWork
    167 https://doi.org/10.1145/2647868.2654889 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052031051
    168 rdf:type schema:CreativeWork
    169 https://www.grid.ac/institutes/grid.4807.b schema:alternateName University of Leon
    170 schema:name Department of Electrical, Systems and Automation, University of León, León, Spain
    171 Department of Mechanical, Informatics and Aerospace Engineering, University of León, León, Spain
    172 INCIBE (Spanish National Cybersecurity Institute), León, Spain
    173 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...