Motion of Oriented Magnitudes Patterns for Human Action Recognition View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Hai-Hong Phan , Ngoc-Son Vu , Vu-Lam Nguyen , Mathias Quoy

ABSTRACT

In this paper, we present a novel descriptor for human action recognition, called Motion of Oriented Magnitudes Patterns (MOMP), which considers the relationships between the local gradient distributions of neighboring patches coming from successive frames in video. The proposed descriptor also characterizes the information changing across different orientations, is therefore very discriminative and robust. The major advantages of MOMP are its very fast computation time and simple implementation. Subsequently, our features are combined with an effective coding scheme VLAD (Vector of locally aggregated descriptors) in the feature representation step, and a SVM (Support Vector Machine) classifier in order to better represent and classify the actions. By experimenting on several common benchmarks, we obtain the state-of-the-art results on the KTH dataset as well as the performance comparable to the literature on the UCF Sport dataset. More... »

PAGES

168-177

References to SciGraph publications

  • 2004-11. Distinctive Image Features from Scale-Invariant Keypoints in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2010. Convolutional Learning of Spatio-temporal Features in COMPUTER VISION – ECCV 2010
  • 2010. Face Recognition with Patterns of Oriented Edge Magnitudes in COMPUTER VISION – ECCV 2010
  • 2006. Human Detection Using Oriented Histograms of Flow and Appearance in COMPUTER VISION – ECCV 2006
  • 2013-05. Dense Trajectories and Motion Boundary Descriptors for Action Recognition in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2009. Group Action Recognition Using Space-Time Interest Points in ADVANCES IN VISUAL COMPUTING
  • 2012. Motion Interchange Patterns for Action Recognition in Unconstrained Videos in COMPUTER VISION – ECCV 2012
  • 2005-09. On Space-Time Interest Points in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • Book

    TITLE

    Advances in Visual Computing

    ISBN

    978-3-319-50831-3
    978-3-319-50832-0

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-50832-0_17

    DOI

    http://dx.doi.org/10.1007/978-3-319-50832-0_17

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1045390213


    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": "Cergy-Pontoise University", 
              "id": "https://www.grid.ac/institutes/grid.7901.f", 
              "name": [
                "ETIS - ENSEA/Universite de Cergy-Pontoise, CNRS UMR 8051 Cergy France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Phan", 
            "givenName": "Hai-Hong", 
            "id": "sg:person.016207726656.39", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016207726656.39"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Cergy-Pontoise University", 
              "id": "https://www.grid.ac/institutes/grid.7901.f", 
              "name": [
                "ETIS - ENSEA/Universite de Cergy-Pontoise, CNRS UMR 8051 Cergy France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Vu", 
            "givenName": "Ngoc-Son", 
            "id": "sg:person.014676147032.78", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014676147032.78"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Cergy-Pontoise University", 
              "id": "https://www.grid.ac/institutes/grid.7901.f", 
              "name": [
                "ETIS - ENSEA/Universite de Cergy-Pontoise, CNRS UMR 8051 Cergy France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Nguyen", 
            "givenName": "Vu-Lam", 
            "id": "sg:person.010302236056.19", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010302236056.19"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Cergy-Pontoise University", 
              "id": "https://www.grid.ac/institutes/grid.7901.f", 
              "name": [
                "ETIS - ENSEA/Universite de Cergy-Pontoise, CNRS UMR 8051 Cergy France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Quoy", 
            "givenName": "Mathias", 
            "id": "sg:person.010556353111.37", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010556353111.37"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/978-3-642-15567-3_11", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005333996", 
              "https://doi.org/10.1007/978-3-642-15567-3_11"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-15567-3_11", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005333996", 
              "https://doi.org/10.1007/978-3-642-15567-3_11"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11263-012-0594-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011251901", 
              "https://doi.org/10.1007/s11263-012-0594-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1961189.1961199", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013637525"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-10520-3_72", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019782346", 
              "https://doi.org/10.1007/978-3-642-10520-3_72"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-10520-3_72", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019782346", 
              "https://doi.org/10.1007/978-3-642-10520-3_72"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-15549-9_23", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020819783", 
              "https://doi.org/10.1007/978-3-642-15549-9_23"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-15549-9_23", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020819783", 
              "https://doi.org/10.1007/978-3-642-15549-9_23"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/11744047_33", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022098216", 
              "https://doi.org/10.1007/11744047_33"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/11744047_33", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022098216", 
              "https://doi.org/10.1007/11744047_33"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11263-005-1838-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031382793", 
              "https://doi.org/10.1007/s11263-005-1838-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11263-005-1838-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031382793", 
              "https://doi.org/10.1007/s11263-005-1838-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11263-005-1838-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031382793", 
              "https://doi.org/10.1007/s11263-005-1838-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/b:visi.0000029664.99615.94", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052687286", 
              "https://doi.org/10.1023/b:visi.0000029664.99615.94"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-33783-3_19", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052793050", 
              "https://doi.org/10.1007/978-3-642-33783-3_19"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tcyb.2015.2399172", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061579933"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tifs.2012.2224866", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061630072"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tpami.2012.59", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061744395"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/iccv.2013.441", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093254042"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2014.332", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093838228"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2013.330", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093894837"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2013.330", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093894837"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2011.5995496", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093974623"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2008.4587727", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094359705"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2012.6247806", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094572393"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/iccv.2015.510", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094614899"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icpr.2004.1334462", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094617043"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2010.5539881", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094630240"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2008.4587756", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094776319"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/iccv.2009.5459201", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095371836"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2007.383266", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095559903"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2010.5540039", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095738093"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.5244/c.23.124", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1099325624"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2016", 
        "datePublishedReg": "2016-01-01", 
        "description": "In this paper, we present a novel descriptor for human action recognition, called Motion of Oriented Magnitudes Patterns (MOMP), which considers the relationships between the local gradient distributions of neighboring patches coming from successive frames in video. The proposed descriptor also characterizes the information changing across different orientations, is therefore very discriminative and robust. The major advantages of MOMP are its very fast computation time and simple implementation. Subsequently, our features are combined with an effective coding scheme VLAD (Vector of locally aggregated descriptors) in the feature representation step, and a SVM (Support Vector Machine) classifier in order to better represent and classify the actions. By experimenting on several common benchmarks, we obtain the state-of-the-art results on the KTH dataset as well as the performance comparable to the literature on the UCF Sport dataset.", 
        "editor": [
          {
            "familyName": "Bebis", 
            "givenName": "George", 
            "type": "Person"
          }, 
          {
            "familyName": "Boyle", 
            "givenName": "Richard", 
            "type": "Person"
          }, 
          {
            "familyName": "Parvin", 
            "givenName": "Bahram", 
            "type": "Person"
          }, 
          {
            "familyName": "Koracin", 
            "givenName": "Darko", 
            "type": "Person"
          }, 
          {
            "familyName": "Porikli", 
            "givenName": "Fatih", 
            "type": "Person"
          }, 
          {
            "familyName": "Skaff", 
            "givenName": "Sandra", 
            "type": "Person"
          }, 
          {
            "familyName": "Entezari", 
            "givenName": "Alireza", 
            "type": "Person"
          }, 
          {
            "familyName": "Min", 
            "givenName": "Jianyuan", 
            "type": "Person"
          }, 
          {
            "familyName": "Iwai", 
            "givenName": "Daisuke", 
            "type": "Person"
          }, 
          {
            "familyName": "Sadagic", 
            "givenName": "Amela", 
            "type": "Person"
          }, 
          {
            "familyName": "Scheidegger", 
            "givenName": "Carlos", 
            "type": "Person"
          }, 
          {
            "familyName": "Isenberg", 
            "givenName": "Tobias", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/978-3-319-50832-0_17", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": {
          "isbn": [
            "978-3-319-50831-3", 
            "978-3-319-50832-0"
          ], 
          "name": "Advances in Visual Computing", 
          "type": "Book"
        }, 
        "name": "Motion of Oriented Magnitudes Patterns for Human Action Recognition", 
        "pagination": "168-177", 
        "productId": [
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/978-3-319-50832-0_17"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "d836913867c16a01616e7f465fe44878843c8f18ee58912c3e45fe4f21841de1"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1045390213"
            ]
          }
        ], 
        "publisher": {
          "location": "Cham", 
          "name": "Springer International Publishing", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/978-3-319-50832-0_17", 
          "https://app.dimensions.ai/details/publication/pub.1045390213"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-15T17:16", 
        "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/v90/0000000001_0000000264/records_8678_00000271.jsonl", 
        "type": "Chapter", 
        "url": "http://link.springer.com/10.1007/978-3-319-50832-0_17"
      }
    ]
     

    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.10.1007/978-3-319-50832-0_17'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-50832-0_17'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-50832-0_17'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-50832-0_17'


     

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

    227 TRIPLES      23 PREDICATES      53 URIs      20 LITERALS      8 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/978-3-319-50832-0_17 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N6ca5130887e04d8296d7832cdeefe0c0
    4 schema:citation sg:pub.10.1007/11744047_33
    5 sg:pub.10.1007/978-3-642-10520-3_72
    6 sg:pub.10.1007/978-3-642-15549-9_23
    7 sg:pub.10.1007/978-3-642-15567-3_11
    8 sg:pub.10.1007/978-3-642-33783-3_19
    9 sg:pub.10.1007/s11263-005-1838-7
    10 sg:pub.10.1007/s11263-012-0594-8
    11 sg:pub.10.1023/b:visi.0000029664.99615.94
    12 https://doi.org/10.1109/cvpr.2007.383266
    13 https://doi.org/10.1109/cvpr.2008.4587727
    14 https://doi.org/10.1109/cvpr.2008.4587756
    15 https://doi.org/10.1109/cvpr.2010.5539881
    16 https://doi.org/10.1109/cvpr.2010.5540039
    17 https://doi.org/10.1109/cvpr.2011.5995496
    18 https://doi.org/10.1109/cvpr.2012.6247806
    19 https://doi.org/10.1109/cvpr.2013.330
    20 https://doi.org/10.1109/cvpr.2014.332
    21 https://doi.org/10.1109/iccv.2009.5459201
    22 https://doi.org/10.1109/iccv.2013.441
    23 https://doi.org/10.1109/iccv.2015.510
    24 https://doi.org/10.1109/icpr.2004.1334462
    25 https://doi.org/10.1109/tcyb.2015.2399172
    26 https://doi.org/10.1109/tifs.2012.2224866
    27 https://doi.org/10.1109/tpami.2012.59
    28 https://doi.org/10.1145/1961189.1961199
    29 https://doi.org/10.5244/c.23.124
    30 schema:datePublished 2016
    31 schema:datePublishedReg 2016-01-01
    32 schema:description In this paper, we present a novel descriptor for human action recognition, called Motion of Oriented Magnitudes Patterns (MOMP), which considers the relationships between the local gradient distributions of neighboring patches coming from successive frames in video. The proposed descriptor also characterizes the information changing across different orientations, is therefore very discriminative and robust. The major advantages of MOMP are its very fast computation time and simple implementation. Subsequently, our features are combined with an effective coding scheme VLAD (Vector of locally aggregated descriptors) in the feature representation step, and a SVM (Support Vector Machine) classifier in order to better represent and classify the actions. By experimenting on several common benchmarks, we obtain the state-of-the-art results on the KTH dataset as well as the performance comparable to the literature on the UCF Sport dataset.
    33 schema:editor N087595d2f783485b95ac781d50aee5ad
    34 schema:genre chapter
    35 schema:inLanguage en
    36 schema:isAccessibleForFree false
    37 schema:isPartOf N09f9a015f33c4144b2832d1b15b26a9c
    38 schema:name Motion of Oriented Magnitudes Patterns for Human Action Recognition
    39 schema:pagination 168-177
    40 schema:productId N4c96c7c5dcfd4f44ba6fca627e38bac9
    41 Nc79d6e76133b4fc9a845678b2cfe4a2e
    42 Nc899a4e2c22e43d189b46c1b48b4b648
    43 schema:publisher Ncb75596a17414bc2b1f563db5ecf5665
    44 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045390213
    45 https://doi.org/10.1007/978-3-319-50832-0_17
    46 schema:sdDatePublished 2019-04-15T17:16
    47 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    48 schema:sdPublisher N2cc8550b87d74c65b731bad00ceb6c5c
    49 schema:url http://link.springer.com/10.1007/978-3-319-50832-0_17
    50 sgo:license sg:explorer/license/
    51 sgo:sdDataset chapters
    52 rdf:type schema:Chapter
    53 N062af5bdf3b34e1a900bbfab5c57ac83 schema:familyName Porikli
    54 schema:givenName Fatih
    55 rdf:type schema:Person
    56 N06cb7107ae164e978b0b3c64011e3118 rdf:first Na2c6c2a276db4e4e9241f8bc348f6ad5
    57 rdf:rest Nc37a579d9333490fb6b01a161ce8460c
    58 N087595d2f783485b95ac781d50aee5ad rdf:first Ne99826511343484c934f5c4608674c3a
    59 rdf:rest N234bb411e2244a689cae1b6fa95d4717
    60 N0937fe2ba88d4ed1a892f2421364002a rdf:first Ne29876bc93c04188805260bb71cfee3a
    61 rdf:rest N8a7ef46c55f148e19ed22cac873fea21
    62 N09f9a015f33c4144b2832d1b15b26a9c schema:isbn 978-3-319-50831-3
    63 978-3-319-50832-0
    64 schema:name Advances in Visual Computing
    65 rdf:type schema:Book
    66 N10fef247fae342aca023e1a5815cad85 schema:familyName Koracin
    67 schema:givenName Darko
    68 rdf:type schema:Person
    69 N234bb411e2244a689cae1b6fa95d4717 rdf:first N796231ea5506466c94f0aa3caff765ec
    70 rdf:rest N06cb7107ae164e978b0b3c64011e3118
    71 N2b289397a6024fd9bb71bbe6bad4893e rdf:first sg:person.014676147032.78
    72 rdf:rest N9d67b4ca22d54ac680582bec641bdfa7
    73 N2cc8550b87d74c65b731bad00ceb6c5c schema:name Springer Nature - SN SciGraph project
    74 rdf:type schema:Organization
    75 N45d062573c5d4a2f884fcb96a6312a24 schema:familyName Min
    76 schema:givenName Jianyuan
    77 rdf:type schema:Person
    78 N4c96c7c5dcfd4f44ba6fca627e38bac9 schema:name dimensions_id
    79 schema:value pub.1045390213
    80 rdf:type schema:PropertyValue
    81 N5f82980105e349f3935ca0964f7cde53 rdf:first N45d062573c5d4a2f884fcb96a6312a24
    82 rdf:rest N7ca4406ed5764620a52cfcd24f73d9fb
    83 N6ca5130887e04d8296d7832cdeefe0c0 rdf:first sg:person.016207726656.39
    84 rdf:rest N2b289397a6024fd9bb71bbe6bad4893e
    85 N796231ea5506466c94f0aa3caff765ec schema:familyName Boyle
    86 schema:givenName Richard
    87 rdf:type schema:Person
    88 N7ca4406ed5764620a52cfcd24f73d9fb rdf:first Ndf7d98a1aabd4668978e602c972ec8bc
    89 rdf:rest Ne35dd87a23754f4eb4633b9017222266
    90 N82c961b8b2d14a84b97e9f1cb5425dbf schema:familyName Entezari
    91 schema:givenName Alireza
    92 rdf:type schema:Person
    93 N866b386587814dd48a4639fb68f2e566 schema:familyName Isenberg
    94 schema:givenName Tobias
    95 rdf:type schema:Person
    96 N8a7ef46c55f148e19ed22cac873fea21 rdf:first N866b386587814dd48a4639fb68f2e566
    97 rdf:rest rdf:nil
    98 N91482a1d9ef14a1d8d0dfcf2f18495d2 rdf:first N82c961b8b2d14a84b97e9f1cb5425dbf
    99 rdf:rest N5f82980105e349f3935ca0964f7cde53
    100 N9d67b4ca22d54ac680582bec641bdfa7 rdf:first sg:person.010302236056.19
    101 rdf:rest Naa0324e0e0ae48f59f87703daebbcfb5
    102 Na2c6c2a276db4e4e9241f8bc348f6ad5 schema:familyName Parvin
    103 schema:givenName Bahram
    104 rdf:type schema:Person
    105 Na617e994265c4cef8e9ede05d92d37ad schema:familyName Skaff
    106 schema:givenName Sandra
    107 rdf:type schema:Person
    108 Na6645e5298a54cacb94c2220f7b273c9 schema:familyName Sadagic
    109 schema:givenName Amela
    110 rdf:type schema:Person
    111 Naa0324e0e0ae48f59f87703daebbcfb5 rdf:first sg:person.010556353111.37
    112 rdf:rest rdf:nil
    113 Nc37a579d9333490fb6b01a161ce8460c rdf:first N10fef247fae342aca023e1a5815cad85
    114 rdf:rest Nfdd4641419ff47a7bbce4b7d1dc2190f
    115 Nc79d6e76133b4fc9a845678b2cfe4a2e schema:name doi
    116 schema:value 10.1007/978-3-319-50832-0_17
    117 rdf:type schema:PropertyValue
    118 Nc899a4e2c22e43d189b46c1b48b4b648 schema:name readcube_id
    119 schema:value d836913867c16a01616e7f465fe44878843c8f18ee58912c3e45fe4f21841de1
    120 rdf:type schema:PropertyValue
    121 Ncb75596a17414bc2b1f563db5ecf5665 schema:location Cham
    122 schema:name Springer International Publishing
    123 rdf:type schema:Organisation
    124 Ndf7d98a1aabd4668978e602c972ec8bc schema:familyName Iwai
    125 schema:givenName Daisuke
    126 rdf:type schema:Person
    127 Ne29876bc93c04188805260bb71cfee3a schema:familyName Scheidegger
    128 schema:givenName Carlos
    129 rdf:type schema:Person
    130 Ne35dd87a23754f4eb4633b9017222266 rdf:first Na6645e5298a54cacb94c2220f7b273c9
    131 rdf:rest N0937fe2ba88d4ed1a892f2421364002a
    132 Ne99826511343484c934f5c4608674c3a schema:familyName Bebis
    133 schema:givenName George
    134 rdf:type schema:Person
    135 Nfc1b47e40ce449ee9b0bb8360e1ad503 rdf:first Na617e994265c4cef8e9ede05d92d37ad
    136 rdf:rest N91482a1d9ef14a1d8d0dfcf2f18495d2
    137 Nfdd4641419ff47a7bbce4b7d1dc2190f rdf:first N062af5bdf3b34e1a900bbfab5c57ac83
    138 rdf:rest Nfc1b47e40ce449ee9b0bb8360e1ad503
    139 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    140 schema:name Information and Computing Sciences
    141 rdf:type schema:DefinedTerm
    142 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    143 schema:name Artificial Intelligence and Image Processing
    144 rdf:type schema:DefinedTerm
    145 sg:person.010302236056.19 schema:affiliation https://www.grid.ac/institutes/grid.7901.f
    146 schema:familyName Nguyen
    147 schema:givenName Vu-Lam
    148 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010302236056.19
    149 rdf:type schema:Person
    150 sg:person.010556353111.37 schema:affiliation https://www.grid.ac/institutes/grid.7901.f
    151 schema:familyName Quoy
    152 schema:givenName Mathias
    153 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010556353111.37
    154 rdf:type schema:Person
    155 sg:person.014676147032.78 schema:affiliation https://www.grid.ac/institutes/grid.7901.f
    156 schema:familyName Vu
    157 schema:givenName Ngoc-Son
    158 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014676147032.78
    159 rdf:type schema:Person
    160 sg:person.016207726656.39 schema:affiliation https://www.grid.ac/institutes/grid.7901.f
    161 schema:familyName Phan
    162 schema:givenName Hai-Hong
    163 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016207726656.39
    164 rdf:type schema:Person
    165 sg:pub.10.1007/11744047_33 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022098216
    166 https://doi.org/10.1007/11744047_33
    167 rdf:type schema:CreativeWork
    168 sg:pub.10.1007/978-3-642-10520-3_72 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019782346
    169 https://doi.org/10.1007/978-3-642-10520-3_72
    170 rdf:type schema:CreativeWork
    171 sg:pub.10.1007/978-3-642-15549-9_23 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020819783
    172 https://doi.org/10.1007/978-3-642-15549-9_23
    173 rdf:type schema:CreativeWork
    174 sg:pub.10.1007/978-3-642-15567-3_11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005333996
    175 https://doi.org/10.1007/978-3-642-15567-3_11
    176 rdf:type schema:CreativeWork
    177 sg:pub.10.1007/978-3-642-33783-3_19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052793050
    178 https://doi.org/10.1007/978-3-642-33783-3_19
    179 rdf:type schema:CreativeWork
    180 sg:pub.10.1007/s11263-005-1838-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031382793
    181 https://doi.org/10.1007/s11263-005-1838-7
    182 rdf:type schema:CreativeWork
    183 sg:pub.10.1007/s11263-012-0594-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011251901
    184 https://doi.org/10.1007/s11263-012-0594-8
    185 rdf:type schema:CreativeWork
    186 sg:pub.10.1023/b:visi.0000029664.99615.94 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052687286
    187 https://doi.org/10.1023/b:visi.0000029664.99615.94
    188 rdf:type schema:CreativeWork
    189 https://doi.org/10.1109/cvpr.2007.383266 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095559903
    190 rdf:type schema:CreativeWork
    191 https://doi.org/10.1109/cvpr.2008.4587727 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094359705
    192 rdf:type schema:CreativeWork
    193 https://doi.org/10.1109/cvpr.2008.4587756 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094776319
    194 rdf:type schema:CreativeWork
    195 https://doi.org/10.1109/cvpr.2010.5539881 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094630240
    196 rdf:type schema:CreativeWork
    197 https://doi.org/10.1109/cvpr.2010.5540039 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095738093
    198 rdf:type schema:CreativeWork
    199 https://doi.org/10.1109/cvpr.2011.5995496 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093974623
    200 rdf:type schema:CreativeWork
    201 https://doi.org/10.1109/cvpr.2012.6247806 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094572393
    202 rdf:type schema:CreativeWork
    203 https://doi.org/10.1109/cvpr.2013.330 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093894837
    204 rdf:type schema:CreativeWork
    205 https://doi.org/10.1109/cvpr.2014.332 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093838228
    206 rdf:type schema:CreativeWork
    207 https://doi.org/10.1109/iccv.2009.5459201 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095371836
    208 rdf:type schema:CreativeWork
    209 https://doi.org/10.1109/iccv.2013.441 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093254042
    210 rdf:type schema:CreativeWork
    211 https://doi.org/10.1109/iccv.2015.510 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094614899
    212 rdf:type schema:CreativeWork
    213 https://doi.org/10.1109/icpr.2004.1334462 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094617043
    214 rdf:type schema:CreativeWork
    215 https://doi.org/10.1109/tcyb.2015.2399172 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061579933
    216 rdf:type schema:CreativeWork
    217 https://doi.org/10.1109/tifs.2012.2224866 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061630072
    218 rdf:type schema:CreativeWork
    219 https://doi.org/10.1109/tpami.2012.59 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061744395
    220 rdf:type schema:CreativeWork
    221 https://doi.org/10.1145/1961189.1961199 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013637525
    222 rdf:type schema:CreativeWork
    223 https://doi.org/10.5244/c.23.124 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099325624
    224 rdf:type schema:CreativeWork
    225 https://www.grid.ac/institutes/grid.7901.f schema:alternateName Cergy-Pontoise University
    226 schema:name ETIS - ENSEA/Universite de Cergy-Pontoise, CNRS UMR 8051 Cergy France
    227 rdf:type schema:Organization
     




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


    ...