Multiregion Level Set Tracking with Transformation Invariant Shape Priors View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2006

AUTHORS

Michael Fussenegger , Rachid Deriche , Axel Pinz

ABSTRACT

Tracking of regions and object boundaries in an image sequence is a well studied problem in image processing and computer vision. So far, numerous approaches tracking different features of the objects (contours, regions or points of interest) have been presented. Most of these approaches have problems with robustness. Typical reasons are noisy images, objects with identical features or partial occlusions of the tracked features. In this paper we propose a novel level set based tracking approach, that allows robust tracking on noisy images. Our framework is able to track multiple regions in an image sequence, where a level set function is assigned to every region. For already known or learned objects, transformation invariant shape priors can be added to ensure a robust tracking even under partial occlusions. Furthermore, we introduce a simple decision function to maintain the desired topology for multiple regions. Experimental results demonstrate the method for arbitrary numbers of shape priors. The approach can even handle full occlusions and objects which are temporarily hidden in containers. More... »

PAGES

674-683

References to SciGraph publications

  • 2004. Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation in COMPUTER VISION - ECCV 2004
  • 2002-12. Using Prior Shapes in Geometric Active Contours in a Variational Framework in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2004. Unlevel-Sets: Geometry and Prior-Based Segmentation in COMPUTER VISION - ECCV 2004
  • 1990. Tracking line segments in COMPUTER VISION — ECCV 90
  • 2003-06-24. Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling in SCALE SPACE METHODS IN COMPUTER VISION
  • 2002-04-29. Shape Priors for Level Set Representations in COMPUTER VISION — ECCV 2002
  • 1981. Multifluid incompressible flows by a finite element method in SEVENTH INTERNATIONAL CONFERENCE ON NUMERICAL METHODS IN FLUID DYNAMICS
  • 1980. A finite element method for the simulation of a Rayleigh-Taylor instability in APPROXIMATION METHODS FOR NAVIER-STOKES PROBLEMS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/11612032_68

    DOI

    http://dx.doi.org/10.1007/11612032_68

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1045798491


    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": "Graz University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.410413.3", 
              "name": [
                "Institute of Electrical Measurement and Measurement Signal Processing, Graz University of Technology, Schie\u00dfstattgasse 14b, 8010, Graz, Austria"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Fussenegger", 
            "givenName": "Michael", 
            "id": "sg:person.01330551375.09", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01330551375.09"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "French Institute for Research in Computer Science and Automation", 
              "id": "https://www.grid.ac/institutes/grid.5328.c", 
              "name": [
                "INRIA, BP 93, 2004 route des Lucioles, 06902, Sophia Antipolis, France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Deriche", 
            "givenName": "Rachid", 
            "id": "sg:person.01010760563.28", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01010760563.28"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Graz University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.410413.3", 
              "name": [
                "Institute of Electrical Measurement and Measurement Signal Processing, Graz University of Technology, Schie\u00dfstattgasse 14b, 8010, Graz, Austria"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Pinz", 
            "givenName": "Axel", 
            "id": "sg:person.012033065653.49", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012033065653.49"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/bfb0014872", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001567098", 
              "https://doi.org/10.1007/bfb0014872"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-44935-3_27", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006695512", 
              "https://doi.org/10.1007/3-540-44935-3_27"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-44935-3_27", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006695512", 
              "https://doi.org/10.1007/3-540-44935-3_27"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1006/jvci.2001.0475", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008535368"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-47967-8_6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012853927", 
              "https://doi.org/10.1007/3-540-47967-8_6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-47967-8_6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012853927", 
              "https://doi.org/10.1007/3-540-47967-8_6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cviu.2003.04.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014287534"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0262-8856(94)90060-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020753425"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0262-8856(94)90060-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020753425"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0021-9991(88)90002-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024042944"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1020878408985", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025745067", 
              "https://doi.org/10.1023/a:1020878408985"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bfb0086904", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026572227", 
              "https://doi.org/10.1007/bfb0086904"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-24673-2_7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036045770", 
              "https://doi.org/10.1007/978-3-540-24673-2_7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-24673-2_7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036045770", 
              "https://doi.org/10.1007/978-3-540-24673-2_7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-10694-4_22", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039259578", 
              "https://doi.org/10.1007/3-540-10694-4_22"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1006/jcph.1995.1098", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043296861"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1006/jcph.1994.1155", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051436961"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-24673-2_5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052914438", 
              "https://doi.org/10.1007/978-3-540-24673-2_5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-24673-2_5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052914438", 
              "https://doi.org/10.1007/978-3-540-24673-2_5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/34.841758", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061157057"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/83.902291", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061240267"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/83.935033", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061240353"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tip.2003.821445", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061640981"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tpami.1987.4767872", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061742275"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tpami.2002.1017621", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061742394"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tpami.2003.1195991", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061742511"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tpami.2004.96", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061742769"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/ccv.1988.590047", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1086231594"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icip.1998.999021", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093323850"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2000.855835", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093715328"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icpr.2000.903738", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093836002"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2005.294", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095366010"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2006", 
        "datePublishedReg": "2006-01-01", 
        "description": "Tracking of regions and object boundaries in an image sequence is a well studied problem in image processing and computer vision. So far, numerous approaches tracking different features of the objects (contours, regions or points of interest) have been presented. Most of these approaches have problems with robustness. Typical reasons are noisy images, objects with identical features or partial occlusions of the tracked features. In this paper we propose a novel level set based tracking approach, that allows robust tracking on noisy images. Our framework is able to track multiple regions in an image sequence, where a level set function is assigned to every region. For already known or learned objects, transformation invariant shape priors can be added to ensure a robust tracking even under partial occlusions. Furthermore, we introduce a simple decision function to maintain the desired topology for multiple regions. Experimental results demonstrate the method for arbitrary numbers of shape priors. The approach can even handle full occlusions and objects which are temporarily hidden in containers.", 
        "editor": [
          {
            "familyName": "Narayanan", 
            "givenName": "P. J.", 
            "type": "Person"
          }, 
          {
            "familyName": "Nayar", 
            "givenName": "Shree K.", 
            "type": "Person"
          }, 
          {
            "familyName": "Shum", 
            "givenName": "Heung-Yeung", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/11612032_68", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": {
          "isbn": [
            "978-3-540-31219-2", 
            "978-3-540-32433-1"
          ], 
          "name": "Computer Vision \u2013 ACCV 2006", 
          "type": "Book"
        }, 
        "name": "Multiregion Level Set Tracking with Transformation Invariant Shape Priors", 
        "pagination": "674-683", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1045798491"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/11612032_68"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "da282e11c8e1c08b9b09785367d9d0a9b73a96493e9ce03dc79bfeb4927589e8"
            ]
          }
        ], 
        "publisher": {
          "location": "Berlin, Heidelberg", 
          "name": "Springer Berlin Heidelberg", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/11612032_68", 
          "https://app.dimensions.ai/details/publication/pub.1045798491"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-16T07:37", 
        "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/v90/0000000357_0000000357/records_99320_00000001.jsonl", 
        "type": "Chapter", 
        "url": "https://link.springer.com/10.1007%2F11612032_68"
      }
    ]
     

    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.10.1007/11612032_68'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/11612032_68'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/11612032_68'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/11612032_68'


     

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

    181 TRIPLES      23 PREDICATES      54 URIs      20 LITERALS      8 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/11612032_68 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N25caa53146984b01929f1258f1df3983
    4 schema:citation sg:pub.10.1007/3-540-10694-4_22
    5 sg:pub.10.1007/3-540-44935-3_27
    6 sg:pub.10.1007/3-540-47967-8_6
    7 sg:pub.10.1007/978-3-540-24673-2_5
    8 sg:pub.10.1007/978-3-540-24673-2_7
    9 sg:pub.10.1007/bfb0014872
    10 sg:pub.10.1007/bfb0086904
    11 sg:pub.10.1023/a:1020878408985
    12 https://doi.org/10.1006/jcph.1994.1155
    13 https://doi.org/10.1006/jcph.1995.1098
    14 https://doi.org/10.1006/jvci.2001.0475
    15 https://doi.org/10.1016/0021-9991(88)90002-2
    16 https://doi.org/10.1016/0262-8856(94)90060-4
    17 https://doi.org/10.1016/j.cviu.2003.04.001
    18 https://doi.org/10.1109/34.841758
    19 https://doi.org/10.1109/83.902291
    20 https://doi.org/10.1109/83.935033
    21 https://doi.org/10.1109/ccv.1988.590047
    22 https://doi.org/10.1109/cvpr.2000.855835
    23 https://doi.org/10.1109/cvpr.2005.294
    24 https://doi.org/10.1109/icip.1998.999021
    25 https://doi.org/10.1109/icpr.2000.903738
    26 https://doi.org/10.1109/tip.2003.821445
    27 https://doi.org/10.1109/tpami.1987.4767872
    28 https://doi.org/10.1109/tpami.2002.1017621
    29 https://doi.org/10.1109/tpami.2003.1195991
    30 https://doi.org/10.1109/tpami.2004.96
    31 schema:datePublished 2006
    32 schema:datePublishedReg 2006-01-01
    33 schema:description Tracking of regions and object boundaries in an image sequence is a well studied problem in image processing and computer vision. So far, numerous approaches tracking different features of the objects (contours, regions or points of interest) have been presented. Most of these approaches have problems with robustness. Typical reasons are noisy images, objects with identical features or partial occlusions of the tracked features. In this paper we propose a novel level set based tracking approach, that allows robust tracking on noisy images. Our framework is able to track multiple regions in an image sequence, where a level set function is assigned to every region. For already known or learned objects, transformation invariant shape priors can be added to ensure a robust tracking even under partial occlusions. Furthermore, we introduce a simple decision function to maintain the desired topology for multiple regions. Experimental results demonstrate the method for arbitrary numbers of shape priors. The approach can even handle full occlusions and objects which are temporarily hidden in containers.
    34 schema:editor Nbc98473935d24522864f5e7a292a8ba6
    35 schema:genre chapter
    36 schema:inLanguage en
    37 schema:isAccessibleForFree false
    38 schema:isPartOf N1197e369de8d466f9bc93879588645f3
    39 schema:name Multiregion Level Set Tracking with Transformation Invariant Shape Priors
    40 schema:pagination 674-683
    41 schema:productId N4f8433bc86a4456e93fc87e8332fd77e
    42 Nc73fa4ffeac743c684013d06e562ba06
    43 Nd11d54e72fb1484bae3123a8a0930215
    44 schema:publisher N6ed86ff740ed41afb0573a52a11a835c
    45 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045798491
    46 https://doi.org/10.1007/11612032_68
    47 schema:sdDatePublished 2019-04-16T07:37
    48 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    49 schema:sdPublisher Nb59274fe44e44000b8dfbcb35483ebae
    50 schema:url https://link.springer.com/10.1007%2F11612032_68
    51 sgo:license sg:explorer/license/
    52 sgo:sdDataset chapters
    53 rdf:type schema:Chapter
    54 N1197e369de8d466f9bc93879588645f3 schema:isbn 978-3-540-31219-2
    55 978-3-540-32433-1
    56 schema:name Computer Vision – ACCV 2006
    57 rdf:type schema:Book
    58 N25caa53146984b01929f1258f1df3983 rdf:first sg:person.01330551375.09
    59 rdf:rest N2b293d30cb95448591c4674f402281f0
    60 N2b293d30cb95448591c4674f402281f0 rdf:first sg:person.01010760563.28
    61 rdf:rest N81bef6f0bcdf4ed8be3e9d57856beb24
    62 N4ee873ee48104909a81250a379c1d23d rdf:first N930bb344eaed443888315d6dfcd86225
    63 rdf:rest Ne344bb5c6ae04ae483e3c05f1709cd73
    64 N4f8433bc86a4456e93fc87e8332fd77e schema:name readcube_id
    65 schema:value da282e11c8e1c08b9b09785367d9d0a9b73a96493e9ce03dc79bfeb4927589e8
    66 rdf:type schema:PropertyValue
    67 N6ed86ff740ed41afb0573a52a11a835c schema:location Berlin, Heidelberg
    68 schema:name Springer Berlin Heidelberg
    69 rdf:type schema:Organisation
    70 N7142a2b183014d6796c8ef74b05f972e schema:familyName Shum
    71 schema:givenName Heung-Yeung
    72 rdf:type schema:Person
    73 N789e929c06c74064a717e3c8b9f210c0 schema:familyName Narayanan
    74 schema:givenName P. J.
    75 rdf:type schema:Person
    76 N81bef6f0bcdf4ed8be3e9d57856beb24 rdf:first sg:person.012033065653.49
    77 rdf:rest rdf:nil
    78 N930bb344eaed443888315d6dfcd86225 schema:familyName Nayar
    79 schema:givenName Shree K.
    80 rdf:type schema:Person
    81 Nb59274fe44e44000b8dfbcb35483ebae schema:name Springer Nature - SN SciGraph project
    82 rdf:type schema:Organization
    83 Nbc98473935d24522864f5e7a292a8ba6 rdf:first N789e929c06c74064a717e3c8b9f210c0
    84 rdf:rest N4ee873ee48104909a81250a379c1d23d
    85 Nc73fa4ffeac743c684013d06e562ba06 schema:name doi
    86 schema:value 10.1007/11612032_68
    87 rdf:type schema:PropertyValue
    88 Nd11d54e72fb1484bae3123a8a0930215 schema:name dimensions_id
    89 schema:value pub.1045798491
    90 rdf:type schema:PropertyValue
    91 Ne344bb5c6ae04ae483e3c05f1709cd73 rdf:first N7142a2b183014d6796c8ef74b05f972e
    92 rdf:rest rdf:nil
    93 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    94 schema:name Information and Computing Sciences
    95 rdf:type schema:DefinedTerm
    96 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    97 schema:name Artificial Intelligence and Image Processing
    98 rdf:type schema:DefinedTerm
    99 sg:person.01010760563.28 schema:affiliation https://www.grid.ac/institutes/grid.5328.c
    100 schema:familyName Deriche
    101 schema:givenName Rachid
    102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01010760563.28
    103 rdf:type schema:Person
    104 sg:person.012033065653.49 schema:affiliation https://www.grid.ac/institutes/grid.410413.3
    105 schema:familyName Pinz
    106 schema:givenName Axel
    107 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012033065653.49
    108 rdf:type schema:Person
    109 sg:person.01330551375.09 schema:affiliation https://www.grid.ac/institutes/grid.410413.3
    110 schema:familyName Fussenegger
    111 schema:givenName Michael
    112 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01330551375.09
    113 rdf:type schema:Person
    114 sg:pub.10.1007/3-540-10694-4_22 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039259578
    115 https://doi.org/10.1007/3-540-10694-4_22
    116 rdf:type schema:CreativeWork
    117 sg:pub.10.1007/3-540-44935-3_27 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006695512
    118 https://doi.org/10.1007/3-540-44935-3_27
    119 rdf:type schema:CreativeWork
    120 sg:pub.10.1007/3-540-47967-8_6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012853927
    121 https://doi.org/10.1007/3-540-47967-8_6
    122 rdf:type schema:CreativeWork
    123 sg:pub.10.1007/978-3-540-24673-2_5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052914438
    124 https://doi.org/10.1007/978-3-540-24673-2_5
    125 rdf:type schema:CreativeWork
    126 sg:pub.10.1007/978-3-540-24673-2_7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036045770
    127 https://doi.org/10.1007/978-3-540-24673-2_7
    128 rdf:type schema:CreativeWork
    129 sg:pub.10.1007/bfb0014872 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001567098
    130 https://doi.org/10.1007/bfb0014872
    131 rdf:type schema:CreativeWork
    132 sg:pub.10.1007/bfb0086904 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026572227
    133 https://doi.org/10.1007/bfb0086904
    134 rdf:type schema:CreativeWork
    135 sg:pub.10.1023/a:1020878408985 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025745067
    136 https://doi.org/10.1023/a:1020878408985
    137 rdf:type schema:CreativeWork
    138 https://doi.org/10.1006/jcph.1994.1155 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051436961
    139 rdf:type schema:CreativeWork
    140 https://doi.org/10.1006/jcph.1995.1098 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043296861
    141 rdf:type schema:CreativeWork
    142 https://doi.org/10.1006/jvci.2001.0475 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008535368
    143 rdf:type schema:CreativeWork
    144 https://doi.org/10.1016/0021-9991(88)90002-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024042944
    145 rdf:type schema:CreativeWork
    146 https://doi.org/10.1016/0262-8856(94)90060-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020753425
    147 rdf:type schema:CreativeWork
    148 https://doi.org/10.1016/j.cviu.2003.04.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014287534
    149 rdf:type schema:CreativeWork
    150 https://doi.org/10.1109/34.841758 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061157057
    151 rdf:type schema:CreativeWork
    152 https://doi.org/10.1109/83.902291 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061240267
    153 rdf:type schema:CreativeWork
    154 https://doi.org/10.1109/83.935033 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061240353
    155 rdf:type schema:CreativeWork
    156 https://doi.org/10.1109/ccv.1988.590047 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086231594
    157 rdf:type schema:CreativeWork
    158 https://doi.org/10.1109/cvpr.2000.855835 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093715328
    159 rdf:type schema:CreativeWork
    160 https://doi.org/10.1109/cvpr.2005.294 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095366010
    161 rdf:type schema:CreativeWork
    162 https://doi.org/10.1109/icip.1998.999021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093323850
    163 rdf:type schema:CreativeWork
    164 https://doi.org/10.1109/icpr.2000.903738 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093836002
    165 rdf:type schema:CreativeWork
    166 https://doi.org/10.1109/tip.2003.821445 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061640981
    167 rdf:type schema:CreativeWork
    168 https://doi.org/10.1109/tpami.1987.4767872 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061742275
    169 rdf:type schema:CreativeWork
    170 https://doi.org/10.1109/tpami.2002.1017621 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061742394
    171 rdf:type schema:CreativeWork
    172 https://doi.org/10.1109/tpami.2003.1195991 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061742511
    173 rdf:type schema:CreativeWork
    174 https://doi.org/10.1109/tpami.2004.96 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061742769
    175 rdf:type schema:CreativeWork
    176 https://www.grid.ac/institutes/grid.410413.3 schema:alternateName Graz University of Technology
    177 schema:name Institute of Electrical Measurement and Measurement Signal Processing, Graz University of Technology, Schießstattgasse 14b, 8010, Graz, Austria
    178 rdf:type schema:Organization
    179 https://www.grid.ac/institutes/grid.5328.c schema:alternateName French Institute for Research in Computer Science and Automation
    180 schema:name INRIA, BP 93, 2004 route des Lucioles, 06902, Sophia Antipolis, France
    181 rdf:type schema:Organization
     




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


    ...