Sparse Spatio-spectral Representation for Hyperspectral Image Super-resolution View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2014

AUTHORS

Naveed Akhtar , Faisal Shafait , Ajmal Mian

ABSTRACT

Existing hyperspectral imaging systems produce low spatial resolution images due to hardware constraints. We propose a sparse representation based approach for hyperspectral image super-resolution. The proposed approach first extracts distinct reflectance spectra of the scene from the available hyperspectral image. Then, the signal sparsity, non-negativity and the spatial structure in the scene are exploited to explain a high-spatial but low-spectral resolution image of the same scene in terms of the extracted spectra. This is done by learning a sparse code with an algorithm G-SOMP+. Finally, the learned sparse code is used with the extracted scene spectra to estimate the super-resolution hyperspectral image. Comparison of the proposed approach with the state-of-the-art methods on both ground-based and remotely-sensed public hyperspectral image databases shows that the presented method achieves the lowest error rate on all test images in the three datasets. More... »

PAGES

63-78

Book

TITLE

Computer Vision – ECCV 2014

ISBN

978-3-319-10583-3
978-3-319-10584-0

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-10584-0_5

DOI

http://dx.doi.org/10.1007/978-3-319-10584-0_5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1053013383


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": {
          "name": [
            "School of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, 6009\u00a0Crawley, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Akhtar", 
        "givenName": "Naveed", 
        "id": "sg:person.013403245773.49", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013403245773.49"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "School of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, 6009\u00a0Crawley, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shafait", 
        "givenName": "Faisal", 
        "id": "sg:person.01002345555.29", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01002345555.29"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "School of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, 6009\u00a0Crawley, WA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mian", 
        "givenName": "Ajmal", 
        "id": "sg:person.0616663134.44", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0616663134.44"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1145/1553374.1553463", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002090081"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0042-6989(97)00169-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016543153"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0034-4257(98)00064-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035457258"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1275808.1276497", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039437559"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.sigpro.2005.05.030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044110758"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.sigpro.2005.05.030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044110758"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01431160802639525", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050906129"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/36.763276", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061162054"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/79.974727", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061232100"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/lgrs.2005.861699", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061358307"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/lgrs.2008.919685", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061358744"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/mgrs.2013.2244672", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061402116"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tgrs.2005.846874", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061609452"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tgrs.2007.901007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061610267"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tgrs.2007.904923", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061610330"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tgrs.2010.2098413", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061611696"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tgrs.2011.2161320", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061611961"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tgrs.2013.2253612", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061612913"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tip.2010.2046811", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061642494"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tit.2007.909108", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061651585"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tit.2008.929920", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061652171"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsmcb.2009.2037132", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061797187"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsp.2006.881199", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061800223"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tsp.2012.2218810", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061803537"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1561/2200000016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068001405"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2011.5995457", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093381887"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icassp.2013.6637883", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093449983"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2011.5995660", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094262782"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icdar.2013.179", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094781550"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/wacv.2014.6836001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094906101"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icpr.2014.640", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095461568"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5244/c.27.57", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1099426357"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2014", 
    "datePublishedReg": "2014-01-01", 
    "description": "Existing hyperspectral imaging systems produce low spatial resolution images due to hardware constraints. We propose a sparse representation based approach for hyperspectral image super-resolution. The proposed approach first extracts distinct reflectance spectra of the scene from the available hyperspectral image. Then, the signal sparsity, non-negativity and the spatial structure in the scene are exploited to explain a high-spatial but low-spectral resolution image of the same scene in terms of the extracted spectra. This is done by learning a sparse code with an algorithm G-SOMP+. Finally, the learned sparse code is used with the extracted scene spectra to estimate the super-resolution hyperspectral image. Comparison of the proposed approach with the state-of-the-art methods on both ground-based and remotely-sensed public hyperspectral image databases shows that the presented method achieves the lowest error rate on all test images in the three datasets.", 
    "editor": [
      {
        "familyName": "Fleet", 
        "givenName": "David", 
        "type": "Person"
      }, 
      {
        "familyName": "Pajdla", 
        "givenName": "Tomas", 
        "type": "Person"
      }, 
      {
        "familyName": "Schiele", 
        "givenName": "Bernt", 
        "type": "Person"
      }, 
      {
        "familyName": "Tuytelaars", 
        "givenName": "Tinne", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-10584-0_5", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-319-10583-3", 
        "978-3-319-10584-0"
      ], 
      "name": "Computer Vision \u2013 ECCV 2014", 
      "type": "Book"
    }, 
    "name": "Sparse Spatio-spectral Representation for Hyperspectral Image Super-resolution", 
    "pagination": "63-78", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-10584-0_5"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "fc84cfd4b9cbdf9c0f6eb1d8b23b2f39590e766d08beeb6de26c3f88d7934fc0"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1053013383"
        ]
      }
    ], 
    "publisher": {
      "location": "Cham", 
      "name": "Springer International Publishing", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-10584-0_5", 
      "https://app.dimensions.ai/details/publication/pub.1053013383"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T21:05", 
    "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_8690_00000276.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-3-319-10584-0_5"
  }
]
 

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.10.1007/978-3-319-10584-0_5'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-10584-0_5'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-10584-0_5'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-10584-0_5'


 

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

190 TRIPLES      23 PREDICATES      58 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-10584-0_5 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N6c75287735a644fcb60c6c6c2e10320b
4 schema:citation https://doi.org/10.1016/j.sigpro.2005.05.030
5 https://doi.org/10.1016/s0034-4257(98)00064-9
6 https://doi.org/10.1016/s0042-6989(97)00169-7
7 https://doi.org/10.1080/01431160802639525
8 https://doi.org/10.1109/36.763276
9 https://doi.org/10.1109/79.974727
10 https://doi.org/10.1109/cvpr.2011.5995457
11 https://doi.org/10.1109/cvpr.2011.5995660
12 https://doi.org/10.1109/icassp.2013.6637883
13 https://doi.org/10.1109/icdar.2013.179
14 https://doi.org/10.1109/icpr.2014.640
15 https://doi.org/10.1109/lgrs.2005.861699
16 https://doi.org/10.1109/lgrs.2008.919685
17 https://doi.org/10.1109/mgrs.2013.2244672
18 https://doi.org/10.1109/tgrs.2005.846874
19 https://doi.org/10.1109/tgrs.2007.901007
20 https://doi.org/10.1109/tgrs.2007.904923
21 https://doi.org/10.1109/tgrs.2010.2098413
22 https://doi.org/10.1109/tgrs.2011.2161320
23 https://doi.org/10.1109/tgrs.2013.2253612
24 https://doi.org/10.1109/tip.2010.2046811
25 https://doi.org/10.1109/tit.2007.909108
26 https://doi.org/10.1109/tit.2008.929920
27 https://doi.org/10.1109/tsmcb.2009.2037132
28 https://doi.org/10.1109/tsp.2006.881199
29 https://doi.org/10.1109/tsp.2012.2218810
30 https://doi.org/10.1109/wacv.2014.6836001
31 https://doi.org/10.1145/1275808.1276497
32 https://doi.org/10.1145/1553374.1553463
33 https://doi.org/10.1561/2200000016
34 https://doi.org/10.5244/c.27.57
35 schema:datePublished 2014
36 schema:datePublishedReg 2014-01-01
37 schema:description Existing hyperspectral imaging systems produce low spatial resolution images due to hardware constraints. We propose a sparse representation based approach for hyperspectral image super-resolution. The proposed approach first extracts distinct reflectance spectra of the scene from the available hyperspectral image. Then, the signal sparsity, non-negativity and the spatial structure in the scene are exploited to explain a high-spatial but low-spectral resolution image of the same scene in terms of the extracted spectra. This is done by learning a sparse code with an algorithm G-SOMP+. Finally, the learned sparse code is used with the extracted scene spectra to estimate the super-resolution hyperspectral image. Comparison of the proposed approach with the state-of-the-art methods on both ground-based and remotely-sensed public hyperspectral image databases shows that the presented method achieves the lowest error rate on all test images in the three datasets.
38 schema:editor N547b46c8c34c4586b39aa676880116fa
39 schema:genre chapter
40 schema:inLanguage en
41 schema:isAccessibleForFree true
42 schema:isPartOf Nb40dfa3f415d4b29b17ab73267a8a744
43 schema:name Sparse Spatio-spectral Representation for Hyperspectral Image Super-resolution
44 schema:pagination 63-78
45 schema:productId N0edbb10806624286a76b5cffda0357cc
46 N3c18f955b7eb4be8848c12c6b6e38502
47 Nb448ee54d9ba4c529050438d33c497aa
48 schema:publisher Ne63ca6c3508645daa3e837979a2eb463
49 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053013383
50 https://doi.org/10.1007/978-3-319-10584-0_5
51 schema:sdDatePublished 2019-04-15T21:05
52 schema:sdLicense https://scigraph.springernature.com/explorer/license/
53 schema:sdPublisher Nffed6c9bebca4b7fa62b84f62bdcbc89
54 schema:url http://link.springer.com/10.1007/978-3-319-10584-0_5
55 sgo:license sg:explorer/license/
56 sgo:sdDataset chapters
57 rdf:type schema:Chapter
58 N018ed31d07a047d2b617415d84cda416 rdf:first sg:person.0616663134.44
59 rdf:rest rdf:nil
60 N0edbb10806624286a76b5cffda0357cc schema:name readcube_id
61 schema:value fc84cfd4b9cbdf9c0f6eb1d8b23b2f39590e766d08beeb6de26c3f88d7934fc0
62 rdf:type schema:PropertyValue
63 N3c18f955b7eb4be8848c12c6b6e38502 schema:name dimensions_id
64 schema:value pub.1053013383
65 rdf:type schema:PropertyValue
66 N547b46c8c34c4586b39aa676880116fa rdf:first Nbe0fb206dd93492cb2a7481069695caf
67 rdf:rest Ncc7af297e85444109701902514cc933d
68 N6c75287735a644fcb60c6c6c2e10320b rdf:first sg:person.013403245773.49
69 rdf:rest N7e75f03f1c1a4e9ab63f0532fa8d21e9
70 N7e75f03f1c1a4e9ab63f0532fa8d21e9 rdf:first sg:person.01002345555.29
71 rdf:rest N018ed31d07a047d2b617415d84cda416
72 N845718f3361a4146b948582822c4429e schema:name School of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, 6009 Crawley, WA, USA
73 rdf:type schema:Organization
74 N85b6a0490879416993545447791bb645 schema:familyName Pajdla
75 schema:givenName Tomas
76 rdf:type schema:Person
77 Nab24b5dd33ae4c84b8a38c360c871e5c rdf:first Nd656456c23074c0e847df4521fd7560b
78 rdf:rest rdf:nil
79 Nad49ffd08596437ba4420aa873ca8f98 schema:familyName Schiele
80 schema:givenName Bernt
81 rdf:type schema:Person
82 Nb40dfa3f415d4b29b17ab73267a8a744 schema:isbn 978-3-319-10583-3
83 978-3-319-10584-0
84 schema:name Computer Vision – ECCV 2014
85 rdf:type schema:Book
86 Nb42112b963fb46f9a2ff63a5484496b3 rdf:first Nad49ffd08596437ba4420aa873ca8f98
87 rdf:rest Nab24b5dd33ae4c84b8a38c360c871e5c
88 Nb448ee54d9ba4c529050438d33c497aa schema:name doi
89 schema:value 10.1007/978-3-319-10584-0_5
90 rdf:type schema:PropertyValue
91 Nbe0fb206dd93492cb2a7481069695caf schema:familyName Fleet
92 schema:givenName David
93 rdf:type schema:Person
94 Nc63d8eccd2ba497da22d29d9c98a453b schema:name School of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, 6009 Crawley, WA, USA
95 rdf:type schema:Organization
96 Ncc7af297e85444109701902514cc933d rdf:first N85b6a0490879416993545447791bb645
97 rdf:rest Nb42112b963fb46f9a2ff63a5484496b3
98 Nd656456c23074c0e847df4521fd7560b schema:familyName Tuytelaars
99 schema:givenName Tinne
100 rdf:type schema:Person
101 Ne63ca6c3508645daa3e837979a2eb463 schema:location Cham
102 schema:name Springer International Publishing
103 rdf:type schema:Organisation
104 Nec419db63fa8415fbe909d99d99f1e60 schema:name School of Computer Science and Software Engineering, The University of Western Australia, 35 Stirling Highway, 6009 Crawley, WA, USA
105 rdf:type schema:Organization
106 Nffed6c9bebca4b7fa62b84f62bdcbc89 schema:name Springer Nature - SN SciGraph project
107 rdf:type schema:Organization
108 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
109 schema:name Information and Computing Sciences
110 rdf:type schema:DefinedTerm
111 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
112 schema:name Artificial Intelligence and Image Processing
113 rdf:type schema:DefinedTerm
114 sg:person.01002345555.29 schema:affiliation Nec419db63fa8415fbe909d99d99f1e60
115 schema:familyName Shafait
116 schema:givenName Faisal
117 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01002345555.29
118 rdf:type schema:Person
119 sg:person.013403245773.49 schema:affiliation Nc63d8eccd2ba497da22d29d9c98a453b
120 schema:familyName Akhtar
121 schema:givenName Naveed
122 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013403245773.49
123 rdf:type schema:Person
124 sg:person.0616663134.44 schema:affiliation N845718f3361a4146b948582822c4429e
125 schema:familyName Mian
126 schema:givenName Ajmal
127 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0616663134.44
128 rdf:type schema:Person
129 https://doi.org/10.1016/j.sigpro.2005.05.030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044110758
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1016/s0034-4257(98)00064-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035457258
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1016/s0042-6989(97)00169-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016543153
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1080/01431160802639525 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050906129
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1109/36.763276 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061162054
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1109/79.974727 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061232100
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1109/cvpr.2011.5995457 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093381887
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1109/cvpr.2011.5995660 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094262782
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1109/icassp.2013.6637883 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093449983
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1109/icdar.2013.179 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094781550
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1109/icpr.2014.640 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095461568
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1109/lgrs.2005.861699 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061358307
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1109/lgrs.2008.919685 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061358744
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1109/mgrs.2013.2244672 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061402116
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1109/tgrs.2005.846874 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061609452
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1109/tgrs.2007.901007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061610267
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1109/tgrs.2007.904923 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061610330
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1109/tgrs.2010.2098413 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061611696
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1109/tgrs.2011.2161320 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061611961
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1109/tgrs.2013.2253612 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061612913
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1109/tip.2010.2046811 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061642494
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1109/tit.2007.909108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061651585
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1109/tit.2008.929920 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061652171
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1109/tsmcb.2009.2037132 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061797187
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1109/tsp.2006.881199 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061800223
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1109/tsp.2012.2218810 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061803537
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1109/wacv.2014.6836001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094906101
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1145/1275808.1276497 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039437559
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1145/1553374.1553463 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002090081
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1561/2200000016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068001405
188 rdf:type schema:CreativeWork
189 https://doi.org/10.5244/c.27.57 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099426357
190 rdf:type schema:CreativeWork
 




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


...