Microscopic image super resolution using deep convolutional neural networks View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-09

AUTHORS

Selen Ayas, Murat Ekinci

ABSTRACT

Recently, deep convolutional neural networks (CNNs) have achieved excellent results in single image super resolution (SISR). Owing to the strength of deep CNNs, it gives promising results compared to state-of-the-art learning based models on natural images. Therefore, deep CNNs techniques have also been successfully applied to medical images to obtain better quality images. In this study, we present the first multi-scale deep CNNs capable of SISR for low resolution (LR) microscopic images. To achieve the difficulty of training deep CNNs, residual learning scheme is adopted where the residuals are explicitly supervised by the difference between the high resolution (HR) and the LR images and HR image is reconstructed by adding the lost details into the LR image. Furthermore, gradient clipping is used to avoid gradient explosions with high learning rates. Unlike the deep CNNs based SISR on natural images where the corresponding LR images are obtained by blurring and subsampling HR images, the proposed deep CNNs approach is tested using thin smear blood samples that are imaged at lower objective lenses and the performance is compared with the HR images taken at higher objective lenses. Extensive evaluations show that the superior performance on SISR for microscopic images is obtained using the proposed approach. More... »

PAGES

1-19

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11042-019-7397-7

DOI

http://dx.doi.org/10.1007/s11042-019-7397-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112672304


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": "Karadeniz Technical University", 
          "id": "https://www.grid.ac/institutes/grid.31564.35", 
          "name": [
            "Department of Computer Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ayas", 
        "givenName": "Selen", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Karadeniz Technical University", 
          "id": "https://www.grid.ac/institutes/grid.31564.35", 
          "name": [
            "Department of Computer Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ekinci", 
        "givenName": "Murat", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/s0167-8655(03)00106-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006491945"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0167-8655(03)00106-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006491945"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/jvci.1993.1030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008842757"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-46475-6_25", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011486685", 
          "https://doi.org/10.1007/978-3-319-46475-6_25"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tip.2010.2050625", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019629051"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11760-014-0708-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031985196", 
          "https://doi.org/10.1007/s11760-014-0708-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-16817-3_8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040555336", 
          "https://doi.org/10.1007/978-3-319-16817-3_8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0450(1979)018<1016:lfioat>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044607315"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/83.503915", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061239457"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/83.951537", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061240383"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tassp.1981.1163711", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061518967"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tip.2003.819861", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061640964"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tip.2006.877407", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061641507"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tip.2007.891794", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061641710"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tip.2008.924289", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061642126"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.2015.2439281", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061744884"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.2016.2577031", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061745117"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1137/s0036144593251710", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062877860"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tip.2017.2662206", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083507470"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11042-017-4495-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084028919", 
          "https://doi.org/10.1007/s11042-017-4495-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11042-017-4495-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084028919", 
          "https://doi.org/10.1007/s11042-017-4495-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.media.2017.07.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1090904008"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/3123266.3123314", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092535309"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/3123266.3123313", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092537210"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1364/optica.4.001437", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092759578"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2008.4587647", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093279302"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iccv.2013.241", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093754462"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iccv.2015.123", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093828312"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/ijcnn.2016.7727519", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093885223"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2014.81", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094727707"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icarcv.2014.7064414", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094981103"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2004.1315043", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095670660"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2017.618", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095851081"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5244/c.26.135", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1099383173"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1104336000", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1104336000", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/3209978.3209996", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105280218"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/3209978.3209996", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105280218"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jocs.2018.07.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1105432723"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3389/fnins.2018.00818", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109770105"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3389/fnins.2018.00818", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109770105"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-03-09", 
    "datePublishedReg": "2019-03-09", 
    "description": "Recently, deep convolutional neural networks (CNNs) have achieved excellent results in single image super resolution (SISR). Owing to the strength of deep CNNs, it gives promising results compared to state-of-the-art learning based models on natural images. Therefore, deep CNNs techniques have also been successfully applied to medical images to obtain better quality images. In this study, we present the first multi-scale deep CNNs capable of SISR for low resolution (LR) microscopic images. To achieve the difficulty of training deep CNNs, residual learning scheme is adopted where the residuals are explicitly supervised by the difference between the high resolution (HR) and the LR images and HR image is reconstructed by adding the lost details into the LR image. Furthermore, gradient clipping is used to avoid gradient explosions with high learning rates. Unlike the deep CNNs based SISR on natural images where the corresponding LR images are obtained by blurring and subsampling HR images, the proposed deep CNNs approach is tested using thin smear blood samples that are imaged at lower objective lenses and the performance is compared with the HR images taken at higher objective lenses. Extensive evaluations show that the superior performance on SISR for microscopic images is obtained using the proposed approach.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s11042-019-7397-7", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1044869", 
        "issn": [
          "1380-7501", 
          "1573-7721"
        ], 
        "name": "Multimedia Tools and Applications", 
        "type": "Periodical"
      }
    ], 
    "name": "Microscopic image super resolution using deep convolutional neural networks", 
    "pagination": "1-19", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "10a12d0b249f27d58fee107d4acc7b3b75d906990a9597303712e4b6ed934aeb"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11042-019-7397-7"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1112672304"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11042-019-7397-7", 
      "https://app.dimensions.ai/details/publication/pub.1112672304"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T11:21", 
    "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/v90/0000000354_0000000354/records_11724_00000002.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs11042-019-7397-7"
  }
]
 

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/s11042-019-7397-7'

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/s11042-019-7397-7'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11042-019-7397-7'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11042-019-7397-7'


 

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

171 TRIPLES      21 PREDICATES      60 URIs      16 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11042-019-7397-7 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N9b4e2922e5f44d7c9fcd955a4534755e
4 schema:citation sg:pub.10.1007/978-3-319-16817-3_8
5 sg:pub.10.1007/978-3-319-46475-6_25
6 sg:pub.10.1007/s11042-017-4495-2
7 sg:pub.10.1007/s11760-014-0708-6
8 https://app.dimensions.ai/details/publication/pub.1104336000
9 https://doi.org/10.1006/jvci.1993.1030
10 https://doi.org/10.1016/j.jocs.2018.07.003
11 https://doi.org/10.1016/j.media.2017.07.005
12 https://doi.org/10.1016/s0167-8655(03)00106-5
13 https://doi.org/10.1109/83.503915
14 https://doi.org/10.1109/83.951537
15 https://doi.org/10.1109/cvpr.2004.1315043
16 https://doi.org/10.1109/cvpr.2008.4587647
17 https://doi.org/10.1109/cvpr.2014.81
18 https://doi.org/10.1109/cvpr.2017.618
19 https://doi.org/10.1109/icarcv.2014.7064414
20 https://doi.org/10.1109/iccv.2013.241
21 https://doi.org/10.1109/iccv.2015.123
22 https://doi.org/10.1109/ijcnn.2016.7727519
23 https://doi.org/10.1109/tassp.1981.1163711
24 https://doi.org/10.1109/tip.2003.819861
25 https://doi.org/10.1109/tip.2006.877407
26 https://doi.org/10.1109/tip.2007.891794
27 https://doi.org/10.1109/tip.2008.924289
28 https://doi.org/10.1109/tip.2010.2050625
29 https://doi.org/10.1109/tip.2017.2662206
30 https://doi.org/10.1109/tpami.2015.2439281
31 https://doi.org/10.1109/tpami.2016.2577031
32 https://doi.org/10.1137/s0036144593251710
33 https://doi.org/10.1145/3123266.3123313
34 https://doi.org/10.1145/3123266.3123314
35 https://doi.org/10.1145/3209978.3209996
36 https://doi.org/10.1175/1520-0450(1979)018<1016:lfioat>2.0.co;2
37 https://doi.org/10.1364/optica.4.001437
38 https://doi.org/10.3389/fnins.2018.00818
39 https://doi.org/10.5244/c.26.135
40 schema:datePublished 2019-03-09
41 schema:datePublishedReg 2019-03-09
42 schema:description Recently, deep convolutional neural networks (CNNs) have achieved excellent results in single image super resolution (SISR). Owing to the strength of deep CNNs, it gives promising results compared to state-of-the-art learning based models on natural images. Therefore, deep CNNs techniques have also been successfully applied to medical images to obtain better quality images. In this study, we present the first multi-scale deep CNNs capable of SISR for low resolution (LR) microscopic images. To achieve the difficulty of training deep CNNs, residual learning scheme is adopted where the residuals are explicitly supervised by the difference between the high resolution (HR) and the LR images and HR image is reconstructed by adding the lost details into the LR image. Furthermore, gradient clipping is used to avoid gradient explosions with high learning rates. Unlike the deep CNNs based SISR on natural images where the corresponding LR images are obtained by blurring and subsampling HR images, the proposed deep CNNs approach is tested using thin smear blood samples that are imaged at lower objective lenses and the performance is compared with the HR images taken at higher objective lenses. Extensive evaluations show that the superior performance on SISR for microscopic images is obtained using the proposed approach.
43 schema:genre research_article
44 schema:inLanguage en
45 schema:isAccessibleForFree false
46 schema:isPartOf sg:journal.1044869
47 schema:name Microscopic image super resolution using deep convolutional neural networks
48 schema:pagination 1-19
49 schema:productId N373f62896c8843e38bbe10f4d0e1edde
50 Nba2ab5de6bc64deba87937da5237b830
51 Nf74860c007bb46388c555fe2ac281d68
52 schema:sameAs https://app.dimensions.ai/details/publication/pub.1112672304
53 https://doi.org/10.1007/s11042-019-7397-7
54 schema:sdDatePublished 2019-04-11T11:21
55 schema:sdLicense https://scigraph.springernature.com/explorer/license/
56 schema:sdPublisher N8a19e87435454a66b4eaddda317b30c0
57 schema:url https://link.springer.com/10.1007%2Fs11042-019-7397-7
58 sgo:license sg:explorer/license/
59 sgo:sdDataset articles
60 rdf:type schema:ScholarlyArticle
61 N1c0a857289514d4a8c673946eae4ddb0 rdf:first Nd2c175549e704d3b8a23d705ff30c858
62 rdf:rest rdf:nil
63 N373f62896c8843e38bbe10f4d0e1edde schema:name readcube_id
64 schema:value 10a12d0b249f27d58fee107d4acc7b3b75d906990a9597303712e4b6ed934aeb
65 rdf:type schema:PropertyValue
66 N7e7a8002e71a4c2a98b93e4c24acdef5 schema:affiliation https://www.grid.ac/institutes/grid.31564.35
67 schema:familyName Ayas
68 schema:givenName Selen
69 rdf:type schema:Person
70 N8a19e87435454a66b4eaddda317b30c0 schema:name Springer Nature - SN SciGraph project
71 rdf:type schema:Organization
72 N9b4e2922e5f44d7c9fcd955a4534755e rdf:first N7e7a8002e71a4c2a98b93e4c24acdef5
73 rdf:rest N1c0a857289514d4a8c673946eae4ddb0
74 Nba2ab5de6bc64deba87937da5237b830 schema:name doi
75 schema:value 10.1007/s11042-019-7397-7
76 rdf:type schema:PropertyValue
77 Nd2c175549e704d3b8a23d705ff30c858 schema:affiliation https://www.grid.ac/institutes/grid.31564.35
78 schema:familyName Ekinci
79 schema:givenName Murat
80 rdf:type schema:Person
81 Nf74860c007bb46388c555fe2ac281d68 schema:name dimensions_id
82 schema:value pub.1112672304
83 rdf:type schema:PropertyValue
84 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
85 schema:name Information and Computing Sciences
86 rdf:type schema:DefinedTerm
87 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
88 schema:name Artificial Intelligence and Image Processing
89 rdf:type schema:DefinedTerm
90 sg:journal.1044869 schema:issn 1380-7501
91 1573-7721
92 schema:name Multimedia Tools and Applications
93 rdf:type schema:Periodical
94 sg:pub.10.1007/978-3-319-16817-3_8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040555336
95 https://doi.org/10.1007/978-3-319-16817-3_8
96 rdf:type schema:CreativeWork
97 sg:pub.10.1007/978-3-319-46475-6_25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011486685
98 https://doi.org/10.1007/978-3-319-46475-6_25
99 rdf:type schema:CreativeWork
100 sg:pub.10.1007/s11042-017-4495-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084028919
101 https://doi.org/10.1007/s11042-017-4495-2
102 rdf:type schema:CreativeWork
103 sg:pub.10.1007/s11760-014-0708-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031985196
104 https://doi.org/10.1007/s11760-014-0708-6
105 rdf:type schema:CreativeWork
106 https://app.dimensions.ai/details/publication/pub.1104336000 schema:CreativeWork
107 https://doi.org/10.1006/jvci.1993.1030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008842757
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1016/j.jocs.2018.07.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105432723
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1016/j.media.2017.07.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090904008
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1016/s0167-8655(03)00106-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006491945
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1109/83.503915 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061239457
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1109/83.951537 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061240383
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1109/cvpr.2004.1315043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095670660
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1109/cvpr.2008.4587647 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093279302
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1109/cvpr.2014.81 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094727707
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1109/cvpr.2017.618 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095851081
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1109/icarcv.2014.7064414 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094981103
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1109/iccv.2013.241 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093754462
130 rdf:type schema:CreativeWork
131 https://doi.org/10.1109/iccv.2015.123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093828312
132 rdf:type schema:CreativeWork
133 https://doi.org/10.1109/ijcnn.2016.7727519 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093885223
134 rdf:type schema:CreativeWork
135 https://doi.org/10.1109/tassp.1981.1163711 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061518967
136 rdf:type schema:CreativeWork
137 https://doi.org/10.1109/tip.2003.819861 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061640964
138 rdf:type schema:CreativeWork
139 https://doi.org/10.1109/tip.2006.877407 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061641507
140 rdf:type schema:CreativeWork
141 https://doi.org/10.1109/tip.2007.891794 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061641710
142 rdf:type schema:CreativeWork
143 https://doi.org/10.1109/tip.2008.924289 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061642126
144 rdf:type schema:CreativeWork
145 https://doi.org/10.1109/tip.2010.2050625 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019629051
146 rdf:type schema:CreativeWork
147 https://doi.org/10.1109/tip.2017.2662206 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083507470
148 rdf:type schema:CreativeWork
149 https://doi.org/10.1109/tpami.2015.2439281 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061744884
150 rdf:type schema:CreativeWork
151 https://doi.org/10.1109/tpami.2016.2577031 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061745117
152 rdf:type schema:CreativeWork
153 https://doi.org/10.1137/s0036144593251710 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062877860
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1145/3123266.3123313 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092537210
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1145/3123266.3123314 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092535309
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1145/3209978.3209996 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105280218
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1175/1520-0450(1979)018<1016:lfioat>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044607315
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1364/optica.4.001437 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092759578
164 rdf:type schema:CreativeWork
165 https://doi.org/10.3389/fnins.2018.00818 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109770105
166 rdf:type schema:CreativeWork
167 https://doi.org/10.5244/c.26.135 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099383173
168 rdf:type schema:CreativeWork
169 https://www.grid.ac/institutes/grid.31564.35 schema:alternateName Karadeniz Technical University
170 schema:name Department of Computer Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey
171 rdf:type schema:Organization
 




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


...