Kinect-Based Real-Time Gesture Recognition Using Deep Convolutional Neural Networks for Touchless Visualization of Hepatic Anatomical Models in Surgery View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2019

AUTHORS

Jia-Qing Liu , Tomoko Tateyama , Yutaro Iwamoto , Yen-Wei Chen

ABSTRACT

In this paper, we present a novel touchless interaction system for visualization of hepatic anatomical models in surgery. Real-time visualization is important in surgery, particularly during the operation. However, it often faces the challenge of efficiently reviewing the patient’s 3D anatomy model while maintaining a sterile field. The touchless technology is an attractive and potential solution to address the above problem. We use a Microsoft Kinect sensor as input device to produce depth images for extracting a hand without markers. Based on this representation, a deep convolutional neural network is used to recognize various hand gestures. Experimental results demonstrate that our system can significantly improve the response time while achieve almost same accuracy compared with the previous researches. More... »

PAGES

223-229

References to SciGraph publications

Book

TITLE

Intelligent Interactive Multimedia Systems and Services

ISBN

978-3-319-92230-0
978-3-319-92231-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-92231-7_23

DOI

http://dx.doi.org/10.1007/978-3-319-92231-7_23

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104528329


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": "Ritsumeikan University", 
          "id": "https://www.grid.ac/institutes/grid.262576.2", 
          "name": [
            "Ritsumeikan University"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liu", 
        "givenName": "Jia-Qing", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hiroshima Institute of Technology", 
          "id": "https://www.grid.ac/institutes/grid.417545.6", 
          "name": [
            "Hiroshima Institute of Technology"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tateyama", 
        "givenName": "Tomoko", 
        "id": "sg:person.01332555451.10", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01332555451.10"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Ritsumeikan University", 
          "id": "https://www.grid.ac/institutes/grid.262576.2", 
          "name": [
            "Ritsumeikan University"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Iwamoto", 
        "givenName": "Yutaro", 
        "id": "sg:person.011666053265.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011666053265.07"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Ritsumeikan University", 
          "id": "https://www.grid.ac/institutes/grid.262576.2", 
          "name": [
            "Ritsumeikan University"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chen", 
        "givenName": "Yen-Wei", 
        "id": "sg:person.011476166671.03", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011476166671.03"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1227/neu.0000000000000214", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010485184"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11548-016-1480-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024536471", 
          "https://doi.org/10.1007/s11548-016-1480-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11548-016-1480-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024536471", 
          "https://doi.org/10.1007/s11548-016-1480-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00345-012-0879-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036870076", 
          "https://doi.org/10.1007/s00345-012-0879-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11605-013-2262-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052275979", 
          "https://doi.org/10.1007/s11605-013-2262-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/5.726791", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061179979"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/3065386", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085642448"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/3065386", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085642448"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.17706/ijcee.2017.9.2.421-429", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092257465"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cbms.2011.5999138", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094062476"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019", 
    "datePublishedReg": "2019-01-01", 
    "description": "In this paper, we present a novel touchless interaction system for visualization of hepatic anatomical models in surgery. Real-time visualization is important in surgery, particularly during the operation. However, it often faces the challenge of efficiently reviewing the patient\u2019s 3D anatomy model while maintaining a sterile field. The touchless technology is an attractive and potential solution to address the above problem. We use a Microsoft Kinect sensor as input device to produce depth images for extracting a hand without markers. Based on this representation, a deep convolutional neural network is used to recognize various hand gestures. Experimental results demonstrate that our system can significantly improve the response time while achieve almost same accuracy compared with the previous researches.", 
    "editor": [
      {
        "familyName": "De Pietro", 
        "givenName": "Giuseppe", 
        "type": "Person"
      }, 
      {
        "familyName": "Gallo", 
        "givenName": "Luigi", 
        "type": "Person"
      }, 
      {
        "familyName": "Howlett", 
        "givenName": "Robert J.", 
        "type": "Person"
      }, 
      {
        "familyName": "Jain", 
        "givenName": "Lakhmi C.", 
        "type": "Person"
      }, 
      {
        "familyName": "Vlacic", 
        "givenName": "Ljubo", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-92231-7_23", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-92230-0", 
        "978-3-319-92231-7"
      ], 
      "name": "Intelligent Interactive Multimedia Systems and Services", 
      "type": "Book"
    }, 
    "name": "Kinect-Based Real-Time Gesture Recognition Using Deep Convolutional Neural Networks for Touchless Visualization of Hepatic Anatomical Models in Surgery", 
    "pagination": "223-229", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-92231-7_23"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "cc7cddec480c78f191d47e7943d217281529427dc493a6a0c83872d34cd1eea7"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1104528329"
        ]
      }
    ], 
    "publisher": {
      "location": "Cham", 
      "name": "Springer International Publishing", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-92231-7_23", 
      "https://app.dimensions.ai/details/publication/pub.1104528329"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T14:43", 
    "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_8669_00000420.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-3-319-92231-7_23"
  }
]
 

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.10.1007/978-3-319-92231-7_23'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-92231-7_23'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-92231-7_23'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-92231-7_23'


 

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

135 TRIPLES      23 PREDICATES      35 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-92231-7_23 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N7d5f729181274ee6bd49f8b08e58ef55
4 schema:citation sg:pub.10.1007/s00345-012-0879-0
5 sg:pub.10.1007/s11548-016-1480-6
6 sg:pub.10.1007/s11605-013-2262-x
7 https://doi.org/10.1109/5.726791
8 https://doi.org/10.1109/cbms.2011.5999138
9 https://doi.org/10.1145/3065386
10 https://doi.org/10.1227/neu.0000000000000214
11 https://doi.org/10.17706/ijcee.2017.9.2.421-429
12 schema:datePublished 2019
13 schema:datePublishedReg 2019-01-01
14 schema:description In this paper, we present a novel touchless interaction system for visualization of hepatic anatomical models in surgery. Real-time visualization is important in surgery, particularly during the operation. However, it often faces the challenge of efficiently reviewing the patient’s 3D anatomy model while maintaining a sterile field. The touchless technology is an attractive and potential solution to address the above problem. We use a Microsoft Kinect sensor as input device to produce depth images for extracting a hand without markers. Based on this representation, a deep convolutional neural network is used to recognize various hand gestures. Experimental results demonstrate that our system can significantly improve the response time while achieve almost same accuracy compared with the previous researches.
15 schema:editor Nc3adb6f3edf24d3188e14d33091e83bc
16 schema:genre chapter
17 schema:inLanguage en
18 schema:isAccessibleForFree false
19 schema:isPartOf N1c77005022a94a71aa4eadd690a37c76
20 schema:name Kinect-Based Real-Time Gesture Recognition Using Deep Convolutional Neural Networks for Touchless Visualization of Hepatic Anatomical Models in Surgery
21 schema:pagination 223-229
22 schema:productId N4d0c06f6b7904922a78458101b15058a
23 N7848799be08f4bdfa4c5614803829ec8
24 Nbdd7c903f9134c479f4883500ec4cd3c
25 schema:publisher N48d2146d86894eeaa6f12582a9b60cb3
26 schema:sameAs https://app.dimensions.ai/details/publication/pub.1104528329
27 https://doi.org/10.1007/978-3-319-92231-7_23
28 schema:sdDatePublished 2019-04-15T14:43
29 schema:sdLicense https://scigraph.springernature.com/explorer/license/
30 schema:sdPublisher N866021609eea4dca9c1ac8754f6b2d5f
31 schema:url http://link.springer.com/10.1007/978-3-319-92231-7_23
32 sgo:license sg:explorer/license/
33 sgo:sdDataset chapters
34 rdf:type schema:Chapter
35 N0d18a4b649514292b73749dac941d2a3 rdf:first Nafb725a1addb497880d963bf0ad0f310
36 rdf:rest rdf:nil
37 N1c77005022a94a71aa4eadd690a37c76 schema:isbn 978-3-319-92230-0
38 978-3-319-92231-7
39 schema:name Intelligent Interactive Multimedia Systems and Services
40 rdf:type schema:Book
41 N1ed6cd2fe4dd4fbfa370416b1c3e9df9 schema:familyName Jain
42 schema:givenName Lakhmi C.
43 rdf:type schema:Person
44 N48d2146d86894eeaa6f12582a9b60cb3 schema:location Cham
45 schema:name Springer International Publishing
46 rdf:type schema:Organisation
47 N4d0c06f6b7904922a78458101b15058a schema:name doi
48 schema:value 10.1007/978-3-319-92231-7_23
49 rdf:type schema:PropertyValue
50 N4ff9f541277345faa52273223c4582df rdf:first sg:person.011476166671.03
51 rdf:rest rdf:nil
52 N60ad472de83a495d8794836849ec8b2a schema:affiliation https://www.grid.ac/institutes/grid.262576.2
53 schema:familyName Liu
54 schema:givenName Jia-Qing
55 rdf:type schema:Person
56 N6928e09141034887b38d52b07641a824 schema:familyName Howlett
57 schema:givenName Robert J.
58 rdf:type schema:Person
59 N7848799be08f4bdfa4c5614803829ec8 schema:name dimensions_id
60 schema:value pub.1104528329
61 rdf:type schema:PropertyValue
62 N7d5f729181274ee6bd49f8b08e58ef55 rdf:first N60ad472de83a495d8794836849ec8b2a
63 rdf:rest N83ec61a1226544ecbf8a1c23ba7b8bd7
64 N83ec61a1226544ecbf8a1c23ba7b8bd7 rdf:first sg:person.01332555451.10
65 rdf:rest Nff7e6fa1046d4715845d3e9631bf791a
66 N866021609eea4dca9c1ac8754f6b2d5f schema:name Springer Nature - SN SciGraph project
67 rdf:type schema:Organization
68 N9069537d015749f2b1be7f6cad1d16ad schema:familyName Gallo
69 schema:givenName Luigi
70 rdf:type schema:Person
71 N9b52033810c843e9a1bc1aafad70352c rdf:first N6928e09141034887b38d52b07641a824
72 rdf:rest Nf0a97c3f86fb492f8d3c5b978997844a
73 Nafb725a1addb497880d963bf0ad0f310 schema:familyName Vlacic
74 schema:givenName Ljubo
75 rdf:type schema:Person
76 Nb3f5a7c176fc4c87a86093664d3e0398 schema:familyName De Pietro
77 schema:givenName Giuseppe
78 rdf:type schema:Person
79 Nbdd7c903f9134c479f4883500ec4cd3c schema:name readcube_id
80 schema:value cc7cddec480c78f191d47e7943d217281529427dc493a6a0c83872d34cd1eea7
81 rdf:type schema:PropertyValue
82 Nc3adb6f3edf24d3188e14d33091e83bc rdf:first Nb3f5a7c176fc4c87a86093664d3e0398
83 rdf:rest Ne38dac99ca164a759bf0e8eb1e8a32b5
84 Ne38dac99ca164a759bf0e8eb1e8a32b5 rdf:first N9069537d015749f2b1be7f6cad1d16ad
85 rdf:rest N9b52033810c843e9a1bc1aafad70352c
86 Nf0a97c3f86fb492f8d3c5b978997844a rdf:first N1ed6cd2fe4dd4fbfa370416b1c3e9df9
87 rdf:rest N0d18a4b649514292b73749dac941d2a3
88 Nff7e6fa1046d4715845d3e9631bf791a rdf:first sg:person.011666053265.07
89 rdf:rest N4ff9f541277345faa52273223c4582df
90 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
91 schema:name Information and Computing Sciences
92 rdf:type schema:DefinedTerm
93 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
94 schema:name Artificial Intelligence and Image Processing
95 rdf:type schema:DefinedTerm
96 sg:person.011476166671.03 schema:affiliation https://www.grid.ac/institutes/grid.262576.2
97 schema:familyName Chen
98 schema:givenName Yen-Wei
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011476166671.03
100 rdf:type schema:Person
101 sg:person.011666053265.07 schema:affiliation https://www.grid.ac/institutes/grid.262576.2
102 schema:familyName Iwamoto
103 schema:givenName Yutaro
104 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011666053265.07
105 rdf:type schema:Person
106 sg:person.01332555451.10 schema:affiliation https://www.grid.ac/institutes/grid.417545.6
107 schema:familyName Tateyama
108 schema:givenName Tomoko
109 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01332555451.10
110 rdf:type schema:Person
111 sg:pub.10.1007/s00345-012-0879-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036870076
112 https://doi.org/10.1007/s00345-012-0879-0
113 rdf:type schema:CreativeWork
114 sg:pub.10.1007/s11548-016-1480-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024536471
115 https://doi.org/10.1007/s11548-016-1480-6
116 rdf:type schema:CreativeWork
117 sg:pub.10.1007/s11605-013-2262-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1052275979
118 https://doi.org/10.1007/s11605-013-2262-x
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1109/5.726791 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061179979
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1109/cbms.2011.5999138 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094062476
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1145/3065386 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085642448
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1227/neu.0000000000000214 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010485184
127 rdf:type schema:CreativeWork
128 https://doi.org/10.17706/ijcee.2017.9.2.421-429 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092257465
129 rdf:type schema:CreativeWork
130 https://www.grid.ac/institutes/grid.262576.2 schema:alternateName Ritsumeikan University
131 schema:name Ritsumeikan University
132 rdf:type schema:Organization
133 https://www.grid.ac/institutes/grid.417545.6 schema:alternateName Hiroshima Institute of Technology
134 schema:name Hiroshima Institute of Technology
135 rdf:type schema:Organization
 




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


...