A general model for visual motion detection View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2004-08

AUTHORS

Takashi Nagano, Makoto Hirahara, Wakako Urushihara

ABSTRACT

We propose a general model for detection of both first-order motion and second-order motion. In this model an input stimulus is divided into a number of partially overlapping spatiotemporal local regions. Spatiotemporal frequency analysis is done for every local region using Gabor filters, then the input stimulus (original spatiotemporal signal) is replaced by the outputs of Gabor filters. Local motion is detected by applying Gabor motion detectors to each local spatiotemporal pattern depicted by each local feature value. Outputs of all the detectors are integrated to give the final output for global motion of the input stimulus. The model was simulated on a computer and was confirmed to correctly detect second-order motion as well as first-order motion. More... »

PAGES

99-103

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00422-004-0493-3

DOI

http://dx.doi.org/10.1007/s00422-004-0493-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1008023773

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/15351886


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"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Brain", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computer Simulation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Lighting", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Neurological", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Motion Perception", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Neurons", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Photic Stimulation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Retina", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Hosei University", 
          "id": "https://www.grid.ac/institutes/grid.257114.4", 
          "name": [
            "Department of Industrial and Systems Engineering, Faculty of Engineering, Hosei University, 3-7-2 Kajinocho, 184-8584, Tokyo, Koganei, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nagano", 
        "givenName": "Takashi", 
        "id": "sg:person.01034442456.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01034442456.31"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hosei University", 
          "id": "https://www.grid.ac/institutes/grid.257114.4", 
          "name": [
            "Department of Industrial and Systems Engineering, Faculty of Engineering, Hosei University, 3-7-2 Kajinocho, 184-8584, Tokyo, Koganei, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hirahara", 
        "givenName": "Makoto", 
        "id": "sg:person.014774362523.72", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014774362523.72"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hosei University", 
          "id": "https://www.grid.ac/institutes/grid.257114.4", 
          "name": [
            "Department of Industrial and Systems Engineering, Faculty of Engineering, Hosei University, 3-7-2 Kajinocho, 184-8584, Tokyo, Koganei, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Urushihara", 
        "givenName": "Wakako", 
        "id": "sg:person.01322272734.83", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01322272734.83"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/s0042-6989(96)00305-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009135906"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1364/josaa.2.000284", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012515972"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rstb.1996.0154", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021816553"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1163/156856889x00077", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022154340"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1163/156856889x00077", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022154340"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0079-6123(03)14414-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028064934"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3169/itej1978.48.157", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034201727"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0959-4388(99)80069-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035711760"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1098/rspb.1990.0012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036947172"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0042-6989(97)00092-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047875310"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0079-6123(01)34013-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051391840"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/cercor/bhg085", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052294060"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1364/josaa.2.000322", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1065159991"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1364/josaa.5.001986", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1065165963"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2004-08", 
    "datePublishedReg": "2004-08-01", 
    "description": "We propose a general model for detection of both first-order motion and second-order motion. In this model an input stimulus is divided into a number of partially overlapping spatiotemporal local regions. Spatiotemporal frequency analysis is done for every local region using Gabor filters, then the input stimulus (original spatiotemporal signal) is replaced by the outputs of Gabor filters. Local motion is detected by applying Gabor motion detectors to each local spatiotemporal pattern depicted by each local feature value. Outputs of all the detectors are integrated to give the final output for global motion of the input stimulus. The model was simulated on a computer and was confirmed to correctly detect second-order motion as well as first-order motion.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00422-004-0493-3", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1081741", 
        "issn": [
          "0340-1200", 
          "1432-0770"
        ], 
        "name": "Biological Cybernetics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "91"
      }
    ], 
    "name": "A general model for visual motion detection", 
    "pagination": "99-103", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "263a0f165fb54b065c22ceebb69e9d07f4f5a81bcccd00f3a9dbe1b4e991b107"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "15351886"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "7502533"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00422-004-0493-3"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1008023773"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00422-004-0493-3", 
      "https://app.dimensions.ai/details/publication/pub.1008023773"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T19:50", 
    "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_8681_00000480.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s00422-004-0493-3"
  }
]
 

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/s00422-004-0493-3'

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/s00422-004-0493-3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00422-004-0493-3'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00422-004-0493-3'


 

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

158 TRIPLES      21 PREDICATES      51 URIs      30 LITERALS      18 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00422-004-0493-3 schema:about N018c89983f424700be750cd30cae2eec
2 N0a55ea3c38de467180b688f3cc2659ba
3 N26f9888d8c1340bbb06d28babbc4425b
4 N31339481dbc84b9fb9e4e98106c6cddf
5 N358551c9fd41489ab1c5c6e04df949a2
6 N473b77fb160242c2828a1bdadeb6e8c6
7 N903627aa74be45e38600272f42e988b3
8 Nbe9e7fcec5e241808e6a356f21b1f62b
9 Nea18dd3d14bc41b4ba990e823c8af2f4
10 anzsrc-for:08
11 anzsrc-for:0801
12 schema:author Nd14da0705c5a42ec83e455dbd807c5e8
13 schema:citation https://doi.org/10.1016/s0042-6989(96)00305-7
14 https://doi.org/10.1016/s0042-6989(97)00092-8
15 https://doi.org/10.1016/s0079-6123(01)34013-x
16 https://doi.org/10.1016/s0079-6123(03)14414-7
17 https://doi.org/10.1016/s0959-4388(99)80069-5
18 https://doi.org/10.1093/cercor/bhg085
19 https://doi.org/10.1098/rspb.1990.0012
20 https://doi.org/10.1098/rstb.1996.0154
21 https://doi.org/10.1163/156856889x00077
22 https://doi.org/10.1364/josaa.2.000284
23 https://doi.org/10.1364/josaa.2.000322
24 https://doi.org/10.1364/josaa.5.001986
25 https://doi.org/10.3169/itej1978.48.157
26 schema:datePublished 2004-08
27 schema:datePublishedReg 2004-08-01
28 schema:description We propose a general model for detection of both first-order motion and second-order motion. In this model an input stimulus is divided into a number of partially overlapping spatiotemporal local regions. Spatiotemporal frequency analysis is done for every local region using Gabor filters, then the input stimulus (original spatiotemporal signal) is replaced by the outputs of Gabor filters. Local motion is detected by applying Gabor motion detectors to each local spatiotemporal pattern depicted by each local feature value. Outputs of all the detectors are integrated to give the final output for global motion of the input stimulus. The model was simulated on a computer and was confirmed to correctly detect second-order motion as well as first-order motion.
29 schema:genre research_article
30 schema:inLanguage en
31 schema:isAccessibleForFree false
32 schema:isPartOf N1d03bdf27c6a446db00b71df30b2704f
33 Nc71410cd16f340669e9cd59fa5dae1c2
34 sg:journal.1081741
35 schema:name A general model for visual motion detection
36 schema:pagination 99-103
37 schema:productId N3fa7d4195e9841a395b7ece7f8b66b3b
38 N63573b2a2af947ee80e690ee1b869d3e
39 Na96029be04894135b9338f75cfd8963a
40 Nb5b25d358ae34884a51d99e4b65c5177
41 Ne53e89337bef427cb293c727b0875271
42 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008023773
43 https://doi.org/10.1007/s00422-004-0493-3
44 schema:sdDatePublished 2019-04-10T19:50
45 schema:sdLicense https://scigraph.springernature.com/explorer/license/
46 schema:sdPublisher N3399b89f7f964470a96b4ce0df924cc9
47 schema:url http://link.springer.com/10.1007/s00422-004-0493-3
48 sgo:license sg:explorer/license/
49 sgo:sdDataset articles
50 rdf:type schema:ScholarlyArticle
51 N018c89983f424700be750cd30cae2eec schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
52 schema:name Neurons
53 rdf:type schema:DefinedTerm
54 N0a55ea3c38de467180b688f3cc2659ba schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
55 schema:name Photic Stimulation
56 rdf:type schema:DefinedTerm
57 N0dc00505e72d42758d5ddd6fcc09646b rdf:first sg:person.01322272734.83
58 rdf:rest rdf:nil
59 N1d03bdf27c6a446db00b71df30b2704f schema:issueNumber 2
60 rdf:type schema:PublicationIssue
61 N26f9888d8c1340bbb06d28babbc4425b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
62 schema:name Models, Neurological
63 rdf:type schema:DefinedTerm
64 N31339481dbc84b9fb9e4e98106c6cddf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
65 schema:name Retina
66 rdf:type schema:DefinedTerm
67 N3399b89f7f964470a96b4ce0df924cc9 schema:name Springer Nature - SN SciGraph project
68 rdf:type schema:Organization
69 N358551c9fd41489ab1c5c6e04df949a2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
70 schema:name Humans
71 rdf:type schema:DefinedTerm
72 N3fa7d4195e9841a395b7ece7f8b66b3b schema:name pubmed_id
73 schema:value 15351886
74 rdf:type schema:PropertyValue
75 N473b77fb160242c2828a1bdadeb6e8c6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
76 schema:name Brain
77 rdf:type schema:DefinedTerm
78 N63573b2a2af947ee80e690ee1b869d3e schema:name nlm_unique_id
79 schema:value 7502533
80 rdf:type schema:PropertyValue
81 N903627aa74be45e38600272f42e988b3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
82 schema:name Computer Simulation
83 rdf:type schema:DefinedTerm
84 Na96029be04894135b9338f75cfd8963a schema:name readcube_id
85 schema:value 263a0f165fb54b065c22ceebb69e9d07f4f5a81bcccd00f3a9dbe1b4e991b107
86 rdf:type schema:PropertyValue
87 Nb16c96ab3da84bd7a4db80d5705d05d3 rdf:first sg:person.014774362523.72
88 rdf:rest N0dc00505e72d42758d5ddd6fcc09646b
89 Nb5b25d358ae34884a51d99e4b65c5177 schema:name doi
90 schema:value 10.1007/s00422-004-0493-3
91 rdf:type schema:PropertyValue
92 Nbe9e7fcec5e241808e6a356f21b1f62b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
93 schema:name Motion Perception
94 rdf:type schema:DefinedTerm
95 Nc71410cd16f340669e9cd59fa5dae1c2 schema:volumeNumber 91
96 rdf:type schema:PublicationVolume
97 Nd14da0705c5a42ec83e455dbd807c5e8 rdf:first sg:person.01034442456.31
98 rdf:rest Nb16c96ab3da84bd7a4db80d5705d05d3
99 Ne53e89337bef427cb293c727b0875271 schema:name dimensions_id
100 schema:value pub.1008023773
101 rdf:type schema:PropertyValue
102 Nea18dd3d14bc41b4ba990e823c8af2f4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
103 schema:name Lighting
104 rdf:type schema:DefinedTerm
105 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
106 schema:name Information and Computing Sciences
107 rdf:type schema:DefinedTerm
108 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
109 schema:name Artificial Intelligence and Image Processing
110 rdf:type schema:DefinedTerm
111 sg:journal.1081741 schema:issn 0340-1200
112 1432-0770
113 schema:name Biological Cybernetics
114 rdf:type schema:Periodical
115 sg:person.01034442456.31 schema:affiliation https://www.grid.ac/institutes/grid.257114.4
116 schema:familyName Nagano
117 schema:givenName Takashi
118 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01034442456.31
119 rdf:type schema:Person
120 sg:person.01322272734.83 schema:affiliation https://www.grid.ac/institutes/grid.257114.4
121 schema:familyName Urushihara
122 schema:givenName Wakako
123 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01322272734.83
124 rdf:type schema:Person
125 sg:person.014774362523.72 schema:affiliation https://www.grid.ac/institutes/grid.257114.4
126 schema:familyName Hirahara
127 schema:givenName Makoto
128 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014774362523.72
129 rdf:type schema:Person
130 https://doi.org/10.1016/s0042-6989(96)00305-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009135906
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1016/s0042-6989(97)00092-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047875310
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1016/s0079-6123(01)34013-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1051391840
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1016/s0079-6123(03)14414-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028064934
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1016/s0959-4388(99)80069-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035711760
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1093/cercor/bhg085 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052294060
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1098/rspb.1990.0012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036947172
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1098/rstb.1996.0154 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021816553
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1163/156856889x00077 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022154340
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1364/josaa.2.000284 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012515972
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1364/josaa.2.000322 schema:sameAs https://app.dimensions.ai/details/publication/pub.1065159991
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1364/josaa.5.001986 schema:sameAs https://app.dimensions.ai/details/publication/pub.1065165963
153 rdf:type schema:CreativeWork
154 https://doi.org/10.3169/itej1978.48.157 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034201727
155 rdf:type schema:CreativeWork
156 https://www.grid.ac/institutes/grid.257114.4 schema:alternateName Hosei University
157 schema:name Department of Industrial and Systems Engineering, Faculty of Engineering, Hosei University, 3-7-2 Kajinocho, 184-8584, Tokyo, Koganei, Japan
158 rdf:type schema:Organization
 




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


...