Ontology type: sgo:Patent
2014-04-01T00:00
AUTHORSAshish Kapoor , Eric J. Horvitz , Desney S. Tan , Pradeep U. Shenoy
ABSTRACTThe subject disclosure relates to a method and system for visual object categorization. The method and system include receiving human inputs including data corresponding to passive human-brain responses to visualization of images. Computer inputs are also received which include data corresponding to outputs from a computerized vision-based processing of the images. The human and computer inputs are processing so as to yield a categorization for the images as a function of the human and computer inputs. More... »
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/2008/2746",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"type": "DefinedTerm"
}
],
"author": [
{
"name": "Ashish Kapoor",
"type": "Person"
},
{
"name": "Eric J. Horvitz",
"type": "Person"
},
{
"name": "Desney S. Tan",
"type": "Person"
},
{
"name": "Pradeep U. Shenoy",
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/bf00058655",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002929950",
"https://doi.org/10.1007/bf00058655"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00058655",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002929950",
"https://doi.org/10.1007/bf00058655"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cviu.2005.09.012",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1004784969"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0013-4694(88)90149-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005445238"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0013-4694(88)90149-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005445238"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11263-006-9794-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008205152",
"https://doi.org/10.1007/s11263-006-9794-4"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.tics.2006.10.012",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011093935"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1088/1741-2560/1/2/001",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014355603"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.patrec.2008.01.030",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1017355298"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cogbrainres.2003.11.010",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1029041858"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/cercor/bhg111",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032469247"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1145/581571.581573",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1034982779"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cviu.2004.02.004",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040501891"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/b:inrt.0000048491.59134.94",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043889433",
"https://doi.org/10.1023/b:inrt.0000048491.59134.94"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/b:visi.0000029664.99615.94",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1052687286",
"https://doi.org/10.1023/b:visi.0000029664.99615.94"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cub.2007.08.048",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053173106"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.cub.2007.08.048",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053173106"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1162/neco.1992.4.4.590",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053589321"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/34.667881",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061156743"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/78.790663",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061230762"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/tnsre.2006.875550",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061740167"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1613/jair.2005",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1105579395"
],
"type": "CreativeWork"
}
],
"datePublished": "2014-04-01T00:00",
"description": "The subject disclosure relates to a method and system for visual object categorization. The method and system include receiving human inputs including data corresponding to passive human-brain responses to visualization of images. Computer inputs are also received which include data corresponding to outputs from a computerized vision-based processing of the images. The human and computer inputs are processing so as to yield a categorization for the images as a function of the human and computer inputs.
",
"id": "sg:patent.US-8688208-B2",
"keywords": [
"method",
"meshing",
"competency",
"categorization",
"disclosure",
"visual object",
"human input",
"brain response",
"visualization",
"computer",
"output",
"processing"
],
"name": "Method and system for meshing human and computer competencies for object categorization",
"recipient": [
{
"id": "https://www.grid.ac/institutes/grid.419815.0",
"type": "Organization"
}
],
"sameAs": [
"https://app.dimensions.ai/details/patent/US-8688208-B2"
],
"sdDataset": "patents",
"sdDatePublished": "2019-04-18T10:12",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-patents-target-20190320-rc/data/sn-export/402f166718b70575fb5d4ffe01f064d1/0000100128-0000352499/json_export_00754.jsonl",
"type": "Patent"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
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/patent.US-8688208-B2'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/patent.US-8688208-B2'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/patent.US-8688208-B2'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/patent.US-8688208-B2'
This table displays all metadata directly associated to this object as RDF triples.
107 TRIPLES
15 PREDICATES
45 URIs
20 LITERALS
2 BLANK NODES