Contour continuity in region based image segmentation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

1998

AUTHORS

Thomas Leung , Jitendra Malik

ABSTRACT

Region-based image segmentation techniques make use of similarity in intensity, color and texture to determine the partitioning of an image. The powerful cue of contour continuity is not exploited at all. In this paper, we provide a way of incorporating curvilinear grouping into region-based image segmentation. Soft contour information is obtained through orientation energy. Weak contrast gaps and subjective contours are completed by contour propagation. The normalized cut approach proposed by Shi and Malik is used for the segmentation. Results on a large variety of images are shown. More... »

PAGES

544-559

Book

TITLE

Computer Vision — ECCV'98

ISBN

978-3-540-64569-6
978-3-540-69354-3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bfb0055689

DOI

http://dx.doi.org/10.1007/bfb0055689

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1047873554


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": "University of California, Berkeley", 
          "id": "https://www.grid.ac/institutes/grid.47840.3f", 
          "name": [
            "Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, 94720\u00a0Berkeley, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Leung", 
        "givenName": "Thomas", 
        "id": "sg:person.016034550437.98", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016034550437.98"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of California, Berkeley", 
          "id": "https://www.grid.ac/institutes/grid.47840.3f", 
          "name": [
            "Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, 94720\u00a0Berkeley, CA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Malik", 
        "givenName": "Jitendra", 
        "id": "sg:person.01364521761.84", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01364521761.84"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "1998", 
    "datePublishedReg": "1998-01-01", 
    "description": "Region-based image segmentation techniques make use of similarity in intensity, color and texture to determine the partitioning of an image. The powerful cue of contour continuity is not exploited at all. In this paper, we provide a way of incorporating curvilinear grouping into region-based image segmentation. Soft contour information is obtained through orientation energy. Weak contrast gaps and subjective contours are completed by contour propagation. The normalized cut approach proposed by Shi and Malik is used for the segmentation. Results on a large variety of images are shown.", 
    "editor": [
      {
        "familyName": "Burkhardt", 
        "givenName": "Hans", 
        "type": "Person"
      }, 
      {
        "familyName": "Neumann", 
        "givenName": "Bernd", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/bfb0055689", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-540-64569-6", 
        "978-3-540-69354-3"
      ], 
      "name": "Computer Vision \u2014 ECCV'98", 
      "type": "Book"
    }, 
    "name": "Contour continuity in region based image segmentation", 
    "pagination": "544-559", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bfb0055689"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "e40eea7a1a27658159f0d35913d1879779ec0f4c7e19b09477007d4669c1d33a"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1047873554"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/bfb0055689", 
      "https://app.dimensions.ai/details/publication/pub.1047873554"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T17:03", 
    "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_8678_00000082.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/BFb0055689"
  }
]
 

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/bfb0055689'

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/bfb0055689'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bfb0055689'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bfb0055689'


 

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

77 TRIPLES      22 PREDICATES      27 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bfb0055689 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N75a847fec05447828f9b0826d16f2044
4 schema:datePublished 1998
5 schema:datePublishedReg 1998-01-01
6 schema:description Region-based image segmentation techniques make use of similarity in intensity, color and texture to determine the partitioning of an image. The powerful cue of contour continuity is not exploited at all. In this paper, we provide a way of incorporating curvilinear grouping into region-based image segmentation. Soft contour information is obtained through orientation energy. Weak contrast gaps and subjective contours are completed by contour propagation. The normalized cut approach proposed by Shi and Malik is used for the segmentation. Results on a large variety of images are shown.
7 schema:editor N0a70f07c17024b96a375cb8f943922e3
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree true
11 schema:isPartOf N01b759167c214625b0fc963cdb86da87
12 schema:name Contour continuity in region based image segmentation
13 schema:pagination 544-559
14 schema:productId N08e5e15d179c40c99f272deef2369892
15 N1782f689e34c42ec8b409b13128331b1
16 Nde21f3fa8e4341eb8987fe84bc6a2dc4
17 schema:publisher N40f9cb43bf3e470dbddd484a9ca9aab1
18 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047873554
19 https://doi.org/10.1007/bfb0055689
20 schema:sdDatePublished 2019-04-15T17:03
21 schema:sdLicense https://scigraph.springernature.com/explorer/license/
22 schema:sdPublisher N1053b2b58e42486eb033f31cc2cf97d9
23 schema:url http://link.springer.com/10.1007/BFb0055689
24 sgo:license sg:explorer/license/
25 sgo:sdDataset chapters
26 rdf:type schema:Chapter
27 N01b759167c214625b0fc963cdb86da87 schema:isbn 978-3-540-64569-6
28 978-3-540-69354-3
29 schema:name Computer Vision — ECCV'98
30 rdf:type schema:Book
31 N08e5e15d179c40c99f272deef2369892 schema:name readcube_id
32 schema:value e40eea7a1a27658159f0d35913d1879779ec0f4c7e19b09477007d4669c1d33a
33 rdf:type schema:PropertyValue
34 N0a70f07c17024b96a375cb8f943922e3 rdf:first Na8ab317700424a6d89acd6741bc99237
35 rdf:rest N6cb747c27b0d431ca638874c514ab8da
36 N1053b2b58e42486eb033f31cc2cf97d9 schema:name Springer Nature - SN SciGraph project
37 rdf:type schema:Organization
38 N1782f689e34c42ec8b409b13128331b1 schema:name doi
39 schema:value 10.1007/bfb0055689
40 rdf:type schema:PropertyValue
41 N40f9cb43bf3e470dbddd484a9ca9aab1 schema:location Berlin, Heidelberg
42 schema:name Springer Berlin Heidelberg
43 rdf:type schema:Organisation
44 N6cb747c27b0d431ca638874c514ab8da rdf:first Ne1f31ccb350841aab800c64e7604609f
45 rdf:rest rdf:nil
46 N75a847fec05447828f9b0826d16f2044 rdf:first sg:person.016034550437.98
47 rdf:rest N77b08e71de1a4a05a695b9ba4eca6bf0
48 N77b08e71de1a4a05a695b9ba4eca6bf0 rdf:first sg:person.01364521761.84
49 rdf:rest rdf:nil
50 Na8ab317700424a6d89acd6741bc99237 schema:familyName Burkhardt
51 schema:givenName Hans
52 rdf:type schema:Person
53 Nde21f3fa8e4341eb8987fe84bc6a2dc4 schema:name dimensions_id
54 schema:value pub.1047873554
55 rdf:type schema:PropertyValue
56 Ne1f31ccb350841aab800c64e7604609f schema:familyName Neumann
57 schema:givenName Bernd
58 rdf:type schema:Person
59 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
60 schema:name Information and Computing Sciences
61 rdf:type schema:DefinedTerm
62 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
63 schema:name Artificial Intelligence and Image Processing
64 rdf:type schema:DefinedTerm
65 sg:person.01364521761.84 schema:affiliation https://www.grid.ac/institutes/grid.47840.3f
66 schema:familyName Malik
67 schema:givenName Jitendra
68 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01364521761.84
69 rdf:type schema:Person
70 sg:person.016034550437.98 schema:affiliation https://www.grid.ac/institutes/grid.47840.3f
71 schema:familyName Leung
72 schema:givenName Thomas
73 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016034550437.98
74 rdf:type schema:Person
75 https://www.grid.ac/institutes/grid.47840.3f schema:alternateName University of California, Berkeley
76 schema:name Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, 94720 Berkeley, CA, USA
77 rdf:type schema:Organization
 




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


...