Hybridization of the Univariate Marginal Distribution Algorithm with Simulated Annealing for Parametric Parabola Detection View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018

AUTHORS

S. Ivvan Valdez , Susana Espinoza-Perez , Fernando Cervantes-Sanchez , Ivan Cruz-Aceves

ABSTRACT

This chapter presents a new hybrid optimization method based on the univariate marginal distribution algorithm for a continuous domain, and the heuristic of simulated annealing for the parabola detection problem. The hybrid proposed method is applied to the DRIVE database of retinal fundus images to approximate the retinal vessels as a parabolic shape. The hybrid method is applied separately using two different objective functions. Firstly, the objective function only considers the superposition of pixels between the target pixels in the input image and the virtual parabola; secondly, the objective function implements a weighted restriction on the pixels close to the parabola vertex. Both objective functions in the hybrid method obtain suitable results to approximate a parabolic form on the retinal vessels present in the retinal images. The experiments show that the parabola detection results obtained from the proposed method are more robust than those obtained by the comparative method. Additionally, the average execution time achieved by the proposed hybrid method (1.57 s) is lower than the computational time obtained by the comparative method on the database of 20 retinal images, which is of interest to computer-aided diagnosis in clinical practice. More... »

PAGES

163-186

Book

TITLE

Hybrid Metaheuristics for Image Analysis

ISBN

978-3-319-77624-8
978-3-319-77625-5

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-77625-5_7

DOI

http://dx.doi.org/10.1007/978-3-319-77625-5_7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1105906641


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": "Universidad de Guanajuato", 
          "id": "https://www.grid.ac/institutes/grid.412891.7", 
          "name": [
            "Universidad de Guanajuato"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Valdez", 
        "givenName": "S. Ivvan", 
        "id": "sg:person.016404271465.10", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016404271465.10"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Universidad del Papaloapan", 
          "id": "https://www.grid.ac/institutes/grid.464700.1", 
          "name": [
            "Universidad del Papaloapan, Ingenier\u00eda en Computaci\u00f3n"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Espinoza-Perez", 
        "givenName": "Susana", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Centro de Investigaci\u00f3n en Matem\u00e1ticas (CIMAT)"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cervantes-Sanchez", 
        "givenName": "Fernando", 
        "id": "sg:person.013444205643.22", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013444205643.22"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "CONACYT, Centro de Investigaci\u00f3n en Matem\u00e1ticas (CIMAT)"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cruz-Aceves", 
        "givenName": "Ivan", 
        "id": "sg:person.01253043473.34", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01253043473.34"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.asoc.2016.01.030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000768520"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ins.2010.12.024", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003806977"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ins.2011.01.024", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004021408"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/cviu.2001.0923", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017243816"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.swevo.2011.08.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018960714"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.eswa.2011.07.063", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019838785"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.compeleceng.2016.05.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022075869"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.patrec.2005.10.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022727530"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.patrec.2005.10.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022727530"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0031-3203(92)90064-p", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026059979"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0031-3203(92)90064-p", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026059979"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10044-010-0183-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033477232", 
          "https://doi.org/10.1007/s10044-010-0183-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10851-005-0482-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034371966", 
          "https://doi.org/10.1007/s10851-005-0482-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10851-005-0482-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034371966", 
          "https://doi.org/10.1007/s10851-005-0482-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.patcog.2008.11.028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036083003"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/361237.361242", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037839065"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0734-189x(88)80033-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038379283"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0031-3203(81)90009-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040477036"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0031-3203(81)90009-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040477036"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11801-009-9071-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040865258", 
          "https://doi.org/10.1007/s11801-009-9071-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11801-009-9071-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040865258", 
          "https://doi.org/10.1007/s11801-009-9071-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0167-8655(90)90042-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047238982"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0167-8655(90)90042-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047238982"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0167-8655(88)90042-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047868061"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0167-8655(88)90042-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047868061"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0262-8856(98)00090-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048303425"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.3182/20130904-3-fr-2041.00213", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048865221"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4615-1539-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049093966", 
          "https://doi.org/10.1007/978-1-4615-1539-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4615-1539-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049093966", 
          "https://doi.org/10.1007/978-1-4615-1539-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijleo.2012.02.045", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051799979"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/29380.29864", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052942382"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/cviu.1993.1043", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054487291"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tim.2012.2192339", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061639125"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tmi.2004.825627", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061694553"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.220.4598.671", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062526985"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1155/2017/6494390", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083891346"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cbms.2001.941749", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094297799"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018", 
    "datePublishedReg": "2018-01-01", 
    "description": "This chapter presents a new hybrid optimization method based on the univariate marginal distribution algorithm for a continuous domain, and the heuristic of simulated annealing for the parabola detection problem. The hybrid proposed method is applied to the DRIVE database of retinal fundus images to approximate the retinal vessels as a parabolic shape. The hybrid method is applied separately using two different objective functions. Firstly, the objective function only considers the superposition of pixels between the target pixels in the input image and the virtual parabola; secondly, the objective function implements a weighted restriction on the pixels close to the parabola vertex. Both objective functions in the hybrid method obtain suitable results to approximate a parabolic form on the retinal vessels present in the retinal images. The experiments show that the parabola detection results obtained from the proposed method are more robust than those obtained by the comparative method. Additionally, the average execution time achieved by the proposed hybrid method (1.57 s) is lower than the computational time obtained by the comparative method on the database of 20 retinal images, which is of interest to computer-aided diagnosis in clinical practice.", 
    "editor": [
      {
        "familyName": "Bhattacharyya", 
        "givenName": "Siddhartha", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-77625-5_7", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-77624-8", 
        "978-3-319-77625-5"
      ], 
      "name": "Hybrid Metaheuristics for Image Analysis", 
      "type": "Book"
    }, 
    "name": "Hybridization of the Univariate Marginal Distribution Algorithm with Simulated Annealing for Parametric Parabola Detection", 
    "pagination": "163-186", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-77625-5_7"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "179e3dea3a263ba97c63d18aabf44efe0d9ec2e0124a3777c8a97661d53ce6fc"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1105906641"
        ]
      }
    ], 
    "publisher": {
      "location": "Cham", 
      "name": "Springer International Publishing", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-77625-5_7", 
      "https://app.dimensions.ai/details/publication/pub.1105906641"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T13:48", 
    "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_8664_00000445.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-3-319-77625-5_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.10.1007/978-3-319-77625-5_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.10.1007/978-3-319-77625-5_7'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-77625-5_7'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-77625-5_7'


 

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

183 TRIPLES      23 PREDICATES      56 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-77625-5_7 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Naee83f6c7feb42d095c43c01339f696f
4 schema:citation sg:pub.10.1007/978-1-4615-1539-5
5 sg:pub.10.1007/s10044-010-0183-9
6 sg:pub.10.1007/s10851-005-0482-8
7 sg:pub.10.1007/s11801-009-9071-1
8 https://doi.org/10.1006/cviu.1993.1043
9 https://doi.org/10.1006/cviu.2001.0923
10 https://doi.org/10.1016/0031-3203(81)90009-1
11 https://doi.org/10.1016/0031-3203(92)90064-p
12 https://doi.org/10.1016/0167-8655(88)90042-6
13 https://doi.org/10.1016/0167-8655(90)90042-z
14 https://doi.org/10.1016/j.asoc.2016.01.030
15 https://doi.org/10.1016/j.compeleceng.2016.05.002
16 https://doi.org/10.1016/j.eswa.2011.07.063
17 https://doi.org/10.1016/j.ijleo.2012.02.045
18 https://doi.org/10.1016/j.ins.2010.12.024
19 https://doi.org/10.1016/j.ins.2011.01.024
20 https://doi.org/10.1016/j.patcog.2008.11.028
21 https://doi.org/10.1016/j.patrec.2005.10.003
22 https://doi.org/10.1016/j.swevo.2011.08.003
23 https://doi.org/10.1016/s0262-8856(98)00090-0
24 https://doi.org/10.1016/s0734-189x(88)80033-1
25 https://doi.org/10.1109/cbms.2001.941749
26 https://doi.org/10.1109/tim.2012.2192339
27 https://doi.org/10.1109/tmi.2004.825627
28 https://doi.org/10.1126/science.220.4598.671
29 https://doi.org/10.1145/29380.29864
30 https://doi.org/10.1145/361237.361242
31 https://doi.org/10.1155/2017/6494390
32 https://doi.org/10.3182/20130904-3-fr-2041.00213
33 schema:datePublished 2018
34 schema:datePublishedReg 2018-01-01
35 schema:description This chapter presents a new hybrid optimization method based on the univariate marginal distribution algorithm for a continuous domain, and the heuristic of simulated annealing for the parabola detection problem. The hybrid proposed method is applied to the DRIVE database of retinal fundus images to approximate the retinal vessels as a parabolic shape. The hybrid method is applied separately using two different objective functions. Firstly, the objective function only considers the superposition of pixels between the target pixels in the input image and the virtual parabola; secondly, the objective function implements a weighted restriction on the pixels close to the parabola vertex. Both objective functions in the hybrid method obtain suitable results to approximate a parabolic form on the retinal vessels present in the retinal images. The experiments show that the parabola detection results obtained from the proposed method are more robust than those obtained by the comparative method. Additionally, the average execution time achieved by the proposed hybrid method (1.57 s) is lower than the computational time obtained by the comparative method on the database of 20 retinal images, which is of interest to computer-aided diagnosis in clinical practice.
36 schema:editor Ne329a73e48df4065bd83bb443e7bdbda
37 schema:genre chapter
38 schema:inLanguage en
39 schema:isAccessibleForFree false
40 schema:isPartOf N19e27ba6288447e2845d44e5cd593374
41 schema:name Hybridization of the Univariate Marginal Distribution Algorithm with Simulated Annealing for Parametric Parabola Detection
42 schema:pagination 163-186
43 schema:productId N2b281549b0b24f03881cd8430e96a3ce
44 Na719afd398a24c2e9c6ec90a0cab91d5
45 Nbff793bc02a3443fbe26e98435b8483a
46 schema:publisher Na0b3aa7a8b4c46d2865fb7a8003afb7d
47 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105906641
48 https://doi.org/10.1007/978-3-319-77625-5_7
49 schema:sdDatePublished 2019-04-15T13:48
50 schema:sdLicense https://scigraph.springernature.com/explorer/license/
51 schema:sdPublisher N2d38aa37b21a47eca9ffddefbd8a8fc3
52 schema:url http://link.springer.com/10.1007/978-3-319-77625-5_7
53 sgo:license sg:explorer/license/
54 sgo:sdDataset chapters
55 rdf:type schema:Chapter
56 N13deaa410c5543b8922196fbeb33d47e schema:name Centro de Investigación en Matemáticas (CIMAT)
57 rdf:type schema:Organization
58 N19e27ba6288447e2845d44e5cd593374 schema:isbn 978-3-319-77624-8
59 978-3-319-77625-5
60 schema:name Hybrid Metaheuristics for Image Analysis
61 rdf:type schema:Book
62 N2b281549b0b24f03881cd8430e96a3ce schema:name doi
63 schema:value 10.1007/978-3-319-77625-5_7
64 rdf:type schema:PropertyValue
65 N2d38aa37b21a47eca9ffddefbd8a8fc3 schema:name Springer Nature - SN SciGraph project
66 rdf:type schema:Organization
67 N7708309178204ed1a67fa1f7f9c1b62b rdf:first Nd4a9b31969de468099d340771aaa61e5
68 rdf:rest N9d6becb27f874c5f848a0aa246052cd7
69 N9d6becb27f874c5f848a0aa246052cd7 rdf:first sg:person.013444205643.22
70 rdf:rest Nafe6b17248464e86a1096c98c336e4a0
71 Na0b3aa7a8b4c46d2865fb7a8003afb7d schema:location Cham
72 schema:name Springer International Publishing
73 rdf:type schema:Organisation
74 Na719afd398a24c2e9c6ec90a0cab91d5 schema:name readcube_id
75 schema:value 179e3dea3a263ba97c63d18aabf44efe0d9ec2e0124a3777c8a97661d53ce6fc
76 rdf:type schema:PropertyValue
77 Nae4072aa90cb45c39721c744c143a957 schema:familyName Bhattacharyya
78 schema:givenName Siddhartha
79 rdf:type schema:Person
80 Naee83f6c7feb42d095c43c01339f696f rdf:first sg:person.016404271465.10
81 rdf:rest N7708309178204ed1a67fa1f7f9c1b62b
82 Nafe6b17248464e86a1096c98c336e4a0 rdf:first sg:person.01253043473.34
83 rdf:rest rdf:nil
84 Nbff793bc02a3443fbe26e98435b8483a schema:name dimensions_id
85 schema:value pub.1105906641
86 rdf:type schema:PropertyValue
87 Nd4a9b31969de468099d340771aaa61e5 schema:affiliation https://www.grid.ac/institutes/grid.464700.1
88 schema:familyName Espinoza-Perez
89 schema:givenName Susana
90 rdf:type schema:Person
91 Ne329a73e48df4065bd83bb443e7bdbda rdf:first Nae4072aa90cb45c39721c744c143a957
92 rdf:rest rdf:nil
93 Ne610d27bdf814991b58e982b438238ee schema:name CONACYT, Centro de Investigación en Matemáticas (CIMAT)
94 rdf:type schema:Organization
95 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
96 schema:name Information and Computing Sciences
97 rdf:type schema:DefinedTerm
98 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
99 schema:name Artificial Intelligence and Image Processing
100 rdf:type schema:DefinedTerm
101 sg:person.01253043473.34 schema:affiliation Ne610d27bdf814991b58e982b438238ee
102 schema:familyName Cruz-Aceves
103 schema:givenName Ivan
104 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01253043473.34
105 rdf:type schema:Person
106 sg:person.013444205643.22 schema:affiliation N13deaa410c5543b8922196fbeb33d47e
107 schema:familyName Cervantes-Sanchez
108 schema:givenName Fernando
109 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013444205643.22
110 rdf:type schema:Person
111 sg:person.016404271465.10 schema:affiliation https://www.grid.ac/institutes/grid.412891.7
112 schema:familyName Valdez
113 schema:givenName S. Ivvan
114 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016404271465.10
115 rdf:type schema:Person
116 sg:pub.10.1007/978-1-4615-1539-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049093966
117 https://doi.org/10.1007/978-1-4615-1539-5
118 rdf:type schema:CreativeWork
119 sg:pub.10.1007/s10044-010-0183-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033477232
120 https://doi.org/10.1007/s10044-010-0183-9
121 rdf:type schema:CreativeWork
122 sg:pub.10.1007/s10851-005-0482-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034371966
123 https://doi.org/10.1007/s10851-005-0482-8
124 rdf:type schema:CreativeWork
125 sg:pub.10.1007/s11801-009-9071-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040865258
126 https://doi.org/10.1007/s11801-009-9071-1
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1006/cviu.1993.1043 schema:sameAs https://app.dimensions.ai/details/publication/pub.1054487291
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1006/cviu.2001.0923 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017243816
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1016/0031-3203(81)90009-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040477036
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1016/0031-3203(92)90064-p schema:sameAs https://app.dimensions.ai/details/publication/pub.1026059979
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1016/0167-8655(88)90042-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047868061
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1016/0167-8655(90)90042-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1047238982
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1016/j.asoc.2016.01.030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000768520
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1016/j.compeleceng.2016.05.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022075869
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1016/j.eswa.2011.07.063 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019838785
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1016/j.ijleo.2012.02.045 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051799979
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1016/j.ins.2010.12.024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003806977
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1016/j.ins.2011.01.024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004021408
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1016/j.patcog.2008.11.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036083003
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1016/j.patrec.2005.10.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022727530
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1016/j.swevo.2011.08.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018960714
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1016/s0262-8856(98)00090-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048303425
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1016/s0734-189x(88)80033-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038379283
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1109/cbms.2001.941749 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094297799
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1109/tim.2012.2192339 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061639125
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1109/tmi.2004.825627 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061694553
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1126/science.220.4598.671 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062526985
169 rdf:type schema:CreativeWork
170 https://doi.org/10.1145/29380.29864 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052942382
171 rdf:type schema:CreativeWork
172 https://doi.org/10.1145/361237.361242 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037839065
173 rdf:type schema:CreativeWork
174 https://doi.org/10.1155/2017/6494390 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083891346
175 rdf:type schema:CreativeWork
176 https://doi.org/10.3182/20130904-3-fr-2041.00213 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048865221
177 rdf:type schema:CreativeWork
178 https://www.grid.ac/institutes/grid.412891.7 schema:alternateName Universidad de Guanajuato
179 schema:name Universidad de Guanajuato
180 rdf:type schema:Organization
181 https://www.grid.ac/institutes/grid.464700.1 schema:alternateName Universidad del Papaloapan
182 schema:name Universidad del Papaloapan, Ingeniería en Computación
183 rdf:type schema:Organization
 




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


...