Blur Insensitive Texture Classification Using Local Phase Quantization View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2008

AUTHORS

Ville Ojansivu , Janne Heikkilä

ABSTRACT

In this paper, we propose a new descriptor for texture classification that is robust to image blurring. The descriptor utilizes phase information computed locally in a window for every image position. The phases of the four low-frequency coefficients are decorrelated and uniformly quantized in an eight-dimensional space. A histogram of the resulting code words is created and used as a feature in texture classification. Ideally, the low-frequency phase components are shown to be invariant to centrally symmetric blur. Although this ideal invariance is not completely achieved due to the finite window size, the method is still highly insensitive to blur. Because only phase information is used, the method is also invariant to uniform illumination changes. According to our experiments, the classification accuracy of blurred texture images is much higher with the new method than with the well-known LBP or Gabor filter bank methods. Interestingly, it is also slightly better for textures that are not blurred. More... »

PAGES

236-243

Book

TITLE

Image and Signal Processing

ISBN

978-3-540-69904-0
978-3-540-69905-7

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-69905-7_27

DOI

http://dx.doi.org/10.1007/978-3-540-69905-7_27

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1009665193


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 Oulu", 
          "id": "https://www.grid.ac/institutes/grid.10858.34", 
          "name": [
            "Machine Vision Group, Department of Electrical and Information Engineering, University of Oulu, 90014, PO Box 4500, Finland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ojansivu", 
        "givenName": "Ville", 
        "id": "sg:person.07602604505.05", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07602604505.05"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Oulu", 
          "id": "https://www.grid.ac/institutes/grid.10858.34", 
          "name": [
            "Machine Vision Group, Department of Electrical and Information Engineering, University of Oulu, 90014, PO Box 4500, Finland"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Heikkil\u00e4", 
        "givenName": "Janne", 
        "id": "sg:person.07474155162.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07474155162.31"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/0031-3203(95)00067-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035783933"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/34.531803", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061156442"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/34.683773", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061156822"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/34.761261", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061156940"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/79.581363", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061231975"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tip.2003.812327", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061640864"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/tpami.2002.1017623", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061742396"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1142/9789812384737_0007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1088714000"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iciap.2007.4362840", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093653073"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icpr.2002.1044854", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094202252"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/icip.2007.4379954", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095687705"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2008", 
    "datePublishedReg": "2008-01-01", 
    "description": "In this paper, we propose a new descriptor for texture classification that is robust to image blurring. The descriptor utilizes phase information computed locally in a window for every image position. The phases of the four low-frequency coefficients are decorrelated and uniformly quantized in an eight-dimensional space. A histogram of the resulting code words is created and used as a feature in texture classification. Ideally, the low-frequency phase components are shown to be invariant to centrally symmetric blur. Although this ideal invariance is not completely achieved due to the finite window size, the method is still highly insensitive to blur. Because only phase information is used, the method is also invariant to uniform illumination changes. According to our experiments, the classification accuracy of blurred texture images is much higher with the new method than with the well-known LBP or Gabor filter bank methods. Interestingly, it is also slightly better for textures that are not blurred.", 
    "editor": [
      {
        "familyName": "Elmoataz", 
        "givenName": "Abderrahim", 
        "type": "Person"
      }, 
      {
        "familyName": "Lezoray", 
        "givenName": "Olivier", 
        "type": "Person"
      }, 
      {
        "familyName": "Nouboud", 
        "givenName": "Fathallah", 
        "type": "Person"
      }, 
      {
        "familyName": "Mammass", 
        "givenName": "Driss", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-540-69905-7_27", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-540-69904-0", 
        "978-3-540-69905-7"
      ], 
      "name": "Image and Signal Processing", 
      "type": "Book"
    }, 
    "name": "Blur Insensitive Texture Classification Using Local Phase Quantization", 
    "pagination": "236-243", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1009665193"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-540-69905-7_27"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "2e0afd06d1038544f9e7678c4b1c1054ecd3571fd09745fd1b9b09fbb0393f45"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-540-69905-7_27", 
      "https://app.dimensions.ai/details/publication/pub.1009665193"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-16T08:31", 
    "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/v90/0000000364_0000000364/records_72847_00000000.jsonl", 
    "type": "Chapter", 
    "url": "https://link.springer.com/10.1007%2F978-3-540-69905-7_27"
  }
]
 

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.10.1007/978-3-540-69905-7_27'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-69905-7_27'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-69905-7_27'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-69905-7_27'


 

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

120 TRIPLES      23 PREDICATES      38 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-540-69905-7_27 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Nada02f0981b6424bb6515eb45e6725ec
4 schema:citation https://doi.org/10.1016/0031-3203(95)00067-4
5 https://doi.org/10.1109/34.531803
6 https://doi.org/10.1109/34.683773
7 https://doi.org/10.1109/34.761261
8 https://doi.org/10.1109/79.581363
9 https://doi.org/10.1109/iciap.2007.4362840
10 https://doi.org/10.1109/icip.2007.4379954
11 https://doi.org/10.1109/icpr.2002.1044854
12 https://doi.org/10.1109/tip.2003.812327
13 https://doi.org/10.1109/tpami.2002.1017623
14 https://doi.org/10.1142/9789812384737_0007
15 schema:datePublished 2008
16 schema:datePublishedReg 2008-01-01
17 schema:description In this paper, we propose a new descriptor for texture classification that is robust to image blurring. The descriptor utilizes phase information computed locally in a window for every image position. The phases of the four low-frequency coefficients are decorrelated and uniformly quantized in an eight-dimensional space. A histogram of the resulting code words is created and used as a feature in texture classification. Ideally, the low-frequency phase components are shown to be invariant to centrally symmetric blur. Although this ideal invariance is not completely achieved due to the finite window size, the method is still highly insensitive to blur. Because only phase information is used, the method is also invariant to uniform illumination changes. According to our experiments, the classification accuracy of blurred texture images is much higher with the new method than with the well-known LBP or Gabor filter bank methods. Interestingly, it is also slightly better for textures that are not blurred.
18 schema:editor Nb1bb0fa25e14494999ce76c7f85b63cd
19 schema:genre chapter
20 schema:inLanguage en
21 schema:isAccessibleForFree true
22 schema:isPartOf N937b69e5f5704210a98708d783a83fc7
23 schema:name Blur Insensitive Texture Classification Using Local Phase Quantization
24 schema:pagination 236-243
25 schema:productId N74f16929018e4d6c952ae80827997dc2
26 Na86ad2e3d916420db524e6761b722328
27 Nb58c01ffd83b4af980152831f1ffd717
28 schema:publisher N6bed56b6f64e4ee8a470989dc8d59e15
29 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009665193
30 https://doi.org/10.1007/978-3-540-69905-7_27
31 schema:sdDatePublished 2019-04-16T08:31
32 schema:sdLicense https://scigraph.springernature.com/explorer/license/
33 schema:sdPublisher Ne2d56b7c385c4e90ad912a3ff6c55f77
34 schema:url https://link.springer.com/10.1007%2F978-3-540-69905-7_27
35 sgo:license sg:explorer/license/
36 sgo:sdDataset chapters
37 rdf:type schema:Chapter
38 N146ba2305e0d4fdeb76649f21bf6387b schema:familyName Mammass
39 schema:givenName Driss
40 rdf:type schema:Person
41 N16993c0cf5b043e39f8d7b95f33cccd3 schema:familyName Elmoataz
42 schema:givenName Abderrahim
43 rdf:type schema:Person
44 N19d3a850ad2b48eca73a422d7770e7ec rdf:first N146ba2305e0d4fdeb76649f21bf6387b
45 rdf:rest rdf:nil
46 N1c2c3844161a471b83afadbda8aca2c5 rdf:first Nda410cea2e1140349dec2a3b1444d469
47 rdf:rest N55e3cdf8da20451590f0dffc6ce4995a
48 N55e3cdf8da20451590f0dffc6ce4995a rdf:first Na4df83896aa6443e99259054bed5e498
49 rdf:rest N19d3a850ad2b48eca73a422d7770e7ec
50 N6bed56b6f64e4ee8a470989dc8d59e15 schema:location Berlin, Heidelberg
51 schema:name Springer Berlin Heidelberg
52 rdf:type schema:Organisation
53 N74f16929018e4d6c952ae80827997dc2 schema:name dimensions_id
54 schema:value pub.1009665193
55 rdf:type schema:PropertyValue
56 N937b69e5f5704210a98708d783a83fc7 schema:isbn 978-3-540-69904-0
57 978-3-540-69905-7
58 schema:name Image and Signal Processing
59 rdf:type schema:Book
60 Na4df83896aa6443e99259054bed5e498 schema:familyName Nouboud
61 schema:givenName Fathallah
62 rdf:type schema:Person
63 Na86ad2e3d916420db524e6761b722328 schema:name readcube_id
64 schema:value 2e0afd06d1038544f9e7678c4b1c1054ecd3571fd09745fd1b9b09fbb0393f45
65 rdf:type schema:PropertyValue
66 Nada02f0981b6424bb6515eb45e6725ec rdf:first sg:person.07602604505.05
67 rdf:rest Nb98d256c1c4a43ee813afef8ce9e85e5
68 Nb1bb0fa25e14494999ce76c7f85b63cd rdf:first N16993c0cf5b043e39f8d7b95f33cccd3
69 rdf:rest N1c2c3844161a471b83afadbda8aca2c5
70 Nb58c01ffd83b4af980152831f1ffd717 schema:name doi
71 schema:value 10.1007/978-3-540-69905-7_27
72 rdf:type schema:PropertyValue
73 Nb98d256c1c4a43ee813afef8ce9e85e5 rdf:first sg:person.07474155162.31
74 rdf:rest rdf:nil
75 Nda410cea2e1140349dec2a3b1444d469 schema:familyName Lezoray
76 schema:givenName Olivier
77 rdf:type schema:Person
78 Ne2d56b7c385c4e90ad912a3ff6c55f77 schema:name Springer Nature - SN SciGraph project
79 rdf:type schema:Organization
80 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
81 schema:name Information and Computing Sciences
82 rdf:type schema:DefinedTerm
83 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
84 schema:name Artificial Intelligence and Image Processing
85 rdf:type schema:DefinedTerm
86 sg:person.07474155162.31 schema:affiliation https://www.grid.ac/institutes/grid.10858.34
87 schema:familyName Heikkilä
88 schema:givenName Janne
89 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07474155162.31
90 rdf:type schema:Person
91 sg:person.07602604505.05 schema:affiliation https://www.grid.ac/institutes/grid.10858.34
92 schema:familyName Ojansivu
93 schema:givenName Ville
94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07602604505.05
95 rdf:type schema:Person
96 https://doi.org/10.1016/0031-3203(95)00067-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035783933
97 rdf:type schema:CreativeWork
98 https://doi.org/10.1109/34.531803 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061156442
99 rdf:type schema:CreativeWork
100 https://doi.org/10.1109/34.683773 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061156822
101 rdf:type schema:CreativeWork
102 https://doi.org/10.1109/34.761261 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061156940
103 rdf:type schema:CreativeWork
104 https://doi.org/10.1109/79.581363 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061231975
105 rdf:type schema:CreativeWork
106 https://doi.org/10.1109/iciap.2007.4362840 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093653073
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1109/icip.2007.4379954 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095687705
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1109/icpr.2002.1044854 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094202252
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1109/tip.2003.812327 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061640864
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1109/tpami.2002.1017623 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061742396
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1142/9789812384737_0007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1088714000
117 rdf:type schema:CreativeWork
118 https://www.grid.ac/institutes/grid.10858.34 schema:alternateName University of Oulu
119 schema:name Machine Vision Group, Department of Electrical and Information Engineering, University of Oulu, 90014, PO Box 4500, Finland
120 rdf:type schema:Organization
 




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


...