Image Segmentation Based on the Evaluation of the Tendency of Image Elements to form Clusters with the Help of Point Field Characteristics
A new approach to the segmentation of an image is proposed on the basis of modeling the spatial distribution of points in the image plane and their ability to identify clusters. Based on detected histogram peaks, a sequence of dominant brightness values (brightnesses) is formed for each fragment of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kosarevych, R J [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2015 |
|---|
| Rechteinformationen: |
Nutzungsrecht: © Springer Science+Business Media New York 2015 |
|---|
| Schlagwörter: |
Artificial Intelligence (incl. Robotics) |
|---|
| Übergeordnetes Werk: |
Enthalten in: Cybernetics and systems analysis - New York, NY [u.a.] : Consultants Bureau, 1992, 51(2015), 5, Seite 704-713 |
|---|---|
| Übergeordnetes Werk: |
volume:51 ; year:2015 ; number:5 ; pages:704-713 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10559-015-9762-5 |
|---|
| Katalog-ID: |
OLC1964327113 |
|---|
| LEADER | 01000caa a2200265 4500 | ||
|---|---|---|---|
| 001 | OLC1964327113 | ||
| 003 | DE-627 | ||
| 005 | 20230714162258.0 | ||
| 007 | tu | ||
| 008 | 160206s2015 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10559-015-9762-5 |2 doi | |
| 028 | 5 | 2 | |a PQ20160617 |
| 035 | |a (DE-627)OLC1964327113 | ||
| 035 | |a (DE-599)GBVOLC1964327113 | ||
| 035 | |a (PRQ)s612-c72b3f623ee20c81af485208d9790d91a9ce04b986a2bcf7ead0c4a8b961a20b0 | ||
| 035 | |a (KEY)0029269920150000051000500704imagesegmentationbasedontheevaluationofthetendency | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 000 |q DNB |
| 100 | 1 | |a Kosarevych, R J |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Image Segmentation Based on the Evaluation of the Tendency of Image Elements to form Clusters with the Help of Point Field Characteristics |
| 264 | 1 | |c 2015 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 520 | |a A new approach to the segmentation of an image is proposed on the basis of modeling the spatial distribution of points in the image plane and their ability to identify clusters. Based on detected histogram peaks, a sequence of dominant brightness values (brightnesses) is formed for each fragment of the image. Point fields are formed for each image brightness and the presence of clusters is checked with the help of second-order characteristics of these point fields. The union of all the brightnesses for which point fields form clusters forms the object of segmentation. The results of segmentation of several images are given as compared with those of the thresholding and seed region growing methods. | ||
| 540 | |a Nutzungsrecht: © Springer Science+Business Media New York 2015 | ||
| 650 | 4 | |a spatial distribution | |
| 650 | 4 | |a point field | |
| 650 | 4 | |a Processor Architectures | |
| 650 | 4 | |a Artificial Intelligence (incl. Robotics) | |
| 650 | 4 | |a Software Engineering/Programming and Operating Systems | |
| 650 | 4 | |a Systems Theory, Control | |
| 650 | 4 | |a clusterization | |
| 650 | 4 | |a local extremum | |
| 650 | 4 | |a image segmentation | |
| 650 | 4 | |a Mathematics | |
| 700 | 1 | |a Rusyn, B P |4 oth | |
| 700 | 1 | |a Korniy, V V |4 oth | |
| 700 | 1 | |a Kerod, T I |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |t Cybernetics and systems analysis |d New York, NY [u.a.] : Consultants Bureau, 1992 |g 51(2015), 5, Seite 704-713 |w (DE-627)131081225 |w (DE-600)1112963-3 |w (DE-576)029167868 |x 1060-0396 |7 nnns |
| 773 | 1 | 8 | |g volume:51 |g year:2015 |g number:5 |g pages:704-713 |
| 856 | 4 | 1 | |u http://dx.doi.org/10.1007/s10559-015-9762-5 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a SSG-OPC-MAT | ||
| 912 | |a GBV_ILN_70 | ||
| 951 | |a AR | ||
| 952 | |d 51 |j 2015 |e 5 |h 704-713 | ||
| author_variant |
r j k rj rjk |
|---|---|
| matchkey_str |
article:10600396:2015----::mgsgettobsdnheautooteednyfmgeeettfrcutrwtte |
| hierarchy_sort_str |
2015 |
| publishDate |
2015 |
| allfields |
10.1007/s10559-015-9762-5 doi PQ20160617 (DE-627)OLC1964327113 (DE-599)GBVOLC1964327113 (PRQ)s612-c72b3f623ee20c81af485208d9790d91a9ce04b986a2bcf7ead0c4a8b961a20b0 (KEY)0029269920150000051000500704imagesegmentationbasedontheevaluationofthetendency DE-627 ger DE-627 rakwb eng 000 DNB Kosarevych, R J verfasserin aut Image Segmentation Based on the Evaluation of the Tendency of Image Elements to form Clusters with the Help of Point Field Characteristics 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A new approach to the segmentation of an image is proposed on the basis of modeling the spatial distribution of points in the image plane and their ability to identify clusters. Based on detected histogram peaks, a sequence of dominant brightness values (brightnesses) is formed for each fragment of the image. Point fields are formed for each image brightness and the presence of clusters is checked with the help of second-order characteristics of these point fields. The union of all the brightnesses for which point fields form clusters forms the object of segmentation. The results of segmentation of several images are given as compared with those of the thresholding and seed region growing methods. Nutzungsrecht: © Springer Science+Business Media New York 2015 spatial distribution point field Processor Architectures Artificial Intelligence (incl. Robotics) Software Engineering/Programming and Operating Systems Systems Theory, Control clusterization local extremum image segmentation Mathematics Rusyn, B P oth Korniy, V V oth Kerod, T I oth Enthalten in Cybernetics and systems analysis New York, NY [u.a.] : Consultants Bureau, 1992 51(2015), 5, Seite 704-713 (DE-627)131081225 (DE-600)1112963-3 (DE-576)029167868 1060-0396 nnns volume:51 year:2015 number:5 pages:704-713 http://dx.doi.org/10.1007/s10559-015-9762-5 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 AR 51 2015 5 704-713 |
| spelling |
10.1007/s10559-015-9762-5 doi PQ20160617 (DE-627)OLC1964327113 (DE-599)GBVOLC1964327113 (PRQ)s612-c72b3f623ee20c81af485208d9790d91a9ce04b986a2bcf7ead0c4a8b961a20b0 (KEY)0029269920150000051000500704imagesegmentationbasedontheevaluationofthetendency DE-627 ger DE-627 rakwb eng 000 DNB Kosarevych, R J verfasserin aut Image Segmentation Based on the Evaluation of the Tendency of Image Elements to form Clusters with the Help of Point Field Characteristics 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A new approach to the segmentation of an image is proposed on the basis of modeling the spatial distribution of points in the image plane and their ability to identify clusters. Based on detected histogram peaks, a sequence of dominant brightness values (brightnesses) is formed for each fragment of the image. Point fields are formed for each image brightness and the presence of clusters is checked with the help of second-order characteristics of these point fields. The union of all the brightnesses for which point fields form clusters forms the object of segmentation. The results of segmentation of several images are given as compared with those of the thresholding and seed region growing methods. Nutzungsrecht: © Springer Science+Business Media New York 2015 spatial distribution point field Processor Architectures Artificial Intelligence (incl. Robotics) Software Engineering/Programming and Operating Systems Systems Theory, Control clusterization local extremum image segmentation Mathematics Rusyn, B P oth Korniy, V V oth Kerod, T I oth Enthalten in Cybernetics and systems analysis New York, NY [u.a.] : Consultants Bureau, 1992 51(2015), 5, Seite 704-713 (DE-627)131081225 (DE-600)1112963-3 (DE-576)029167868 1060-0396 nnns volume:51 year:2015 number:5 pages:704-713 http://dx.doi.org/10.1007/s10559-015-9762-5 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 AR 51 2015 5 704-713 |
| allfields_unstemmed |
10.1007/s10559-015-9762-5 doi PQ20160617 (DE-627)OLC1964327113 (DE-599)GBVOLC1964327113 (PRQ)s612-c72b3f623ee20c81af485208d9790d91a9ce04b986a2bcf7ead0c4a8b961a20b0 (KEY)0029269920150000051000500704imagesegmentationbasedontheevaluationofthetendency DE-627 ger DE-627 rakwb eng 000 DNB Kosarevych, R J verfasserin aut Image Segmentation Based on the Evaluation of the Tendency of Image Elements to form Clusters with the Help of Point Field Characteristics 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A new approach to the segmentation of an image is proposed on the basis of modeling the spatial distribution of points in the image plane and their ability to identify clusters. Based on detected histogram peaks, a sequence of dominant brightness values (brightnesses) is formed for each fragment of the image. Point fields are formed for each image brightness and the presence of clusters is checked with the help of second-order characteristics of these point fields. The union of all the brightnesses for which point fields form clusters forms the object of segmentation. The results of segmentation of several images are given as compared with those of the thresholding and seed region growing methods. Nutzungsrecht: © Springer Science+Business Media New York 2015 spatial distribution point field Processor Architectures Artificial Intelligence (incl. Robotics) Software Engineering/Programming and Operating Systems Systems Theory, Control clusterization local extremum image segmentation Mathematics Rusyn, B P oth Korniy, V V oth Kerod, T I oth Enthalten in Cybernetics and systems analysis New York, NY [u.a.] : Consultants Bureau, 1992 51(2015), 5, Seite 704-713 (DE-627)131081225 (DE-600)1112963-3 (DE-576)029167868 1060-0396 nnns volume:51 year:2015 number:5 pages:704-713 http://dx.doi.org/10.1007/s10559-015-9762-5 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 AR 51 2015 5 704-713 |
| allfieldsGer |
10.1007/s10559-015-9762-5 doi PQ20160617 (DE-627)OLC1964327113 (DE-599)GBVOLC1964327113 (PRQ)s612-c72b3f623ee20c81af485208d9790d91a9ce04b986a2bcf7ead0c4a8b961a20b0 (KEY)0029269920150000051000500704imagesegmentationbasedontheevaluationofthetendency DE-627 ger DE-627 rakwb eng 000 DNB Kosarevych, R J verfasserin aut Image Segmentation Based on the Evaluation of the Tendency of Image Elements to form Clusters with the Help of Point Field Characteristics 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A new approach to the segmentation of an image is proposed on the basis of modeling the spatial distribution of points in the image plane and their ability to identify clusters. Based on detected histogram peaks, a sequence of dominant brightness values (brightnesses) is formed for each fragment of the image. Point fields are formed for each image brightness and the presence of clusters is checked with the help of second-order characteristics of these point fields. The union of all the brightnesses for which point fields form clusters forms the object of segmentation. The results of segmentation of several images are given as compared with those of the thresholding and seed region growing methods. Nutzungsrecht: © Springer Science+Business Media New York 2015 spatial distribution point field Processor Architectures Artificial Intelligence (incl. Robotics) Software Engineering/Programming and Operating Systems Systems Theory, Control clusterization local extremum image segmentation Mathematics Rusyn, B P oth Korniy, V V oth Kerod, T I oth Enthalten in Cybernetics and systems analysis New York, NY [u.a.] : Consultants Bureau, 1992 51(2015), 5, Seite 704-713 (DE-627)131081225 (DE-600)1112963-3 (DE-576)029167868 1060-0396 nnns volume:51 year:2015 number:5 pages:704-713 http://dx.doi.org/10.1007/s10559-015-9762-5 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 AR 51 2015 5 704-713 |
| allfieldsSound |
10.1007/s10559-015-9762-5 doi PQ20160617 (DE-627)OLC1964327113 (DE-599)GBVOLC1964327113 (PRQ)s612-c72b3f623ee20c81af485208d9790d91a9ce04b986a2bcf7ead0c4a8b961a20b0 (KEY)0029269920150000051000500704imagesegmentationbasedontheevaluationofthetendency DE-627 ger DE-627 rakwb eng 000 DNB Kosarevych, R J verfasserin aut Image Segmentation Based on the Evaluation of the Tendency of Image Elements to form Clusters with the Help of Point Field Characteristics 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A new approach to the segmentation of an image is proposed on the basis of modeling the spatial distribution of points in the image plane and their ability to identify clusters. Based on detected histogram peaks, a sequence of dominant brightness values (brightnesses) is formed for each fragment of the image. Point fields are formed for each image brightness and the presence of clusters is checked with the help of second-order characteristics of these point fields. The union of all the brightnesses for which point fields form clusters forms the object of segmentation. The results of segmentation of several images are given as compared with those of the thresholding and seed region growing methods. Nutzungsrecht: © Springer Science+Business Media New York 2015 spatial distribution point field Processor Architectures Artificial Intelligence (incl. Robotics) Software Engineering/Programming and Operating Systems Systems Theory, Control clusterization local extremum image segmentation Mathematics Rusyn, B P oth Korniy, V V oth Kerod, T I oth Enthalten in Cybernetics and systems analysis New York, NY [u.a.] : Consultants Bureau, 1992 51(2015), 5, Seite 704-713 (DE-627)131081225 (DE-600)1112963-3 (DE-576)029167868 1060-0396 nnns volume:51 year:2015 number:5 pages:704-713 http://dx.doi.org/10.1007/s10559-015-9762-5 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 AR 51 2015 5 704-713 |
| language |
English |
| source |
Enthalten in Cybernetics and systems analysis 51(2015), 5, Seite 704-713 volume:51 year:2015 number:5 pages:704-713 |
| sourceStr |
Enthalten in Cybernetics and systems analysis 51(2015), 5, Seite 704-713 volume:51 year:2015 number:5 pages:704-713 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
spatial distribution point field Processor Architectures Artificial Intelligence (incl. Robotics) Software Engineering/Programming and Operating Systems Systems Theory, Control clusterization local extremum image segmentation Mathematics |
| dewey-raw |
000 |
| isfreeaccess_bool |
false |
| container_title |
Cybernetics and systems analysis |
| authorswithroles_txt_mv |
Kosarevych, R J @@aut@@ Rusyn, B P @@oth@@ Korniy, V V @@oth@@ Kerod, T I @@oth@@ |
| publishDateDaySort_date |
2015-01-01T00:00:00Z |
| hierarchy_top_id |
131081225 |
| dewey-sort |
0 |
| id |
OLC1964327113 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1964327113</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230714162258.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160206s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10559-015-9762-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160617</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1964327113</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1964327113</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)s612-c72b3f623ee20c81af485208d9790d91a9ce04b986a2bcf7ead0c4a8b961a20b0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0029269920150000051000500704imagesegmentationbasedontheevaluationofthetendency</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">000</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kosarevych, R J</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Image Segmentation Based on the Evaluation of the Tendency of Image Elements to form Clusters with the Help of Point Field Characteristics</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">A new approach to the segmentation of an image is proposed on the basis of modeling the spatial distribution of points in the image plane and their ability to identify clusters. Based on detected histogram peaks, a sequence of dominant brightness values (brightnesses) is formed for each fragment of the image. Point fields are formed for each image brightness and the presence of clusters is checked with the help of second-order characteristics of these point fields. The union of all the brightnesses for which point fields form clusters forms the object of segmentation. The results of segmentation of several images are given as compared with those of the thresholding and seed region growing methods.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: © Springer Science+Business Media New York 2015</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">spatial distribution</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">point field</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Processor Architectures</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial Intelligence (incl. Robotics)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Software Engineering/Programming and Operating Systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Systems Theory, Control</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">clusterization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">local extremum</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">image segmentation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematics</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rusyn, B P</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Korniy, V V</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kerod, T I</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Cybernetics and systems analysis</subfield><subfield code="d">New York, NY [u.a.] : Consultants Bureau, 1992</subfield><subfield code="g">51(2015), 5, Seite 704-713</subfield><subfield code="w">(DE-627)131081225</subfield><subfield code="w">(DE-600)1112963-3</subfield><subfield code="w">(DE-576)029167868</subfield><subfield code="x">1060-0396</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:51</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:5</subfield><subfield code="g">pages:704-713</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1007/s10559-015-9762-5</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">51</subfield><subfield code="j">2015</subfield><subfield code="e">5</subfield><subfield code="h">704-713</subfield></datafield></record></collection>
|
| author |
Kosarevych, R J |
| spellingShingle |
Kosarevych, R J ddc 000 misc spatial distribution misc point field misc Processor Architectures misc Artificial Intelligence (incl. Robotics) misc Software Engineering/Programming and Operating Systems misc Systems Theory, Control misc clusterization misc local extremum misc image segmentation misc Mathematics Image Segmentation Based on the Evaluation of the Tendency of Image Elements to form Clusters with the Help of Point Field Characteristics |
| authorStr |
Kosarevych, R J |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)131081225 |
| format |
Article |
| dewey-ones |
000 - Computer science, information & general works |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
1060-0396 |
| topic_title |
000 DNB Image Segmentation Based on the Evaluation of the Tendency of Image Elements to form Clusters with the Help of Point Field Characteristics spatial distribution point field Processor Architectures Artificial Intelligence (incl. Robotics) Software Engineering/Programming and Operating Systems Systems Theory, Control clusterization local extremum image segmentation Mathematics |
| topic |
ddc 000 misc spatial distribution misc point field misc Processor Architectures misc Artificial Intelligence (incl. Robotics) misc Software Engineering/Programming and Operating Systems misc Systems Theory, Control misc clusterization misc local extremum misc image segmentation misc Mathematics |
| topic_unstemmed |
ddc 000 misc spatial distribution misc point field misc Processor Architectures misc Artificial Intelligence (incl. Robotics) misc Software Engineering/Programming and Operating Systems misc Systems Theory, Control misc clusterization misc local extremum misc image segmentation misc Mathematics |
| topic_browse |
ddc 000 misc spatial distribution misc point field misc Processor Architectures misc Artificial Intelligence (incl. Robotics) misc Software Engineering/Programming and Operating Systems misc Systems Theory, Control misc clusterization misc local extremum misc image segmentation misc Mathematics |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| author2_variant |
b p r bp bpr v v k vv vvk t i k ti tik |
| hierarchy_parent_title |
Cybernetics and systems analysis |
| hierarchy_parent_id |
131081225 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Cybernetics and systems analysis |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)131081225 (DE-600)1112963-3 (DE-576)029167868 |
| title |
Image Segmentation Based on the Evaluation of the Tendency of Image Elements to form Clusters with the Help of Point Field Characteristics |
| ctrlnum |
(DE-627)OLC1964327113 (DE-599)GBVOLC1964327113 (PRQ)s612-c72b3f623ee20c81af485208d9790d91a9ce04b986a2bcf7ead0c4a8b961a20b0 (KEY)0029269920150000051000500704imagesegmentationbasedontheevaluationofthetendency |
| title_full |
Image Segmentation Based on the Evaluation of the Tendency of Image Elements to form Clusters with the Help of Point Field Characteristics |
| author_sort |
Kosarevych, R J |
| journal |
Cybernetics and systems analysis |
| journalStr |
Cybernetics and systems analysis |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2015 |
| contenttype_str_mv |
txt |
| container_start_page |
704 |
| author_browse |
Kosarevych, R J |
| container_volume |
51 |
| class |
000 DNB |
| format_se |
Aufsätze |
| author-letter |
Kosarevych, R J |
| doi_str_mv |
10.1007/s10559-015-9762-5 |
| dewey-full |
000 |
| title_sort |
image segmentation based on the evaluation of the tendency of image elements to form clusters with the help of point field characteristics |
| title_auth |
Image Segmentation Based on the Evaluation of the Tendency of Image Elements to form Clusters with the Help of Point Field Characteristics |
| abstract |
A new approach to the segmentation of an image is proposed on the basis of modeling the spatial distribution of points in the image plane and their ability to identify clusters. Based on detected histogram peaks, a sequence of dominant brightness values (brightnesses) is formed for each fragment of the image. Point fields are formed for each image brightness and the presence of clusters is checked with the help of second-order characteristics of these point fields. The union of all the brightnesses for which point fields form clusters forms the object of segmentation. The results of segmentation of several images are given as compared with those of the thresholding and seed region growing methods. |
| abstractGer |
A new approach to the segmentation of an image is proposed on the basis of modeling the spatial distribution of points in the image plane and their ability to identify clusters. Based on detected histogram peaks, a sequence of dominant brightness values (brightnesses) is formed for each fragment of the image. Point fields are formed for each image brightness and the presence of clusters is checked with the help of second-order characteristics of these point fields. The union of all the brightnesses for which point fields form clusters forms the object of segmentation. The results of segmentation of several images are given as compared with those of the thresholding and seed region growing methods. |
| abstract_unstemmed |
A new approach to the segmentation of an image is proposed on the basis of modeling the spatial distribution of points in the image plane and their ability to identify clusters. Based on detected histogram peaks, a sequence of dominant brightness values (brightnesses) is formed for each fragment of the image. Point fields are formed for each image brightness and the presence of clusters is checked with the help of second-order characteristics of these point fields. The union of all the brightnesses for which point fields form clusters forms the object of segmentation. The results of segmentation of several images are given as compared with those of the thresholding and seed region growing methods. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-MAT GBV_ILN_70 |
| container_issue |
5 |
| title_short |
Image Segmentation Based on the Evaluation of the Tendency of Image Elements to form Clusters with the Help of Point Field Characteristics |
| url |
http://dx.doi.org/10.1007/s10559-015-9762-5 |
| remote_bool |
false |
| author2 |
Rusyn, B P Korniy, V V Kerod, T I |
| author2Str |
Rusyn, B P Korniy, V V Kerod, T I |
| ppnlink |
131081225 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth |
| doi_str |
10.1007/s10559-015-9762-5 |
| up_date |
2024-07-03T13:37:32.136Z |
| _version_ |
1803565245019455488 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1964327113</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230714162258.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160206s2015 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10559-015-9762-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160617</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1964327113</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1964327113</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)s612-c72b3f623ee20c81af485208d9790d91a9ce04b986a2bcf7ead0c4a8b961a20b0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0029269920150000051000500704imagesegmentationbasedontheevaluationofthetendency</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">000</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kosarevych, R J</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Image Segmentation Based on the Evaluation of the Tendency of Image Elements to form Clusters with the Help of Point Field Characteristics</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">A new approach to the segmentation of an image is proposed on the basis of modeling the spatial distribution of points in the image plane and their ability to identify clusters. Based on detected histogram peaks, a sequence of dominant brightness values (brightnesses) is formed for each fragment of the image. Point fields are formed for each image brightness and the presence of clusters is checked with the help of second-order characteristics of these point fields. The union of all the brightnesses for which point fields form clusters forms the object of segmentation. The results of segmentation of several images are given as compared with those of the thresholding and seed region growing methods.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: © Springer Science+Business Media New York 2015</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">spatial distribution</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">point field</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Processor Architectures</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial Intelligence (incl. Robotics)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Software Engineering/Programming and Operating Systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Systems Theory, Control</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">clusterization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">local extremum</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">image segmentation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematics</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rusyn, B P</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Korniy, V V</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kerod, T I</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Cybernetics and systems analysis</subfield><subfield code="d">New York, NY [u.a.] : Consultants Bureau, 1992</subfield><subfield code="g">51(2015), 5, Seite 704-713</subfield><subfield code="w">(DE-627)131081225</subfield><subfield code="w">(DE-600)1112963-3</subfield><subfield code="w">(DE-576)029167868</subfield><subfield code="x">1060-0396</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:51</subfield><subfield code="g">year:2015</subfield><subfield code="g">number:5</subfield><subfield code="g">pages:704-713</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1007/s10559-015-9762-5</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">51</subfield><subfield code="j">2015</subfield><subfield code="e">5</subfield><subfield code="h">704-713</subfield></datafield></record></collection>
|
| score |
7.397806 |