Bayesian Approach to Image Interpretation:
Bayesian Approach to Image Interpretation will interest anyone working in image interpretation. It is complete in itself and includes background material. This makes it useful for a novice as well as for an expert. It reviews some of the existing probabilistic methods for image interpretation and pr...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
2002
|
| Schriftenreihe: | The International Series in Engineering and Computer Science
616 |
| Schlagworte: | |
| Online-Zugang: | FHI01 BTU01 Volltext |
| Zusammenfassung: | Bayesian Approach to Image Interpretation will interest anyone working in image interpretation. It is complete in itself and includes background material. This makes it useful for a novice as well as for an expert. It reviews some of the existing probabilistic methods for image interpretation and presents some new results. Additionally, there is extensive bibliography covering references in varied areas. For a researcher in this field, the material on synergistic integration of segmentation and interpretation modules and the Bayesian approach to image interpretation will be beneficial. For a practicing engineer, the procedure for generating knowledge base, selecting initial temperature for the simulated annealing algorithm, and some implementation issues will be valuable. New ideas introduced in the book include: New approach to image interpretation using synergism between the segmentation and the interpretation modules. A new segmentation algorithm based on multiresolution analysis. Novel use of the Bayesian networks (causal networks) for image interpretation. Emphasis on making the interpretation approach less dependent on the knowledge base and hence more reliable by modeling the knowledge base in a probabilistic framework. Useful in both the academic and industrial research worlds, Bayesian Approach to Image Interpretation may also be used as a textbook for a semester course in computer vision or pattern recognition |
| Beschreibung: | 1 Online-Ressource (XV, 127 p) |
| ISBN: | 9780306469961 |
| DOI: | 10.1007/b117231 |
Internformat
MARC
| LEADER | 00000nmm a2200000zcb4500 | ||
|---|---|---|---|
| 001 | BV045148417 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 180827s2002 |||| o||u| ||||||eng d | ||
| 020 | |a 9780306469961 |9 978-0-306-46996-1 | ||
| 024 | 7 | |a 10.1007/b117231 |2 doi | |
| 035 | |a (ZDB-2-ENG)978-0-306-46996-1 | ||
| 035 | |a (OCoLC)1190214785 | ||
| 035 | |a (DE-599)BVBBV045148417 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-573 |a DE-634 | ||
| 082 | 0 | |a 006.37 |2 23 | |
| 082 | 0 | |a 006.6 |2 23 | |
| 084 | |a ST 330 |0 (DE-625)143663: |2 rvk | ||
| 100 | 1 | |a Kopparapu, Sunil K. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Bayesian Approach to Image Interpretation |c by Sunil K. Kopparapu, Uday B. Desai |
| 264 | 1 | |a Boston, MA |b Springer US |c 2002 | |
| 300 | |a 1 Online-Ressource (XV, 127 p) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a The International Series in Engineering and Computer Science |v 616 | |
| 520 | |a Bayesian Approach to Image Interpretation will interest anyone working in image interpretation. It is complete in itself and includes background material. This makes it useful for a novice as well as for an expert. It reviews some of the existing probabilistic methods for image interpretation and presents some new results. Additionally, there is extensive bibliography covering references in varied areas. For a researcher in this field, the material on synergistic integration of segmentation and interpretation modules and the Bayesian approach to image interpretation will be beneficial. For a practicing engineer, the procedure for generating knowledge base, selecting initial temperature for the simulated annealing algorithm, and some implementation issues will be valuable. New ideas introduced in the book include: New approach to image interpretation using synergism between the segmentation and the interpretation modules. A new segmentation algorithm based on multiresolution analysis. Novel use of the Bayesian networks (causal networks) for image interpretation. Emphasis on making the interpretation approach less dependent on the knowledge base and hence more reliable by modeling the knowledge base in a probabilistic framework. Useful in both the academic and industrial research worlds, Bayesian Approach to Image Interpretation may also be used as a textbook for a semester course in computer vision or pattern recognition | ||
| 650 | 4 | |a Computer Science | |
| 650 | 4 | |a Image Processing and Computer Vision | |
| 650 | 4 | |a Computer Imaging, Vision, Pattern Recognition and Graphics | |
| 650 | 4 | |a Computer Graphics | |
| 650 | 4 | |a Computer Communication Networks | |
| 650 | 4 | |a Computer science | |
| 650 | 4 | |a Computer communication systems | |
| 650 | 4 | |a Computer graphics | |
| 650 | 4 | |a Image processing | |
| 650 | 0 | 7 | |a Bayes-Verfahren |0 (DE-588)4204326-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Bildverstehen |0 (DE-588)4202022-0 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Bildverstehen |0 (DE-588)4202022-0 |D s |
| 689 | 0 | 1 | |a Bayes-Verfahren |0 (DE-588)4204326-8 |D s |
| 689 | 0 | |8 1\p |5 DE-604 | |
| 700 | 1 | |a Desai, Uday B. |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9780792373728 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/b117231 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
| 912 | |a ZDB-2-ENG | ||
| 940 | 1 | |q ZDB-2-ENG_2000/2004 | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-030538116 | ||
| 883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 966 | e | |u https://doi.org/10.1007/b117231 |l FHI01 |p ZDB-2-ENG |q ZDB-2-ENG_2000/2004 |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/b117231 |l BTU01 |p ZDB-2-ENG |q ZDB-2-ENG_Archiv |x Verlag |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1804178818365128704 |
|---|---|
| any_adam_object | |
| author | Kopparapu, Sunil K. Desai, Uday B. |
| author_facet | Kopparapu, Sunil K. Desai, Uday B. |
| author_role | aut aut |
| author_sort | Kopparapu, Sunil K. |
| author_variant | s k k sk skk u b d ub ubd |
| building | Verbundindex |
| bvnumber | BV045148417 |
| classification_rvk | ST 330 |
| collection | ZDB-2-ENG |
| ctrlnum | (ZDB-2-ENG)978-0-306-46996-1 (OCoLC)1190214785 (DE-599)BVBBV045148417 |
| dewey-full | 006.37 006.6 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.37 006.6 |
| dewey-search | 006.37 006.6 |
| dewey-sort | 16.37 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.1007/b117231 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03699nmm a2200601zcb4500</leader><controlfield tag="001">BV045148417</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">180827s2002 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780306469961</subfield><subfield code="9">978-0-306-46996-1</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/b117231</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-2-ENG)978-0-306-46996-1</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1190214785</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV045148417</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">aacr</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-573</subfield><subfield code="a">DE-634</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.37</subfield><subfield code="2">23</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.6</subfield><subfield code="2">23</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 330</subfield><subfield code="0">(DE-625)143663:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kopparapu, Sunil K.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Bayesian Approach to Image Interpretation</subfield><subfield code="c">by Sunil K. Kopparapu, Uday B. Desai</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Boston, MA</subfield><subfield code="b">Springer US</subfield><subfield code="c">2002</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (XV, 127 p)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">The International Series in Engineering and Computer Science</subfield><subfield code="v">616</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Bayesian Approach to Image Interpretation will interest anyone working in image interpretation. It is complete in itself and includes background material. This makes it useful for a novice as well as for an expert. It reviews some of the existing probabilistic methods for image interpretation and presents some new results. Additionally, there is extensive bibliography covering references in varied areas. For a researcher in this field, the material on synergistic integration of segmentation and interpretation modules and the Bayesian approach to image interpretation will be beneficial. For a practicing engineer, the procedure for generating knowledge base, selecting initial temperature for the simulated annealing algorithm, and some implementation issues will be valuable. New ideas introduced in the book include: New approach to image interpretation using synergism between the segmentation and the interpretation modules. A new segmentation algorithm based on multiresolution analysis. Novel use of the Bayesian networks (causal networks) for image interpretation. Emphasis on making the interpretation approach less dependent on the knowledge base and hence more reliable by modeling the knowledge base in a probabilistic framework. Useful in both the academic and industrial research worlds, Bayesian Approach to Image Interpretation may also be used as a textbook for a semester course in computer vision or pattern recognition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer Science</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image Processing and Computer Vision</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer Imaging, Vision, Pattern Recognition and Graphics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer Graphics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer Communication Networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer science</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer communication systems</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer graphics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image processing</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Bayes-Verfahren</subfield><subfield code="0">(DE-588)4204326-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Bildverstehen</subfield><subfield code="0">(DE-588)4202022-0</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Bildverstehen</subfield><subfield code="0">(DE-588)4202022-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Bayes-Verfahren</subfield><subfield code="0">(DE-588)4204326-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="8">1\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Desai, Uday B.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">9780792373728</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/b117231</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-2-ENG</subfield></datafield><datafield tag="940" ind1="1" ind2=" "><subfield code="q">ZDB-2-ENG_2000/2004</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-030538116</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/b117231</subfield><subfield code="l">FHI01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="q">ZDB-2-ENG_2000/2004</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/b117231</subfield><subfield code="l">BTU01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="q">ZDB-2-ENG_Archiv</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV045148417 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:01Z |
| institution | BVB |
| isbn | 9780306469961 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030538116 |
| oclc_num | 1190214785 |
| open_access_boolean | |
| owner | DE-573 DE-634 |
| owner_facet | DE-573 DE-634 |
| physical | 1 Online-Ressource (XV, 127 p) |
| psigel | ZDB-2-ENG ZDB-2-ENG_2000/2004 ZDB-2-ENG ZDB-2-ENG_2000/2004 ZDB-2-ENG ZDB-2-ENG_Archiv |
| publishDate | 2002 |
| publishDateSearch | 2002 |
| publishDateSort | 2002 |
| publisher | Springer US |
| record_format | marc |
| series2 | The International Series in Engineering and Computer Science |
| spelling | Kopparapu, Sunil K. Verfasser aut Bayesian Approach to Image Interpretation by Sunil K. Kopparapu, Uday B. Desai Boston, MA Springer US 2002 1 Online-Ressource (XV, 127 p) txt rdacontent c rdamedia cr rdacarrier The International Series in Engineering and Computer Science 616 Bayesian Approach to Image Interpretation will interest anyone working in image interpretation. It is complete in itself and includes background material. This makes it useful for a novice as well as for an expert. It reviews some of the existing probabilistic methods for image interpretation and presents some new results. Additionally, there is extensive bibliography covering references in varied areas. For a researcher in this field, the material on synergistic integration of segmentation and interpretation modules and the Bayesian approach to image interpretation will be beneficial. For a practicing engineer, the procedure for generating knowledge base, selecting initial temperature for the simulated annealing algorithm, and some implementation issues will be valuable. New ideas introduced in the book include: New approach to image interpretation using synergism between the segmentation and the interpretation modules. A new segmentation algorithm based on multiresolution analysis. Novel use of the Bayesian networks (causal networks) for image interpretation. Emphasis on making the interpretation approach less dependent on the knowledge base and hence more reliable by modeling the knowledge base in a probabilistic framework. Useful in both the academic and industrial research worlds, Bayesian Approach to Image Interpretation may also be used as a textbook for a semester course in computer vision or pattern recognition Computer Science Image Processing and Computer Vision Computer Imaging, Vision, Pattern Recognition and Graphics Computer Graphics Computer Communication Networks Computer science Computer communication systems Computer graphics Image processing Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf Bildverstehen (DE-588)4202022-0 gnd rswk-swf Bildverstehen (DE-588)4202022-0 s Bayes-Verfahren (DE-588)4204326-8 s 1\p DE-604 Desai, Uday B. aut Erscheint auch als Druck-Ausgabe 9780792373728 https://doi.org/10.1007/b117231 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Kopparapu, Sunil K. Desai, Uday B. Bayesian Approach to Image Interpretation Computer Science Image Processing and Computer Vision Computer Imaging, Vision, Pattern Recognition and Graphics Computer Graphics Computer Communication Networks Computer science Computer communication systems Computer graphics Image processing Bayes-Verfahren (DE-588)4204326-8 gnd Bildverstehen (DE-588)4202022-0 gnd |
| subject_GND | (DE-588)4204326-8 (DE-588)4202022-0 |
| title | Bayesian Approach to Image Interpretation |
| title_auth | Bayesian Approach to Image Interpretation |
| title_exact_search | Bayesian Approach to Image Interpretation |
| title_full | Bayesian Approach to Image Interpretation by Sunil K. Kopparapu, Uday B. Desai |
| title_fullStr | Bayesian Approach to Image Interpretation by Sunil K. Kopparapu, Uday B. Desai |
| title_full_unstemmed | Bayesian Approach to Image Interpretation by Sunil K. Kopparapu, Uday B. Desai |
| title_short | Bayesian Approach to Image Interpretation |
| title_sort | bayesian approach to image interpretation |
| topic | Computer Science Image Processing and Computer Vision Computer Imaging, Vision, Pattern Recognition and Graphics Computer Graphics Computer Communication Networks Computer science Computer communication systems Computer graphics Image processing Bayes-Verfahren (DE-588)4204326-8 gnd Bildverstehen (DE-588)4202022-0 gnd |
| topic_facet | Computer Science Image Processing and Computer Vision Computer Imaging, Vision, Pattern Recognition and Graphics Computer Graphics Computer Communication Networks Computer science Computer communication systems Computer graphics Image processing Bayes-Verfahren Bildverstehen |
| url | https://doi.org/10.1007/b117231 |
| work_keys_str_mv | AT kopparapusunilk bayesianapproachtoimageinterpretation AT desaiudayb bayesianapproachtoimageinterpretation |