A Performance Comparison of Supervised and Unsupervised Image Segmentation Methods
Image processing plays a vital role in many recent computer applications in the association with machine learning technology. The supervised training on dataset of features can only be successful if the segmentation process is accurate in the computer vision phase. The term segmentation is the proce...
Gespeichert in:
| Veröffentlicht in: | SN computer science 2020-05, Vol.1 (3), p.122, Article 122 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 3 |
| container_start_page | 122 |
| container_title | SN computer science |
| container_volume | 1 |
| creator | Baby, Diana Devaraj, Sujitha Juliet Mathew, Soumya Anishin Raj, M. M. Karthikeyan, B. |
| description | Image processing plays a vital role in many recent computer applications in the association with machine learning technology. The supervised training on dataset of features can only be successful if the segmentation process is accurate in the computer vision phase. The term segmentation is the process of extracting or identification of distinguishable regions in an image. This is performed based on the properties of image pixel intensity values and their proximities. This paper mainly focuses on an investigation of various latest image segmentation techniques performed in the field of computer vision and image processing. Segmentation plays a vital role in computer vision since any fault in segmentation will led to inaccurate extraction of features which results in wrong prediction of the decision support systems. |
| doi_str_mv | 10.1007/s42979-020-00136-9 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2932505601</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2932505601</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2789-9a72006af8784b895dc1f5f10ab3ffc7cd743015b1a89946032bc75d5458865b3</originalsourceid><addsrcrecordid>eNp9kE1LAzEQhoMoWGr_gKcFz9FJdvN1LMWPQkWxFryF7G5St7jJmmwF_71rV-jN0wzD-8wwD0KXBK4JgLhJBVVCYaCAAUjOsTpBE8o5wVKBOD30FCvF3s7RLKUdAFAGRcHZBL3Ms2cbXYit8ZXNFqHtTGxS8Flw2Xrf2fjVJFtnxtfZxqfjYNmarc3Wdtta35u-GYhH27-HOl2gM2c-kp391Sna3N2-Lh7w6ul-uZivcEWFVFgZQQG4cVLIopSK1RVxzBEwZe5cJapaFDkQVhIjlSo45LSsBKtZwaTkrMyn6Grc28Xwubep17uwj344qanKhw8ZH2xMER1TVQwpRet0F5vWxG9NQP_q06M-PejTB31aDVA-QmkI-62Nx9X_UD_vaXHl</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2932505601</pqid></control><display><type>article</type><title>A Performance Comparison of Supervised and Unsupervised Image Segmentation Methods</title><source>Springer Nature - Complete Springer Journals</source><source>ProQuest Central</source><creator>Baby, Diana ; Devaraj, Sujitha Juliet ; Mathew, Soumya ; Anishin Raj, M. M. ; Karthikeyan, B.</creator><creatorcontrib>Baby, Diana ; Devaraj, Sujitha Juliet ; Mathew, Soumya ; Anishin Raj, M. M. ; Karthikeyan, B.</creatorcontrib><description>Image processing plays a vital role in many recent computer applications in the association with machine learning technology. The supervised training on dataset of features can only be successful if the segmentation process is accurate in the computer vision phase. The term segmentation is the process of extracting or identification of distinguishable regions in an image. This is performed based on the properties of image pixel intensity values and their proximities. This paper mainly focuses on an investigation of various latest image segmentation techniques performed in the field of computer vision and image processing. Segmentation plays a vital role in computer vision since any fault in segmentation will led to inaccurate extraction of features which results in wrong prediction of the decision support systems.</description><identifier>ISSN: 2662-995X</identifier><identifier>EISSN: 2661-8907</identifier><identifier>DOI: 10.1007/s42979-020-00136-9</identifier><language>eng</language><publisher>Singapore: Springer Singapore</publisher><subject>Advances in Computational Approaches for Artificial Intelligence ; Algorithms ; Clustering ; Computer Imaging ; Computer Science ; Computer Systems Organization and Communication Networks ; Computer vision ; Data Structures and Information Theory ; Decision support systems ; Dictionaries ; Image Processing ; Image segmentation ; Information Systems and Communication Service ; IoT and Cloud Applications ; Machine learning ; Methods ; Pattern Recognition and Graphics ; Software Engineering/Programming and Operating Systems ; Survey Article ; Vision</subject><ispartof>SN computer science, 2020-05, Vol.1 (3), p.122, Article 122</ispartof><rights>Springer Nature Singapore Pte Ltd 2020</rights><rights>Springer Nature Singapore Pte Ltd 2020.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2789-9a72006af8784b895dc1f5f10ab3ffc7cd743015b1a89946032bc75d5458865b3</citedby><cites>FETCH-LOGICAL-c2789-9a72006af8784b895dc1f5f10ab3ffc7cd743015b1a89946032bc75d5458865b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s42979-020-00136-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2932505601?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,21367,27901,27902,33721,41464,42533,43781,51294</link.rule.ids></links><search><creatorcontrib>Baby, Diana</creatorcontrib><creatorcontrib>Devaraj, Sujitha Juliet</creatorcontrib><creatorcontrib>Mathew, Soumya</creatorcontrib><creatorcontrib>Anishin Raj, M. M.</creatorcontrib><creatorcontrib>Karthikeyan, B.</creatorcontrib><title>A Performance Comparison of Supervised and Unsupervised Image Segmentation Methods</title><title>SN computer science</title><addtitle>SN COMPUT. SCI</addtitle><description>Image processing plays a vital role in many recent computer applications in the association with machine learning technology. The supervised training on dataset of features can only be successful if the segmentation process is accurate in the computer vision phase. The term segmentation is the process of extracting or identification of distinguishable regions in an image. This is performed based on the properties of image pixel intensity values and their proximities. This paper mainly focuses on an investigation of various latest image segmentation techniques performed in the field of computer vision and image processing. Segmentation plays a vital role in computer vision since any fault in segmentation will led to inaccurate extraction of features which results in wrong prediction of the decision support systems.</description><subject>Advances in Computational Approaches for Artificial Intelligence</subject><subject>Algorithms</subject><subject>Clustering</subject><subject>Computer Imaging</subject><subject>Computer Science</subject><subject>Computer Systems Organization and Communication Networks</subject><subject>Computer vision</subject><subject>Data Structures and Information Theory</subject><subject>Decision support systems</subject><subject>Dictionaries</subject><subject>Image Processing</subject><subject>Image segmentation</subject><subject>Information Systems and Communication Service</subject><subject>IoT and Cloud Applications</subject><subject>Machine learning</subject><subject>Methods</subject><subject>Pattern Recognition and Graphics</subject><subject>Software Engineering/Programming and Operating Systems</subject><subject>Survey Article</subject><subject>Vision</subject><issn>2662-995X</issn><issn>2661-8907</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kE1LAzEQhoMoWGr_gKcFz9FJdvN1LMWPQkWxFryF7G5St7jJmmwF_71rV-jN0wzD-8wwD0KXBK4JgLhJBVVCYaCAAUjOsTpBE8o5wVKBOD30FCvF3s7RLKUdAFAGRcHZBL3Ms2cbXYit8ZXNFqHtTGxS8Flw2Xrf2fjVJFtnxtfZxqfjYNmarc3Wdtta35u-GYhH27-HOl2gM2c-kp391Sna3N2-Lh7w6ul-uZivcEWFVFgZQQG4cVLIopSK1RVxzBEwZe5cJapaFDkQVhIjlSo45LSsBKtZwaTkrMyn6Grc28Xwubep17uwj344qanKhw8ZH2xMER1TVQwpRet0F5vWxG9NQP_q06M-PejTB31aDVA-QmkI-62Nx9X_UD_vaXHl</recordid><startdate>20200501</startdate><enddate>20200501</enddate><creator>Baby, Diana</creator><creator>Devaraj, Sujitha Juliet</creator><creator>Mathew, Soumya</creator><creator>Anishin Raj, M. M.</creator><creator>Karthikeyan, B.</creator><general>Springer Singapore</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20200501</creationdate><title>A Performance Comparison of Supervised and Unsupervised Image Segmentation Methods</title><author>Baby, Diana ; Devaraj, Sujitha Juliet ; Mathew, Soumya ; Anishin Raj, M. M. ; Karthikeyan, B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2789-9a72006af8784b895dc1f5f10ab3ffc7cd743015b1a89946032bc75d5458865b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Advances in Computational Approaches for Artificial Intelligence</topic><topic>Algorithms</topic><topic>Clustering</topic><topic>Computer Imaging</topic><topic>Computer Science</topic><topic>Computer Systems Organization and Communication Networks</topic><topic>Computer vision</topic><topic>Data Structures and Information Theory</topic><topic>Decision support systems</topic><topic>Dictionaries</topic><topic>Image Processing</topic><topic>Image segmentation</topic><topic>Information Systems and Communication Service</topic><topic>IoT and Cloud Applications</topic><topic>Machine learning</topic><topic>Methods</topic><topic>Pattern Recognition and Graphics</topic><topic>Software Engineering/Programming and Operating Systems</topic><topic>Survey Article</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Baby, Diana</creatorcontrib><creatorcontrib>Devaraj, Sujitha Juliet</creatorcontrib><creatorcontrib>Mathew, Soumya</creatorcontrib><creatorcontrib>Anishin Raj, M. M.</creatorcontrib><creatorcontrib>Karthikeyan, B.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>SN computer science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Baby, Diana</au><au>Devaraj, Sujitha Juliet</au><au>Mathew, Soumya</au><au>Anishin Raj, M. M.</au><au>Karthikeyan, B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Performance Comparison of Supervised and Unsupervised Image Segmentation Methods</atitle><jtitle>SN computer science</jtitle><stitle>SN COMPUT. SCI</stitle><date>2020-05-01</date><risdate>2020</risdate><volume>1</volume><issue>3</issue><spage>122</spage><pages>122-</pages><artnum>122</artnum><issn>2662-995X</issn><eissn>2661-8907</eissn><abstract>Image processing plays a vital role in many recent computer applications in the association with machine learning technology. The supervised training on dataset of features can only be successful if the segmentation process is accurate in the computer vision phase. The term segmentation is the process of extracting or identification of distinguishable regions in an image. This is performed based on the properties of image pixel intensity values and their proximities. This paper mainly focuses on an investigation of various latest image segmentation techniques performed in the field of computer vision and image processing. Segmentation plays a vital role in computer vision since any fault in segmentation will led to inaccurate extraction of features which results in wrong prediction of the decision support systems.</abstract><cop>Singapore</cop><pub>Springer Singapore</pub><doi>10.1007/s42979-020-00136-9</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2662-995X |
| ispartof | SN computer science, 2020-05, Vol.1 (3), p.122, Article 122 |
| issn | 2662-995X 2661-8907 |
| language | eng |
| recordid | cdi_proquest_journals_2932505601 |
| source | Springer Nature - Complete Springer Journals; ProQuest Central |
| subjects | Advances in Computational Approaches for Artificial Intelligence Algorithms Clustering Computer Imaging Computer Science Computer Systems Organization and Communication Networks Computer vision Data Structures and Information Theory Decision support systems Dictionaries Image Processing Image segmentation Information Systems and Communication Service IoT and Cloud Applications Machine learning Methods Pattern Recognition and Graphics Software Engineering/Programming and Operating Systems Survey Article Vision |
| title | A Performance Comparison of Supervised and Unsupervised Image Segmentation Methods |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-13T12%3A36%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Performance%20Comparison%20of%20Supervised%20and%20Unsupervised%20Image%20Segmentation%20Methods&rft.jtitle=SN%20computer%20science&rft.au=Baby,%20Diana&rft.date=2020-05-01&rft.volume=1&rft.issue=3&rft.spage=122&rft.pages=122-&rft.artnum=122&rft.issn=2662-995X&rft.eissn=2661-8907&rft_id=info:doi/10.1007/s42979-020-00136-9&rft_dat=%3Cproquest_cross%3E2932505601%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2932505601&rft_id=info:pmid/&rfr_iscdi=true |