Towards a likelihood ratio approach for bloodstain pattern analysis
In this work, we explore the application of likelihood ratio as a forensic evidence assessment tool to evaluate the causal mechanism of a bloodstain pattern. It is assumed that there are two competing hypotheses regarding the cause of a bloodstain pattern. The bloodstain patterns are represented as...
Gespeichert in:
| Veröffentlicht in: | Forensic science international 2022-12, Vol.341, p.111512-111512, Article 111512 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 111512 |
|---|---|
| container_issue | |
| container_start_page | 111512 |
| container_title | Forensic science international |
| container_volume | 341 |
| creator | Zou, Tong Stern, Hal S. |
| description | In this work, we explore the application of likelihood ratio as a forensic evidence assessment tool to evaluate the causal mechanism of a bloodstain pattern. It is assumed that there are two competing hypotheses regarding the cause of a bloodstain pattern. The bloodstain patterns are represented as a collection of ellipses with each ellipse characterized by its location, size and orientation. Quantitative measures and features are derived to summarize key aspects of the patterns. A bivariate Gaussian model is chosen to estimate the distribution of features under a given hypothesis and thus approximate the likelihood of a pattern. Published data with 59 impact patterns and 55 gunshot patterns is used to train and evaluate the model. Results demonstrate the feasibility of the likelihood ratio approach for bloodstain pattern analysis. The results also hint at some of the challenges that need to be addressed for future use of the likelihood ratio approach for bloodstain pattern analysis. |
| doi_str_mv | 10.1016/j.forsciint.2022.111512 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2736304205</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0379073822003425</els_id><sourcerecordid>2736304205</sourcerecordid><originalsourceid>FETCH-LOGICAL-c425t-88fa115a3924715506ec1ca37d6fda6fe511aca7ed629fcbc7b9183332b8a9733</originalsourceid><addsrcrecordid>eNqFkEtPwzAQhC0EEqXwG7DEhUuCH7WdHKuKl1SJSzlbG8dRHdI42Cmo_x6XIA5cOO1hv5mdHYSuKckpofKuzRsfonGuH3NGGMsppYKyEzSjhWKZZAU_RTPCVZkRxYtzdBFjSwgRgskZWm38J4Q6YsCde7Od23pf4wCj8xiGIXgwW5wO4KpLiziC6_EA42hDj6GH7hBdvERnDXTRXv3MOXp9uN-snrL1y-PzarnOzIKJMSuKBlI04CVbKCoEkdZQA1zVsqlBNlZQCgaUrSUrG1MZVZW04JyzqoBScT5Ht5NvivW-t3HUOxeN7Trord9HzRSXnCwYEQm9-YO2fh9S3m-qJCIlKhKlJsoEH2OwjR6C20E4aEr0sVzd6t9y9bFcPZWblMtJadO_H84GnSDbG1u7YM2oa-_-9fgCM1aG0w</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2739054258</pqid></control><display><type>article</type><title>Towards a likelihood ratio approach for bloodstain pattern analysis</title><source>Access via ScienceDirect (Elsevier)</source><source>ProQuest Central UK/Ireland</source><creator>Zou, Tong ; Stern, Hal S.</creator><creatorcontrib>Zou, Tong ; Stern, Hal S.</creatorcontrib><description>In this work, we explore the application of likelihood ratio as a forensic evidence assessment tool to evaluate the causal mechanism of a bloodstain pattern. It is assumed that there are two competing hypotheses regarding the cause of a bloodstain pattern. The bloodstain patterns are represented as a collection of ellipses with each ellipse characterized by its location, size and orientation. Quantitative measures and features are derived to summarize key aspects of the patterns. A bivariate Gaussian model is chosen to estimate the distribution of features under a given hypothesis and thus approximate the likelihood of a pattern. Published data with 59 impact patterns and 55 gunshot patterns is used to train and evaluate the model. Results demonstrate the feasibility of the likelihood ratio approach for bloodstain pattern analysis. The results also hint at some of the challenges that need to be addressed for future use of the likelihood ratio approach for bloodstain pattern analysis.</description><identifier>ISSN: 0379-0738</identifier><identifier>EISSN: 1872-6283</identifier><identifier>DOI: 10.1016/j.forsciint.2022.111512</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Bivariate analysis ; Blood ; Crime scenes ; Datasets ; Evaluation ; Feature extraction ; Firearms ; Forensic ballistics ; Forensic science ; Forensic sciences ; Forensic statistics ; Hypotheses ; Image processing ; Likelihood ratio ; Pattern analysis ; Statistical modeling</subject><ispartof>Forensic science international, 2022-12, Vol.341, p.111512-111512, Article 111512</ispartof><rights>2022 The Authors</rights><rights>2022. The Authors</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c425t-88fa115a3924715506ec1ca37d6fda6fe511aca7ed629fcbc7b9183332b8a9733</citedby><cites>FETCH-LOGICAL-c425t-88fa115a3924715506ec1ca37d6fda6fe511aca7ed629fcbc7b9183332b8a9733</cites><orcidid>0000-0003-1302-8363</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2739054258?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995,64385,64387,64389,72469</link.rule.ids></links><search><creatorcontrib>Zou, Tong</creatorcontrib><creatorcontrib>Stern, Hal S.</creatorcontrib><title>Towards a likelihood ratio approach for bloodstain pattern analysis</title><title>Forensic science international</title><description>In this work, we explore the application of likelihood ratio as a forensic evidence assessment tool to evaluate the causal mechanism of a bloodstain pattern. It is assumed that there are two competing hypotheses regarding the cause of a bloodstain pattern. The bloodstain patterns are represented as a collection of ellipses with each ellipse characterized by its location, size and orientation. Quantitative measures and features are derived to summarize key aspects of the patterns. A bivariate Gaussian model is chosen to estimate the distribution of features under a given hypothesis and thus approximate the likelihood of a pattern. Published data with 59 impact patterns and 55 gunshot patterns is used to train and evaluate the model. Results demonstrate the feasibility of the likelihood ratio approach for bloodstain pattern analysis. The results also hint at some of the challenges that need to be addressed for future use of the likelihood ratio approach for bloodstain pattern analysis.</description><subject>Bivariate analysis</subject><subject>Blood</subject><subject>Crime scenes</subject><subject>Datasets</subject><subject>Evaluation</subject><subject>Feature extraction</subject><subject>Firearms</subject><subject>Forensic ballistics</subject><subject>Forensic science</subject><subject>Forensic sciences</subject><subject>Forensic statistics</subject><subject>Hypotheses</subject><subject>Image processing</subject><subject>Likelihood ratio</subject><subject>Pattern analysis</subject><subject>Statistical modeling</subject><issn>0379-0738</issn><issn>1872-6283</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkEtPwzAQhC0EEqXwG7DEhUuCH7WdHKuKl1SJSzlbG8dRHdI42Cmo_x6XIA5cOO1hv5mdHYSuKckpofKuzRsfonGuH3NGGMsppYKyEzSjhWKZZAU_RTPCVZkRxYtzdBFjSwgRgskZWm38J4Q6YsCde7Od23pf4wCj8xiGIXgwW5wO4KpLiziC6_EA42hDj6GH7hBdvERnDXTRXv3MOXp9uN-snrL1y-PzarnOzIKJMSuKBlI04CVbKCoEkdZQA1zVsqlBNlZQCgaUrSUrG1MZVZW04JyzqoBScT5Ht5NvivW-t3HUOxeN7Trord9HzRSXnCwYEQm9-YO2fh9S3m-qJCIlKhKlJsoEH2OwjR6C20E4aEr0sVzd6t9y9bFcPZWblMtJadO_H84GnSDbG1u7YM2oa-_-9fgCM1aG0w</recordid><startdate>202212</startdate><enddate>202212</enddate><creator>Zou, Tong</creator><creator>Stern, Hal S.</creator><general>Elsevier B.V</general><general>Elsevier Limited</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QP</scope><scope>7RV</scope><scope>7U7</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1302-8363</orcidid></search><sort><creationdate>202212</creationdate><title>Towards a likelihood ratio approach for bloodstain pattern analysis</title><author>Zou, Tong ; Stern, Hal S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c425t-88fa115a3924715506ec1ca37d6fda6fe511aca7ed629fcbc7b9183332b8a9733</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Bivariate analysis</topic><topic>Blood</topic><topic>Crime scenes</topic><topic>Datasets</topic><topic>Evaluation</topic><topic>Feature extraction</topic><topic>Firearms</topic><topic>Forensic ballistics</topic><topic>Forensic science</topic><topic>Forensic sciences</topic><topic>Forensic statistics</topic><topic>Hypotheses</topic><topic>Image processing</topic><topic>Likelihood ratio</topic><topic>Pattern analysis</topic><topic>Statistical modeling</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zou, Tong</creatorcontrib><creatorcontrib>Stern, Hal S.</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Proquest Nursing & Allied Health Source</collection><collection>Toxicology Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Forensic science international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zou, Tong</au><au>Stern, Hal S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Towards a likelihood ratio approach for bloodstain pattern analysis</atitle><jtitle>Forensic science international</jtitle><date>2022-12</date><risdate>2022</risdate><volume>341</volume><spage>111512</spage><epage>111512</epage><pages>111512-111512</pages><artnum>111512</artnum><issn>0379-0738</issn><eissn>1872-6283</eissn><abstract>In this work, we explore the application of likelihood ratio as a forensic evidence assessment tool to evaluate the causal mechanism of a bloodstain pattern. It is assumed that there are two competing hypotheses regarding the cause of a bloodstain pattern. The bloodstain patterns are represented as a collection of ellipses with each ellipse characterized by its location, size and orientation. Quantitative measures and features are derived to summarize key aspects of the patterns. A bivariate Gaussian model is chosen to estimate the distribution of features under a given hypothesis and thus approximate the likelihood of a pattern. Published data with 59 impact patterns and 55 gunshot patterns is used to train and evaluate the model. Results demonstrate the feasibility of the likelihood ratio approach for bloodstain pattern analysis. The results also hint at some of the challenges that need to be addressed for future use of the likelihood ratio approach for bloodstain pattern analysis.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.forsciint.2022.111512</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-1302-8363</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0379-0738 |
| ispartof | Forensic science international, 2022-12, Vol.341, p.111512-111512, Article 111512 |
| issn | 0379-0738 1872-6283 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2736304205 |
| source | Access via ScienceDirect (Elsevier); ProQuest Central UK/Ireland |
| subjects | Bivariate analysis Blood Crime scenes Datasets Evaluation Feature extraction Firearms Forensic ballistics Forensic science Forensic sciences Forensic statistics Hypotheses Image processing Likelihood ratio Pattern analysis Statistical modeling |
| title | Towards a likelihood ratio approach for bloodstain pattern analysis |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-18T20%3A18%3A40IST&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=Towards%20a%20likelihood%20ratio%20approach%20for%20bloodstain%20pattern%20analysis&rft.jtitle=Forensic%20science%20international&rft.au=Zou,%20Tong&rft.date=2022-12&rft.volume=341&rft.spage=111512&rft.epage=111512&rft.pages=111512-111512&rft.artnum=111512&rft.issn=0379-0738&rft.eissn=1872-6283&rft_id=info:doi/10.1016/j.forsciint.2022.111512&rft_dat=%3Cproquest_cross%3E2736304205%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=2739054258&rft_id=info:pmid/&rft_els_id=S0379073822003425&rfr_iscdi=true |