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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier6qtX3pFwXQZeBBx/w9xB2
Repositorysid.inpe.br/banon/2002/11.13.11.36
Last Update2002:11.13.02.00.00 (UTC) administrator
Metadata Repositorysid.inpe.br/banon/2002/11.13.11.36.54
Metadata Last Update2022:06.14.00.12.14 (UTC) administrator
DOI10.1109/SIBGRA.2000.883917
Citation KeyNehabGatt:2000:RaPaCa
TitleRay path categorization
Year2000
Access Date2024, May 04
Number of Files1
Size883 KiB
2. Context
Author1 Nehab, Diego
2 Gattass, Marcelo
EditorCarvalho, Paulo Cezar Pinto
Walter, Marcelo
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 13 (SIBGRAPI)
Conference LocationGramado, RS, Brazil
Date17-20 Oct. 2000
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Pages227-234
Book TitleProceedings
Tertiary TypeFull Paper
OrganizationSBC - Brazilian Computer Society
History (UTC)2008-07-17 14:10:51 :: administrator -> banon ::
2008-08-26 15:23:02 :: banon -> administrator ::
2009-08-13 20:37:02 :: administrator -> banon ::
2010-08-28 20:00:11 :: banon -> administrator ::
2022-06-14 00:12:14 :: administrator -> :: 2000
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsedge detection
ray path categorization
edge detection
image segmentation algorithms
geometrical information extraction
pixel colors
ray-traced images
geometrical information
image rendering
equivalence classes
category information
rendering process
detected edges
aliasing
ray tracer
memory requirements
AbstractEdge detection and image segmentation algorithms usually operate on an image to extract geometrical information based on pixel colors. For ray-traced images, the presence of geometrical information on the scene from which the image was rendered allows for a completely different approach. We present an algorithm that divides rays into equivalence classes, or categories. The category information is generated during the rendering process and used to determine edges in the resulting image. Detected edges can later be used to help determine areas subject to aliasing. Little effort is needed to implement the described algorithms over an existing ray tracer. Furthermore, the extra computational and memory requirements are modest.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2000 > Ray path categorization
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Ray path categorization
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
data URLhttp://urlib.net/ibi/6qtX3pFwXQZeBBx/w9xB2
zipped data URLhttp://urlib.net/zip/6qtX3pFwXQZeBBx/w9xB2
Target File227-234.pdf
User Groupadministrator
Visibilityshown
5. Allied materials
Next Higher Units8JMKD3MGPEW34M/46PN6AP
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/04.27.03.08 6
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
NotesThe conference was held in Gramado, RS, Brazil, from October 17 to 20.
Empty Fieldsaffiliation archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage e-mailaddress edition electronicmailaddress format group isbn issn label language lineage mark mirrorrepository nextedition numberofvolumes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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