Canonical decomposition of steerable functions
Steerable functions find application in numerous problems in image processing, computer vision and computer graphics. As such, it is important to develop the appropriate mathematical tools to analyze them. In this paper, we introduce the mathematics of Lie group theory in the context of steerable fu...
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description | Steerable functions find application in numerous problems in image processing, computer vision and computer graphics. As such, it is important to develop the appropriate mathematical tools to analyze them. In this paper, we introduce the mathematics of Lie group theory in the context of steerable functions and present a canonical decomposition of these functions under any transformation group. The theory presented in this paper can be applied and extended in various ways. |
doi_str_mv | 10.1109/CVPR.1996.517165 |
format | Conference Proceeding |
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As such, it is important to develop the appropriate mathematical tools to analyze them. In this paper, we introduce the mathematics of Lie group theory in the context of steerable functions and present a canonical decomposition of these functions under any transformation group. 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As such, it is important to develop the appropriate mathematical tools to analyze them. In this paper, we introduce the mathematics of Lie group theory in the context of steerable functions and present a canonical decomposition of these functions under any transformation group. The theory presented in this paper can be applied and extended in various ways.</description><subject>Computer science</subject><subject>Computer vision</subject><subject>Convolution</subject><subject>Image processing</subject><subject>Kernel</subject><subject>Linear systems</subject><subject>NASA</subject><subject>Nonlinear filters</subject><subject>Pattern recognition</subject><subject>Singular value decomposition</subject><issn>1063-6919</issn><isbn>9780818672590</isbn><isbn>0818672595</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1996</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkL9LxTAURgMq-Hh2F6dObq33pkluM0rxFzxQRF1L0iYQaZva9A3-9yp1OvBx-IbD2CVCiQj6pvl4eS1Ra1VKJFTyhGWaaqixVsSlhlO2Q1BVoTTqc5al9AkAqJUGIXasbMwUp9CZIe9dF8c5prCGOOXR52l1bjF2cLk_Tt3fmi7YmTdDctk_9-z9_u6teSwOzw9Pze2hCFzItSBEB5yM8KK2HYnK9wKNJGmV6HuQ0hoisFIrZclCpXrLJXmua_CAHVR7dr39zkv8Orq0tmNInRsGM7l4TC1XiJyk-hWvNjE459p5CaNZvtutRPUDaZxQXQ</recordid><startdate>1996</startdate><enddate>1996</enddate><creator>Hel-Or, Y.</creator><creator>Teo, P.C.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>1996</creationdate><title>Canonical decomposition of steerable functions</title><author>Hel-Or, Y. ; Teo, P.C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i245t-711e027a4f48bc743fd41a575b64dd055ba770b5966b7b036db257f2980f01c03</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1996</creationdate><topic>Computer science</topic><topic>Computer vision</topic><topic>Convolution</topic><topic>Image processing</topic><topic>Kernel</topic><topic>Linear systems</topic><topic>NASA</topic><topic>Nonlinear filters</topic><topic>Pattern recognition</topic><topic>Singular value decomposition</topic><toplevel>online_resources</toplevel><creatorcontrib>Hel-Or, Y.</creatorcontrib><creatorcontrib>Teo, P.C.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hel-Or, Y.</au><au>Teo, P.C.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Canonical decomposition of steerable functions</atitle><btitle>Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition</btitle><stitle>CVPR</stitle><date>1996</date><risdate>1996</risdate><spage>809</spage><epage>816</epage><pages>809-816</pages><issn>1063-6919</issn><isbn>9780818672590</isbn><isbn>0818672595</isbn><abstract>Steerable functions find application in numerous problems in image processing, computer vision and computer graphics. As such, it is important to develop the appropriate mathematical tools to analyze them. In this paper, we introduce the mathematics of Lie group theory in the context of steerable functions and present a canonical decomposition of these functions under any transformation group. The theory presented in this paper can be applied and extended in various ways.</abstract><pub>IEEE</pub><doi>10.1109/CVPR.1996.517165</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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identifier | ISSN: 1063-6919 |
ispartof | Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1996, p.809-816 |
issn | 1063-6919 |
language | eng |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Computer science Computer vision Convolution Image processing Kernel Linear systems NASA Nonlinear filters Pattern recognition Singular value decomposition |
title | Canonical decomposition of steerable functions |
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