Indexing visual representations through the complexity map
In differential geometry curves are characterized as mappings from an interval to the plane. In topology curves are characterized as a Hausdorff space with certain countability properties. Neither of these definitions captures the role that curves play in vision, however, in which curves can denote...
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description | In differential geometry curves are characterized as mappings from an interval to the plane. In topology curves are characterized as a Hausdorff space with certain countability properties. Neither of these definitions captures the role that curves play in vision, however, in which curves can denote simple objects (such as a straight line), or complicated objects (such as a jumble of string). The difference between these situations is in part a measure of their complexity, and in part a measure of their dimensionality. Note that the map defining such curves is unknown, as is the proper way to represent them. We propose a formal complexity theory of curves appropriate for computational vision in general, and for problems like separating straight lines from jumbles in particular. The theory is applied to the problem of perceptual grouping.< > |
doi_str_mv | 10.1109/ICCV.1995.466794 |
format | Conference Proceeding |
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In topology curves are characterized as a Hausdorff space with certain countability properties. Neither of these definitions captures the role that curves play in vision, however, in which curves can denote simple objects (such as a straight line), or complicated objects (such as a jumble of string). The difference between these situations is in part a measure of their complexity, and in part a measure of their dimensionality. Note that the map defining such curves is unknown, as is the proper way to represent them. We propose a formal complexity theory of curves appropriate for computational vision in general, and for problems like separating straight lines from jumbles in particular. The theory is applied to the problem of perceptual grouping.< ></description><identifier>ISBN: 9780818670428</identifier><identifier>ISBN: 0818670428</identifier><identifier>DOI: 10.1109/ICCV.1995.466794</identifier><language>eng</language><publisher>IEEE Comput. Soc. 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In topology curves are characterized as a Hausdorff space with certain countability properties. Neither of these definitions captures the role that curves play in vision, however, in which curves can denote simple objects (such as a straight line), or complicated objects (such as a jumble of string). The difference between these situations is in part a measure of their complexity, and in part a measure of their dimensionality. Note that the map defining such curves is unknown, as is the proper way to represent them. We propose a formal complexity theory of curves appropriate for computational vision in general, and for problems like separating straight lines from jumbles in particular. 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Soc. Press</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1995</creationdate><title>Indexing visual representations through the complexity map</title><author>Dubuc, B. ; Zucker, S.W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i104t-e2f2a29ed572bf9452847f68aa6d1f0a1ab6e52838ac320fbe40eda00f6be9ee3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1995</creationdate><topic>Area measurement</topic><topic>Complexity theory</topic><topic>Computer vision</topic><topic>Detectors</topic><topic>Geometry</topic><topic>Hair</topic><topic>Image edge detection</topic><topic>Indexing</topic><topic>Length measurement</topic><topic>Topology</topic><toplevel>online_resources</toplevel><creatorcontrib>Dubuc, B.</creatorcontrib><creatorcontrib>Zucker, S.W.</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></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Dubuc, B.</au><au>Zucker, S.W.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Indexing visual representations through the complexity map</atitle><btitle>Proceedings of IEEE International Conference on Computer Vision</btitle><stitle>ICCV</stitle><date>1995</date><risdate>1995</risdate><spage>142</spage><epage>149</epage><pages>142-149</pages><isbn>9780818670428</isbn><isbn>0818670428</isbn><abstract>In differential geometry curves are characterized as mappings from an interval to the plane. In topology curves are characterized as a Hausdorff space with certain countability properties. Neither of these definitions captures the role that curves play in vision, however, in which curves can denote simple objects (such as a straight line), or complicated objects (such as a jumble of string). The difference between these situations is in part a measure of their complexity, and in part a measure of their dimensionality. Note that the map defining such curves is unknown, as is the proper way to represent them. We propose a formal complexity theory of curves appropriate for computational vision in general, and for problems like separating straight lines from jumbles in particular. The theory is applied to the problem of perceptual grouping.< ></abstract><pub>IEEE Comput. Soc. Press</pub><doi>10.1109/ICCV.1995.466794</doi><tpages>8</tpages></addata></record> |
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subjects | Area measurement Complexity theory Computer vision Detectors Geometry Hair Image edge detection Indexing Length measurement Topology |
title | Indexing visual representations through the complexity map |
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