Shape from Specular Flow
An image of a specular (mirror-like) object is nothing but a distorted reflection of its environment. When the environment is unknown, reconstructing shape from such an image can be very difficult. This reconstruction task can be made tractable when, instead of a single image, one observes relative...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 2010-11, Vol.32 (11), p.2054-2070 |
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| creator | Adato, Y Vasilyev, Y Zickler, T Ben-Shahar, O |
| description | An image of a specular (mirror-like) object is nothing but a distorted reflection of its environment. When the environment is unknown, reconstructing shape from such an image can be very difficult. This reconstruction task can be made tractable when, instead of a single image, one observes relative motion between the specular object and its environment, and therefore, a motion field-or specular flow-in the image plane. In this paper, we study the shape from specular flow problem and show that observable specular flow is directly related to surface shape through a nonlinear partial differential equation. This equation has the key property of depending only on the relative motion of the environment while being independent of its content. We take first steps toward understanding and exploiting this PDE, and we examine its qualitative properties in relation to shape geometry. We analyze several cases in which the surface shape can be recovered in closed form, and we show that, under certain conditions, specular shape can be reconstructed when both the relative motion and the content of the environment are unknown. We discuss numerical issues related to the proposed reconstruction algorithms, and we validate our findings using both real and synthetic data. |
| doi_str_mv | 10.1109/TPAMI.2010.126 |
| format | Article |
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When the environment is unknown, reconstructing shape from such an image can be very difficult. This reconstruction task can be made tractable when, instead of a single image, one observes relative motion between the specular object and its environment, and therefore, a motion field-or specular flow-in the image plane. In this paper, we study the shape from specular flow problem and show that observable specular flow is directly related to surface shape through a nonlinear partial differential equation. This equation has the key property of depending only on the relative motion of the environment while being independent of its content. We take first steps toward understanding and exploiting this PDE, and we examine its qualitative properties in relation to shape geometry. We analyze several cases in which the surface shape can be recovered in closed form, and we show that, under certain conditions, specular shape can be reconstructed when both the relative motion and the content of the environment are unknown. We discuss numerical issues related to the proposed reconstruction algorithms, and we validate our findings using both real and synthetic data.</description><identifier>ISSN: 0162-8828</identifier><identifier>EISSN: 1939-3539</identifier><identifier>DOI: 10.1109/TPAMI.2010.126</identifier><identifier>PMID: 20847393</identifier><identifier>CODEN: ITPIDJ</identifier><language>eng</language><publisher>Los Alamitos, CA: IEEE</publisher><subject>Algorithms ; Applied sciences ; Artificial Intelligence ; Computer science; control theory; systems ; Computer Simulation ; Distortion ; environment motion field ; Exact sciences and technology ; Exact solutions ; Gaussian curvature ; Geometry ; Humans ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Image reconstruction ; Imaging, Three-Dimensional - methods ; Mathematical analysis ; Motion ; Motion analysis ; Nonlinear distortion ; Nonlinear equations ; Normal Distribution ; parabolic points ; Partial differential equations ; Pattern Recognition, Automated - methods ; Pattern recognition. Digital image processing. Computational geometry ; Reconstruction ; Reconstruction algorithms ; Reflection ; Shape ; shape reconstruction ; specular curvature ; specular flow ; Specular objects ; Studies ; Surface reconstruction ; Tasks</subject><ispartof>IEEE transactions on pattern analysis and machine intelligence, 2010-11, Vol.32 (11), p.2054-2070</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Nov 2010</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c489t-58360f22c8712b98422a05ceab244e25710eb974431eb6de8b36fcd2b3729c453</citedby><cites>FETCH-LOGICAL-c489t-58360f22c8712b98422a05ceab244e25710eb974431eb6de8b36fcd2b3729c453</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5499479$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5499479$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23327829$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/20847393$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Adato, Y</creatorcontrib><creatorcontrib>Vasilyev, Y</creatorcontrib><creatorcontrib>Zickler, T</creatorcontrib><creatorcontrib>Ben-Shahar, O</creatorcontrib><title>Shape from Specular Flow</title><title>IEEE transactions on pattern analysis and machine intelligence</title><addtitle>TPAMI</addtitle><addtitle>IEEE Trans Pattern Anal Mach Intell</addtitle><description>An image of a specular (mirror-like) object is nothing but a distorted reflection of its environment. When the environment is unknown, reconstructing shape from such an image can be very difficult. This reconstruction task can be made tractable when, instead of a single image, one observes relative motion between the specular object and its environment, and therefore, a motion field-or specular flow-in the image plane. In this paper, we study the shape from specular flow problem and show that observable specular flow is directly related to surface shape through a nonlinear partial differential equation. This equation has the key property of depending only on the relative motion of the environment while being independent of its content. We take first steps toward understanding and exploiting this PDE, and we examine its qualitative properties in relation to shape geometry. We analyze several cases in which the surface shape can be recovered in closed form, and we show that, under certain conditions, specular shape can be reconstructed when both the relative motion and the content of the environment are unknown. We discuss numerical issues related to the proposed reconstruction algorithms, and we validate our findings using both real and synthetic data.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial Intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Computer Simulation</subject><subject>Distortion</subject><subject>environment motion field</subject><subject>Exact sciences and technology</subject><subject>Exact solutions</subject><subject>Gaussian curvature</subject><subject>Geometry</subject><subject>Humans</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Image reconstruction</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Mathematical analysis</subject><subject>Motion</subject><subject>Motion analysis</subject><subject>Nonlinear distortion</subject><subject>Nonlinear equations</subject><subject>Normal Distribution</subject><subject>parabolic points</subject><subject>Partial differential equations</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Reconstruction</subject><subject>Reconstruction algorithms</subject><subject>Reflection</subject><subject>Shape</subject><subject>shape reconstruction</subject><subject>specular curvature</subject><subject>specular flow</subject><subject>Specular objects</subject><subject>Studies</subject><subject>Surface reconstruction</subject><subject>Tasks</subject><issn>0162-8828</issn><issn>1939-3539</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqF0M9LwzAYgOEgipvTqwiCDEQ8deZ3vhzHcDqYKGyeS5ql2NGuM1kR_3tTNyd48RRCnnwkL0LnBA8Iwfpu_jJ8mgwobvdUHqAu0UwnTDB9iLqYSJoAUOigkxCWGBMuMDtGHYqBK6ZZF13M3sza9XNfV_3Z2tmmNL4_LuuPU3SUmzK4s93aQ6_j-_noMZk-P0xGw2liOehNIoBJnFNqQRGaaeCUGiysMxnl3FGhCHaZVpwz4jK5cJAxmdsFzZii2nLBeuh2O3ft6_fGhU1aFcG6sjQrVzchBa45YJD_SyUEAZCgo7z-I5d141fxGynBLFYALElUg62yvg7Buzxd-6Iy_jOitI2bfsdN27hpjBsvXO3GNlnlFnv-UzOCmx0wwZoy92Zli_DrGKMKaPu-y60rnHP7Y8G15kqzL-EHhfQ</recordid><startdate>20101101</startdate><enddate>20101101</enddate><creator>Adato, Y</creator><creator>Vasilyev, Y</creator><creator>Zickler, T</creator><creator>Ben-Shahar, O</creator><general>IEEE</general><general>IEEE Computer Society</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Digital image processing. Computational geometry</topic><topic>Reconstruction</topic><topic>Reconstruction algorithms</topic><topic>Reflection</topic><topic>Shape</topic><topic>shape reconstruction</topic><topic>specular curvature</topic><topic>specular flow</topic><topic>Specular objects</topic><topic>Studies</topic><topic>Surface reconstruction</topic><topic>Tasks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Adato, Y</creatorcontrib><creatorcontrib>Vasilyev, Y</creatorcontrib><creatorcontrib>Zickler, T</creatorcontrib><creatorcontrib>Ben-Shahar, O</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications 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><collection>MEDLINE - Academic</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Adato, Y</au><au>Vasilyev, Y</au><au>Zickler, T</au><au>Ben-Shahar, O</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Shape from Specular Flow</atitle><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle><stitle>TPAMI</stitle><addtitle>IEEE Trans Pattern Anal Mach Intell</addtitle><date>2010-11-01</date><risdate>2010</risdate><volume>32</volume><issue>11</issue><spage>2054</spage><epage>2070</epage><pages>2054-2070</pages><issn>0162-8828</issn><eissn>1939-3539</eissn><coden>ITPIDJ</coden><abstract>An image of a specular (mirror-like) object is nothing but a distorted reflection of its environment. When the environment is unknown, reconstructing shape from such an image can be very difficult. This reconstruction task can be made tractable when, instead of a single image, one observes relative motion between the specular object and its environment, and therefore, a motion field-or specular flow-in the image plane. In this paper, we study the shape from specular flow problem and show that observable specular flow is directly related to surface shape through a nonlinear partial differential equation. This equation has the key property of depending only on the relative motion of the environment while being independent of its content. We take first steps toward understanding and exploiting this PDE, and we examine its qualitative properties in relation to shape geometry. 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| ispartof | IEEE transactions on pattern analysis and machine intelligence, 2010-11, Vol.32 (11), p.2054-2070 |
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| subjects | Algorithms Applied sciences Artificial Intelligence Computer science control theory systems Computer Simulation Distortion environment motion field Exact sciences and technology Exact solutions Gaussian curvature Geometry Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image reconstruction Imaging, Three-Dimensional - methods Mathematical analysis Motion Motion analysis Nonlinear distortion Nonlinear equations Normal Distribution parabolic points Partial differential equations Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Reconstruction Reconstruction algorithms Reflection Shape shape reconstruction specular curvature specular flow Specular objects Studies Surface reconstruction Tasks |
| title | Shape from Specular Flow |
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