A Robust Approach for Structure from Planar Motion by Stereo Image Sequences
Abstract This paper proposes a robust method for recovery of motion and structure from two image sequences taken by stereo cameras undergoing a planar motion. The feature correspondences between images are extracted and refined automatically by the relation of the stereo cameras and the property of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chen, Tai [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2006 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer-Verlag 2006 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Machine vision and applications - Springer-Verlag, 1988, 17(2006), 3 vom: 13. Juni, Seite 197-209 |
|---|---|
| Übergeordnetes Werk: |
volume:17 ; year:2006 ; number:3 ; day:13 ; month:06 ; pages:197-209 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00138-006-0031-5 |
|---|
| Katalog-ID: |
OLC2074623224 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2074623224 | ||
| 003 | DE-627 | ||
| 005 | 20230401063122.0 | ||
| 007 | tu | ||
| 008 | 200820s2006 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00138-006-0031-5 |2 doi | |
| 035 | |a (DE-627)OLC2074623224 | ||
| 035 | |a (DE-He213)s00138-006-0031-5-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |q VZ |
| 084 | |a 11 |2 ssgn | ||
| 100 | 1 | |a Chen, Tai |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a A Robust Approach for Structure from Planar Motion by Stereo Image Sequences |
| 264 | 1 | |c 2006 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer-Verlag 2006 | ||
| 520 | |a Abstract This paper proposes a robust method for recovery of motion and structure from two image sequences taken by stereo cameras undergoing a planar motion. The feature correspondences between images are extracted and refined automatically by the relation of the stereo cameras and the property of the motion. To improve the robustness, an auto-scale random sample consensus (RANSAC) algorithm is adopted in the motion and structure estimation. Unlike other work recovering epipolar geometry, here we use a random sampling algorithm to recover the 2D motion and to exclude the outliers which lie both on and out of the epipolar lines. Further more, the idea of RANSAC is used in structure estimation to exclude the outliers from the image sequence. The contribution of this work is the development of an approach to make structure and motion estimation more robust and efficient so as to be applicable to real applications. With the adoption of the auto-scale technique, the algorithm completely automates the estimation process without any prior information or user’s specification of parameters like thresholds. Indoor and outdoor experiments have been done to verify the performance of the algorithm. The results demonstrated that the proposed algorithm is robust and efficient for applications in planar motions. | ||
| 650 | 4 | |a Planar motion | |
| 650 | 4 | |a Random sampling | |
| 650 | 4 | |a Auto-scale | |
| 650 | 4 | |a Stereo cameras | |
| 700 | 1 | |a Liu, Yun-Hui |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Machine vision and applications |d Springer-Verlag, 1988 |g 17(2006), 3 vom: 13. Juni, Seite 197-209 |w (DE-627)129248843 |w (DE-600)59385-0 |w (DE-576)017944139 |x 0932-8092 |7 nnns |
| 773 | 1 | 8 | |g volume:17 |g year:2006 |g number:3 |g day:13 |g month:06 |g pages:197-209 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s00138-006-0031-5 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a GBV_ILN_21 | ||
| 912 | |a GBV_ILN_32 | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_100 | ||
| 912 | |a GBV_ILN_2015 | ||
| 912 | |a GBV_ILN_2018 | ||
| 912 | |a GBV_ILN_2020 | ||
| 912 | |a GBV_ILN_4116 | ||
| 912 | |a GBV_ILN_4277 | ||
| 912 | |a GBV_ILN_4307 | ||
| 912 | |a GBV_ILN_4313 | ||
| 951 | |a AR | ||
| 952 | |d 17 |j 2006 |e 3 |b 13 |c 06 |h 197-209 | ||
| author_variant |
t c tc y h l yhl |
|---|---|
| matchkey_str |
article:09328092:2006----::rbsapocfrtutrfopaamtobse |
| hierarchy_sort_str |
2006 |
| publishDate |
2006 |
| allfields |
10.1007/s00138-006-0031-5 doi (DE-627)OLC2074623224 (DE-He213)s00138-006-0031-5-p DE-627 ger DE-627 rakwb eng 004 VZ 11 ssgn Chen, Tai verfasserin aut A Robust Approach for Structure from Planar Motion by Stereo Image Sequences 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2006 Abstract This paper proposes a robust method for recovery of motion and structure from two image sequences taken by stereo cameras undergoing a planar motion. The feature correspondences between images are extracted and refined automatically by the relation of the stereo cameras and the property of the motion. To improve the robustness, an auto-scale random sample consensus (RANSAC) algorithm is adopted in the motion and structure estimation. Unlike other work recovering epipolar geometry, here we use a random sampling algorithm to recover the 2D motion and to exclude the outliers which lie both on and out of the epipolar lines. Further more, the idea of RANSAC is used in structure estimation to exclude the outliers from the image sequence. The contribution of this work is the development of an approach to make structure and motion estimation more robust and efficient so as to be applicable to real applications. With the adoption of the auto-scale technique, the algorithm completely automates the estimation process without any prior information or user’s specification of parameters like thresholds. Indoor and outdoor experiments have been done to verify the performance of the algorithm. The results demonstrated that the proposed algorithm is robust and efficient for applications in planar motions. Planar motion Random sampling Auto-scale Stereo cameras Liu, Yun-Hui aut Enthalten in Machine vision and applications Springer-Verlag, 1988 17(2006), 3 vom: 13. Juni, Seite 197-209 (DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 0932-8092 nnns volume:17 year:2006 number:3 day:13 month:06 pages:197-209 https://doi.org/10.1007/s00138-006-0031-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_32 GBV_ILN_40 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4116 GBV_ILN_4277 GBV_ILN_4307 GBV_ILN_4313 AR 17 2006 3 13 06 197-209 |
| spelling |
10.1007/s00138-006-0031-5 doi (DE-627)OLC2074623224 (DE-He213)s00138-006-0031-5-p DE-627 ger DE-627 rakwb eng 004 VZ 11 ssgn Chen, Tai verfasserin aut A Robust Approach for Structure from Planar Motion by Stereo Image Sequences 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2006 Abstract This paper proposes a robust method for recovery of motion and structure from two image sequences taken by stereo cameras undergoing a planar motion. The feature correspondences between images are extracted and refined automatically by the relation of the stereo cameras and the property of the motion. To improve the robustness, an auto-scale random sample consensus (RANSAC) algorithm is adopted in the motion and structure estimation. Unlike other work recovering epipolar geometry, here we use a random sampling algorithm to recover the 2D motion and to exclude the outliers which lie both on and out of the epipolar lines. Further more, the idea of RANSAC is used in structure estimation to exclude the outliers from the image sequence. The contribution of this work is the development of an approach to make structure and motion estimation more robust and efficient so as to be applicable to real applications. With the adoption of the auto-scale technique, the algorithm completely automates the estimation process without any prior information or user’s specification of parameters like thresholds. Indoor and outdoor experiments have been done to verify the performance of the algorithm. The results demonstrated that the proposed algorithm is robust and efficient for applications in planar motions. Planar motion Random sampling Auto-scale Stereo cameras Liu, Yun-Hui aut Enthalten in Machine vision and applications Springer-Verlag, 1988 17(2006), 3 vom: 13. Juni, Seite 197-209 (DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 0932-8092 nnns volume:17 year:2006 number:3 day:13 month:06 pages:197-209 https://doi.org/10.1007/s00138-006-0031-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_32 GBV_ILN_40 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4116 GBV_ILN_4277 GBV_ILN_4307 GBV_ILN_4313 AR 17 2006 3 13 06 197-209 |
| allfields_unstemmed |
10.1007/s00138-006-0031-5 doi (DE-627)OLC2074623224 (DE-He213)s00138-006-0031-5-p DE-627 ger DE-627 rakwb eng 004 VZ 11 ssgn Chen, Tai verfasserin aut A Robust Approach for Structure from Planar Motion by Stereo Image Sequences 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2006 Abstract This paper proposes a robust method for recovery of motion and structure from two image sequences taken by stereo cameras undergoing a planar motion. The feature correspondences between images are extracted and refined automatically by the relation of the stereo cameras and the property of the motion. To improve the robustness, an auto-scale random sample consensus (RANSAC) algorithm is adopted in the motion and structure estimation. Unlike other work recovering epipolar geometry, here we use a random sampling algorithm to recover the 2D motion and to exclude the outliers which lie both on and out of the epipolar lines. Further more, the idea of RANSAC is used in structure estimation to exclude the outliers from the image sequence. The contribution of this work is the development of an approach to make structure and motion estimation more robust and efficient so as to be applicable to real applications. With the adoption of the auto-scale technique, the algorithm completely automates the estimation process without any prior information or user’s specification of parameters like thresholds. Indoor and outdoor experiments have been done to verify the performance of the algorithm. The results demonstrated that the proposed algorithm is robust and efficient for applications in planar motions. Planar motion Random sampling Auto-scale Stereo cameras Liu, Yun-Hui aut Enthalten in Machine vision and applications Springer-Verlag, 1988 17(2006), 3 vom: 13. Juni, Seite 197-209 (DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 0932-8092 nnns volume:17 year:2006 number:3 day:13 month:06 pages:197-209 https://doi.org/10.1007/s00138-006-0031-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_32 GBV_ILN_40 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4116 GBV_ILN_4277 GBV_ILN_4307 GBV_ILN_4313 AR 17 2006 3 13 06 197-209 |
| allfieldsGer |
10.1007/s00138-006-0031-5 doi (DE-627)OLC2074623224 (DE-He213)s00138-006-0031-5-p DE-627 ger DE-627 rakwb eng 004 VZ 11 ssgn Chen, Tai verfasserin aut A Robust Approach for Structure from Planar Motion by Stereo Image Sequences 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2006 Abstract This paper proposes a robust method for recovery of motion and structure from two image sequences taken by stereo cameras undergoing a planar motion. The feature correspondences between images are extracted and refined automatically by the relation of the stereo cameras and the property of the motion. To improve the robustness, an auto-scale random sample consensus (RANSAC) algorithm is adopted in the motion and structure estimation. Unlike other work recovering epipolar geometry, here we use a random sampling algorithm to recover the 2D motion and to exclude the outliers which lie both on and out of the epipolar lines. Further more, the idea of RANSAC is used in structure estimation to exclude the outliers from the image sequence. The contribution of this work is the development of an approach to make structure and motion estimation more robust and efficient so as to be applicable to real applications. With the adoption of the auto-scale technique, the algorithm completely automates the estimation process without any prior information or user’s specification of parameters like thresholds. Indoor and outdoor experiments have been done to verify the performance of the algorithm. The results demonstrated that the proposed algorithm is robust and efficient for applications in planar motions. Planar motion Random sampling Auto-scale Stereo cameras Liu, Yun-Hui aut Enthalten in Machine vision and applications Springer-Verlag, 1988 17(2006), 3 vom: 13. Juni, Seite 197-209 (DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 0932-8092 nnns volume:17 year:2006 number:3 day:13 month:06 pages:197-209 https://doi.org/10.1007/s00138-006-0031-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_32 GBV_ILN_40 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4116 GBV_ILN_4277 GBV_ILN_4307 GBV_ILN_4313 AR 17 2006 3 13 06 197-209 |
| allfieldsSound |
10.1007/s00138-006-0031-5 doi (DE-627)OLC2074623224 (DE-He213)s00138-006-0031-5-p DE-627 ger DE-627 rakwb eng 004 VZ 11 ssgn Chen, Tai verfasserin aut A Robust Approach for Structure from Planar Motion by Stereo Image Sequences 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2006 Abstract This paper proposes a robust method for recovery of motion and structure from two image sequences taken by stereo cameras undergoing a planar motion. The feature correspondences between images are extracted and refined automatically by the relation of the stereo cameras and the property of the motion. To improve the robustness, an auto-scale random sample consensus (RANSAC) algorithm is adopted in the motion and structure estimation. Unlike other work recovering epipolar geometry, here we use a random sampling algorithm to recover the 2D motion and to exclude the outliers which lie both on and out of the epipolar lines. Further more, the idea of RANSAC is used in structure estimation to exclude the outliers from the image sequence. The contribution of this work is the development of an approach to make structure and motion estimation more robust and efficient so as to be applicable to real applications. With the adoption of the auto-scale technique, the algorithm completely automates the estimation process without any prior information or user’s specification of parameters like thresholds. Indoor and outdoor experiments have been done to verify the performance of the algorithm. The results demonstrated that the proposed algorithm is robust and efficient for applications in planar motions. Planar motion Random sampling Auto-scale Stereo cameras Liu, Yun-Hui aut Enthalten in Machine vision and applications Springer-Verlag, 1988 17(2006), 3 vom: 13. Juni, Seite 197-209 (DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 0932-8092 nnns volume:17 year:2006 number:3 day:13 month:06 pages:197-209 https://doi.org/10.1007/s00138-006-0031-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_32 GBV_ILN_40 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4116 GBV_ILN_4277 GBV_ILN_4307 GBV_ILN_4313 AR 17 2006 3 13 06 197-209 |
| language |
English |
| source |
Enthalten in Machine vision and applications 17(2006), 3 vom: 13. Juni, Seite 197-209 volume:17 year:2006 number:3 day:13 month:06 pages:197-209 |
| sourceStr |
Enthalten in Machine vision and applications 17(2006), 3 vom: 13. Juni, Seite 197-209 volume:17 year:2006 number:3 day:13 month:06 pages:197-209 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Planar motion Random sampling Auto-scale Stereo cameras |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Machine vision and applications |
| authorswithroles_txt_mv |
Chen, Tai @@aut@@ Liu, Yun-Hui @@aut@@ |
| publishDateDaySort_date |
2006-06-13T00:00:00Z |
| hierarchy_top_id |
129248843 |
| dewey-sort |
14 |
| id |
OLC2074623224 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2074623224</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230401063122.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2006 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00138-006-0031-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2074623224</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00138-006-0031-5-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">11</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Chen, Tai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A Robust Approach for Structure from Planar Motion by Stereo Image Sequences</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2006</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag 2006</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper proposes a robust method for recovery of motion and structure from two image sequences taken by stereo cameras undergoing a planar motion. The feature correspondences between images are extracted and refined automatically by the relation of the stereo cameras and the property of the motion. To improve the robustness, an auto-scale random sample consensus (RANSAC) algorithm is adopted in the motion and structure estimation. Unlike other work recovering epipolar geometry, here we use a random sampling algorithm to recover the 2D motion and to exclude the outliers which lie both on and out of the epipolar lines. Further more, the idea of RANSAC is used in structure estimation to exclude the outliers from the image sequence. The contribution of this work is the development of an approach to make structure and motion estimation more robust and efficient so as to be applicable to real applications. With the adoption of the auto-scale technique, the algorithm completely automates the estimation process without any prior information or user’s specification of parameters like thresholds. Indoor and outdoor experiments have been done to verify the performance of the algorithm. The results demonstrated that the proposed algorithm is robust and efficient for applications in planar motions.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Planar motion</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Random sampling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Auto-scale</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stereo cameras</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Yun-Hui</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Machine vision and applications</subfield><subfield code="d">Springer-Verlag, 1988</subfield><subfield code="g">17(2006), 3 vom: 13. Juni, Seite 197-209</subfield><subfield code="w">(DE-627)129248843</subfield><subfield code="w">(DE-600)59385-0</subfield><subfield code="w">(DE-576)017944139</subfield><subfield code="x">0932-8092</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:17</subfield><subfield code="g">year:2006</subfield><subfield code="g">number:3</subfield><subfield code="g">day:13</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:197-209</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00138-006-0031-5</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4116</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">17</subfield><subfield code="j">2006</subfield><subfield code="e">3</subfield><subfield code="b">13</subfield><subfield code="c">06</subfield><subfield code="h">197-209</subfield></datafield></record></collection>
|
| author |
Chen, Tai |
| spellingShingle |
Chen, Tai ddc 004 ssgn 11 misc Planar motion misc Random sampling misc Auto-scale misc Stereo cameras A Robust Approach for Structure from Planar Motion by Stereo Image Sequences |
| authorStr |
Chen, Tai |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)129248843 |
| format |
Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0932-8092 |
| topic_title |
004 VZ 11 ssgn A Robust Approach for Structure from Planar Motion by Stereo Image Sequences Planar motion Random sampling Auto-scale Stereo cameras |
| topic |
ddc 004 ssgn 11 misc Planar motion misc Random sampling misc Auto-scale misc Stereo cameras |
| topic_unstemmed |
ddc 004 ssgn 11 misc Planar motion misc Random sampling misc Auto-scale misc Stereo cameras |
| topic_browse |
ddc 004 ssgn 11 misc Planar motion misc Random sampling misc Auto-scale misc Stereo cameras |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Machine vision and applications |
| hierarchy_parent_id |
129248843 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Machine vision and applications |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 |
| title |
A Robust Approach for Structure from Planar Motion by Stereo Image Sequences |
| ctrlnum |
(DE-627)OLC2074623224 (DE-He213)s00138-006-0031-5-p |
| title_full |
A Robust Approach for Structure from Planar Motion by Stereo Image Sequences |
| author_sort |
Chen, Tai |
| journal |
Machine vision and applications |
| journalStr |
Machine vision and applications |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2006 |
| contenttype_str_mv |
txt |
| container_start_page |
197 |
| author_browse |
Chen, Tai Liu, Yun-Hui |
| container_volume |
17 |
| class |
004 VZ 11 ssgn |
| format_se |
Aufsätze |
| author-letter |
Chen, Tai |
| doi_str_mv |
10.1007/s00138-006-0031-5 |
| dewey-full |
004 |
| title_sort |
a robust approach for structure from planar motion by stereo image sequences |
| title_auth |
A Robust Approach for Structure from Planar Motion by Stereo Image Sequences |
| abstract |
Abstract This paper proposes a robust method for recovery of motion and structure from two image sequences taken by stereo cameras undergoing a planar motion. The feature correspondences between images are extracted and refined automatically by the relation of the stereo cameras and the property of the motion. To improve the robustness, an auto-scale random sample consensus (RANSAC) algorithm is adopted in the motion and structure estimation. Unlike other work recovering epipolar geometry, here we use a random sampling algorithm to recover the 2D motion and to exclude the outliers which lie both on and out of the epipolar lines. Further more, the idea of RANSAC is used in structure estimation to exclude the outliers from the image sequence. The contribution of this work is the development of an approach to make structure and motion estimation more robust and efficient so as to be applicable to real applications. With the adoption of the auto-scale technique, the algorithm completely automates the estimation process without any prior information or user’s specification of parameters like thresholds. Indoor and outdoor experiments have been done to verify the performance of the algorithm. The results demonstrated that the proposed algorithm is robust and efficient for applications in planar motions. © Springer-Verlag 2006 |
| abstractGer |
Abstract This paper proposes a robust method for recovery of motion and structure from two image sequences taken by stereo cameras undergoing a planar motion. The feature correspondences between images are extracted and refined automatically by the relation of the stereo cameras and the property of the motion. To improve the robustness, an auto-scale random sample consensus (RANSAC) algorithm is adopted in the motion and structure estimation. Unlike other work recovering epipolar geometry, here we use a random sampling algorithm to recover the 2D motion and to exclude the outliers which lie both on and out of the epipolar lines. Further more, the idea of RANSAC is used in structure estimation to exclude the outliers from the image sequence. The contribution of this work is the development of an approach to make structure and motion estimation more robust and efficient so as to be applicable to real applications. With the adoption of the auto-scale technique, the algorithm completely automates the estimation process without any prior information or user’s specification of parameters like thresholds. Indoor and outdoor experiments have been done to verify the performance of the algorithm. The results demonstrated that the proposed algorithm is robust and efficient for applications in planar motions. © Springer-Verlag 2006 |
| abstract_unstemmed |
Abstract This paper proposes a robust method for recovery of motion and structure from two image sequences taken by stereo cameras undergoing a planar motion. The feature correspondences between images are extracted and refined automatically by the relation of the stereo cameras and the property of the motion. To improve the robustness, an auto-scale random sample consensus (RANSAC) algorithm is adopted in the motion and structure estimation. Unlike other work recovering epipolar geometry, here we use a random sampling algorithm to recover the 2D motion and to exclude the outliers which lie both on and out of the epipolar lines. Further more, the idea of RANSAC is used in structure estimation to exclude the outliers from the image sequence. The contribution of this work is the development of an approach to make structure and motion estimation more robust and efficient so as to be applicable to real applications. With the adoption of the auto-scale technique, the algorithm completely automates the estimation process without any prior information or user’s specification of parameters like thresholds. Indoor and outdoor experiments have been done to verify the performance of the algorithm. The results demonstrated that the proposed algorithm is robust and efficient for applications in planar motions. © Springer-Verlag 2006 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_21 GBV_ILN_32 GBV_ILN_40 GBV_ILN_70 GBV_ILN_100 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_4116 GBV_ILN_4277 GBV_ILN_4307 GBV_ILN_4313 |
| container_issue |
3 |
| title_short |
A Robust Approach for Structure from Planar Motion by Stereo Image Sequences |
| url |
https://doi.org/10.1007/s00138-006-0031-5 |
| remote_bool |
false |
| author2 |
Liu, Yun-Hui |
| author2Str |
Liu, Yun-Hui |
| ppnlink |
129248843 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00138-006-0031-5 |
| up_date |
2024-07-03T22:52:36.441Z |
| _version_ |
1803600167113326592 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2074623224</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230401063122.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2006 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00138-006-0031-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2074623224</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00138-006-0031-5-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">11</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Chen, Tai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A Robust Approach for Structure from Planar Motion by Stereo Image Sequences</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2006</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag 2006</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper proposes a robust method for recovery of motion and structure from two image sequences taken by stereo cameras undergoing a planar motion. The feature correspondences between images are extracted and refined automatically by the relation of the stereo cameras and the property of the motion. To improve the robustness, an auto-scale random sample consensus (RANSAC) algorithm is adopted in the motion and structure estimation. Unlike other work recovering epipolar geometry, here we use a random sampling algorithm to recover the 2D motion and to exclude the outliers which lie both on and out of the epipolar lines. Further more, the idea of RANSAC is used in structure estimation to exclude the outliers from the image sequence. The contribution of this work is the development of an approach to make structure and motion estimation more robust and efficient so as to be applicable to real applications. With the adoption of the auto-scale technique, the algorithm completely automates the estimation process without any prior information or user’s specification of parameters like thresholds. Indoor and outdoor experiments have been done to verify the performance of the algorithm. The results demonstrated that the proposed algorithm is robust and efficient for applications in planar motions.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Planar motion</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Random sampling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Auto-scale</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stereo cameras</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Yun-Hui</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Machine vision and applications</subfield><subfield code="d">Springer-Verlag, 1988</subfield><subfield code="g">17(2006), 3 vom: 13. Juni, Seite 197-209</subfield><subfield code="w">(DE-627)129248843</subfield><subfield code="w">(DE-600)59385-0</subfield><subfield code="w">(DE-576)017944139</subfield><subfield code="x">0932-8092</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:17</subfield><subfield code="g">year:2006</subfield><subfield code="g">number:3</subfield><subfield code="g">day:13</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:197-209</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00138-006-0031-5</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4116</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">17</subfield><subfield code="j">2006</subfield><subfield code="e">3</subfield><subfield code="b">13</subfield><subfield code="c">06</subfield><subfield code="h">197-209</subfield></datafield></record></collection>
|
| score |
7.402647 |