Visual-Tactile Fusion for Object Recognition

The camera provides rich visual information regarding objects and becomes one of the most mainstream sensors in the automation community. However, it is often difficult to be applicable when the objects are not visually distinguished. On the other hand, tactile sensors can be used to capture multipl...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on automation science and engineering Vol. 14; no. 2; pp. 996 - 1008
Main Authors:	Huaping Liu, Yuanlong Yu, Fuchun Sun, Gu, Jason
Format:	Journal Article
Language:	English
Published:	IEEE 01.04.2017
Subjects:	Automation Joint sparse coding Manipulators Object recognition tactile perception Tactile sensors Training visual perception Visualization
ISSN:	1545-5955, 1558-3783
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	The camera provides rich visual information regarding objects and becomes one of the most mainstream sensors in the automation community. However, it is often difficult to be applicable when the objects are not visually distinguished. On the other hand, tactile sensors can be used to capture multiple object properties, such as textures, roughness, spatial features, compliance, and friction, and therefore provide another important modality for the perception. Nevertheless, effective combination of the visual and tactile modalities is still a challenging problem. In this paper, we develop a visual-tactile fusion framework for object recognition tasks. This paper uses the multivariate-time-series model to represent the tactile sequence and the covariance descriptor to characterize the image. Further, we design a joint group kernel sparse coding (JGKSC) method to tackle the intrinsically weak pairing problem in visual-tactile data samples. Finally, we develop a visual-tactile data set, composed of 18 household objects for validation. The experimental results show that considering both visual and tactile inputs is beneficial and the proposed method indeed provides an effective strategy for fusion.
AbstractList	The camera provides rich visual information regarding objects and becomes one of the most mainstream sensors in the automation community. However, it is often difficult to be applicable when the objects are not visually distinguished. On the other hand, tactile sensors can be used to capture multiple object properties, such as textures, roughness, spatial features, compliance, and friction, and therefore provide another important modality for the perception. Nevertheless, effective combination of the visual and tactile modalities is still a challenging problem. In this paper, we develop a visual-tactile fusion framework for object recognition tasks. This paper uses the multivariate-time-series model to represent the tactile sequence and the covariance descriptor to characterize the image. Further, we design a joint group kernel sparse coding (JGKSC) method to tackle the intrinsically weak pairing problem in visual-tactile data samples. Finally, we develop a visual-tactile data set, composed of 18 household objects for validation. The experimental results show that considering both visual and tactile inputs is beneficial and the proposed method indeed provides an effective strategy for fusion.
Author	Gu, Jason Fuchun Sun Huaping Liu Yuanlong Yu
Author_xml	– sequence: 1 surname: Huaping Liu fullname: Huaping Liu email: hpliu@tsinghua.edu.cn organization: Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China – sequence: 2 surname: Yuanlong Yu fullname: Yuanlong Yu email: yu.yuanlong@fzu.edu.cn organization: Coll. of Comput., Fuzhou Univ., Fuzhou, China – sequence: 3 surname: Fuchun Sun fullname: Fuchun Sun email: fcsun@tsinghua.edu.cn organization: Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China – sequence: 4 givenname: Jason surname: Gu fullname: Gu, Jason email: jason.gu@dal.ca organization: Dept. of Electr. & Comput. Eng., Dalhousie Univ., Halifax, NS, Canada
BookMark	eNp9j81OwzAMgCM0JLbBAyAufQAy8lMn6XGaNkCaNAkK1ypLHZSptKjJDrw9rTZx4MDJlu3P9jcjk7ZrkZBbzhacs-KhXL6uF4JxtRCQFwDigkw5gKFSGzkZ8xwoDI0rMovxwJjITcGm5P49xKNtaGldCg1mm2MMXZv5rs92-wO6lL2g6z7akIbyNbn0tol4c45z8rZZl6snut09Pq-WW-qEgkRFjspyCU56xjlKzTQyUysz3PessFbn0itdFzjM2BrtHgwX3AsANUAg54Sf9rq-i7FHX3314dP23xVn1ahbjbrVqFuddQdG_2FcSHb8OvU2NP-SdycyIOLvJZ0rIZiRP9SSYyg
CODEN	ITASC7
CitedBy_id	crossref_primary_10_1109_JPROC_2019_2930808 crossref_primary_10_1109_TCDS_2018_2819826 crossref_primary_10_1109_JSYST_2023_3317328 crossref_primary_10_1109_TSMC_2023_3279023 crossref_primary_10_1016_j_ins_2017_09_010 crossref_primary_10_3389_fnbot_2019_00073 crossref_primary_10_1016_j_patcog_2021_108176 crossref_primary_10_1007_s11432_017_9214_8 crossref_primary_10_1109_ACCESS_2019_2959011 crossref_primary_10_1016_j_neucom_2017_01_113 crossref_primary_10_1109_TMECH_2021_3080378 crossref_primary_10_1177_1729881417740711 crossref_primary_10_1109_TASE_2020_3017022 crossref_primary_10_1007_s11370_025_00628_8 crossref_primary_10_1016_j_cej_2022_140890 crossref_primary_10_1109_TNNLS_2020_2980892 crossref_primary_10_1177_1729881417716043 crossref_primary_10_1109_TASE_2018_2847222 crossref_primary_10_1038_s41598_024_78764_x crossref_primary_10_1109_TSMC_2021_3096235 crossref_primary_10_1016_j_bspc_2019_101810 crossref_primary_10_1109_LRA_2021_3136657 crossref_primary_10_1108_IR_06_2018_0114 crossref_primary_10_1109_TCDS_2018_2868425 crossref_primary_10_1016_j_inffus_2019_09_004 crossref_primary_10_1093_comjnl_bxaa045 crossref_primary_10_1109_TNNLS_2018_2877468 crossref_primary_10_1109_TIE_2016_2643625 crossref_primary_10_3389_fnbot_2023_1181383 crossref_primary_10_3390_s21082716 crossref_primary_10_1109_LRA_2020_3038377 crossref_primary_10_1007_s12559_017_9471_7 crossref_primary_10_1109_TIM_2024_3386205 crossref_primary_10_1109_TOH_2020_3011899 crossref_primary_10_1109_LRA_2020_3010720 crossref_primary_10_1109_TCDS_2018_2883653 crossref_primary_10_1109_TASE_2019_2892081 crossref_primary_10_1007_s00138_023_01374_6 crossref_primary_10_1002_aisy_202100074 crossref_primary_10_1109_ACCESS_2020_3028740 crossref_primary_10_1109_TIE_2017_2745445 crossref_primary_10_1016_j_future_2020_08_016 crossref_primary_10_1016_j_compag_2024_108619 crossref_primary_10_1109_LRA_2018_2818933 crossref_primary_10_1109_TSMC_2016_2635141 crossref_primary_10_1109_TASE_2024_3440047 crossref_primary_10_1007_s12555_016_0720_4 crossref_primary_10_1109_LRA_2024_3505783 crossref_primary_10_1063_5_0176343 crossref_primary_10_1016_j_ins_2017_09_068 crossref_primary_10_1177_1729881418825093 crossref_primary_10_1016_j_engappai_2025_111064 crossref_primary_10_1016_j_ins_2017_08_035 crossref_primary_10_1007_s12559_017_9504_2 crossref_primary_10_1016_j_engappai_2025_111986 crossref_primary_10_1109_ACCESS_2020_3014879 crossref_primary_10_1109_TII_2018_2822670 crossref_primary_10_1109_TRO_2019_2914772 crossref_primary_10_3389_fnins_2022_1070645 crossref_primary_10_1007_s12559_018_9571_z crossref_primary_10_1016_j_cviu_2022_103498 crossref_primary_10_1109_TASE_2017_2783342 crossref_primary_10_1016_j_robot_2019_01_009 crossref_primary_10_1007_s12559_017_9536_7 crossref_primary_10_1177_1729881417710793 crossref_primary_10_1177_1729881417710794 crossref_primary_10_1109_TASE_2019_2897791 crossref_primary_10_1108_IR_07_2018_0154 crossref_primary_10_1038_s41598_024_75607_7 crossref_primary_10_1109_TIE_2021_3068678 crossref_primary_10_1109_TII_2018_2879426 crossref_primary_10_3390_info12020092 crossref_primary_10_1038_s41598_025_16456_w crossref_primary_10_1109_JSEN_2023_3319114 crossref_primary_10_1049_iet_its_2018_5351 crossref_primary_10_1109_TCDS_2023_3311871 crossref_primary_10_1016_j_neucom_2018_06_028 crossref_primary_10_1108_AA_11_2018_0228 crossref_primary_10_1007_s00170_022_09161_9 crossref_primary_10_1007_s10514_023_10091_y crossref_primary_10_1016_j_ins_2017_08_004 crossref_primary_10_1109_TASE_2019_2910508 crossref_primary_10_1109_TCYB_2016_2609999 crossref_primary_10_1177_1729881417717059 crossref_primary_10_1007_s13042_018_0825_6 crossref_primary_10_3390_s20041050 crossref_primary_10_1017_S0263574721000023 crossref_primary_10_1177_1729881417717057 crossref_primary_10_3390_s17112653 crossref_primary_10_1007_s13042_017_0736_y crossref_primary_10_1109_TIM_2021_3096858 crossref_primary_10_1109_TSP_2020_3003453 crossref_primary_10_1109_TASE_2018_2877499 crossref_primary_10_1038_s41467_024_51261_5 crossref_primary_10_1109_TII_2019_2898264 crossref_primary_10_1109_TMECH_2017_2775208 crossref_primary_10_1007_s13042_019_00962_1 crossref_primary_10_1016_j_ifacol_2020_12_2430 crossref_primary_10_1016_j_imavis_2018_09_016 crossref_primary_10_1109_ACCESS_2022_3174874 crossref_primary_10_1109_TASE_2019_2941230 crossref_primary_10_1007_s12559_017_9505_1 crossref_primary_10_1177_1729881417704544 crossref_primary_10_1109_TSMC_2018_2818184 crossref_primary_10_3390_e20070522 crossref_primary_10_1177_00368504211037771 crossref_primary_10_1109_TMECH_2020_3048441 crossref_primary_10_1007_s13042_017_0666_8 crossref_primary_10_1109_TCYB_2021_3080321 crossref_primary_10_3390_biomimetics8080591 crossref_primary_10_1109_TIM_2023_3326241 crossref_primary_10_1007_s00500_018_3108_y crossref_primary_10_1007_s12559_018_9558_9 crossref_primary_10_1177_0278364920907688 crossref_primary_10_1109_TITS_2017_2754099 crossref_primary_10_1016_j_measurement_2025_118174 crossref_primary_10_1007_s11432_021_3512_6 crossref_primary_10_1109_TCSVT_2022_3206865 crossref_primary_10_1002_adem_202300469 crossref_primary_10_1186_s40638_017_0058_2 crossref_primary_10_1109_TASE_2017_2692271 crossref_primary_10_1016_j_ins_2017_11_038 crossref_primary_10_1016_j_rcim_2024_102792 crossref_primary_10_1007_s10846_022_01595_3 crossref_primary_10_1109_TIM_2017_2764298 crossref_primary_10_1109_TASE_2020_2969485 crossref_primary_10_1177_1729881420948727 crossref_primary_10_12688_cobot_17497_1 crossref_primary_10_1002_admt_202300322 crossref_primary_10_1007_s00521_021_06089_1 crossref_primary_10_1109_JSYST_2020_3039785 crossref_primary_10_1007_s12555_019_0186_2 crossref_primary_10_1016_j_robot_2017_11_015 crossref_primary_10_1109_TASE_2018_2883587 crossref_primary_10_1016_j_bspc_2022_104259 crossref_primary_10_1007_s12555_017_0060_z crossref_primary_10_1109_ACCESS_2017_2698419 crossref_primary_10_3389_fnins_2022_1100812 crossref_primary_10_1109_TII_2019_2895602
Cites_doi	10.1109/TASE.2012.2206075 10.1109/ICHR.2008.4756005 10.1109/TIM.2014.2315734 10.1109/MFI.2012.6343036 10.1109/ROMAN.2010.5598659 10.1109/TASE.2013.2252617 10.1109/TASE.2012.2194143 10.1109/TRO.2011.2132870 10.1109/TII.2016.2542208 10.1016/j.robot.2012.07.021 10.1109/ROBOT.2006.1641793 10.1007/978-3-642-25116-0_28 10.1109/TRO.2009.2033627 10.1109/TIM.2016.2514779 10.1109/IROS.2014.6942622 10.1109/JPROC.2010.2044470 10.1109/TSP.2011.2157912 10.1109/HUMANOIDS.2014.7041493 10.1109/TPAMI.2013.109 10.1109/TIP.2012.2215620 10.1109/ICRA.2015.7139443 10.1109/IROS.2009.5354648 10.1109/TASE.2012.2214772 10.1016/j.ins.2014.08.066 10.1016/j.sigpro.2012.09.011 10.1109/TASE.2014.2371852 10.1177/027836498800700603 10.1109/TIP.2014.2324290 10.1109/ROBOT.2009.5152515 10.1109/TCYB.2015.2481713 10.1109/ROBOT.2009.5152709 10.1109/IROS.2012.6385992 10.1109/TNNLS.2015.2424995 10.1109/ICRA.2013.6631074 10.1109/TASE.2009.2020994 10.1007/s11432-015-5404-9 10.1068/p5850 10.1109/TRO.2011.2121130 10.1016/j.jfranklin.2015.07.002 10.1109/ROBOT.1993.291939 10.1109/TST.2013.6449409 10.1109/TIP.2012.2205006 10.1109/ICRA.2014.6907172 10.1109/IROS.2011.6094878 10.1109/ROBOT.2010.5509553 10.1109/TIP.2013.2282078 10.1109/TASE.2014.2320157 10.1109/ICCV.2013.202 10.1109/TRO.2011.2134130 10.1109/TIM.2012.2210460
ContentType	Journal Article
DBID	97E RIA RIE AAYXX CITATION
DOI	10.1109/TASE.2016.2549552
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE/IET Electronic Library (IEL) (UW System Shared) CrossRef
DatabaseTitle	CrossRef
DatabaseTitleList
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1558-3783
EndPage	1008
ExternalDocumentID	10_1109_TASE_2016_2549552 7462208
Genre	orig-research
GrantInformation_xml	– fundername: National Key Project for Basic Research of China grantid: 2013CB329403 – fundername: National Natural Science Foundation of China grantid: 61327809 funderid: 10.13039/501100001809 – fundername: National High-Tech Research and Development Plan grantid: 2015AA042306
GroupedDBID	-~X 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AIBXA AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 IFIPE IPLJI JAVBF LAI M43 O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION
ID	FETCH-LOGICAL-c265t-24e6a135c3f011e3707e08d68595f09aa743f67d9e35cadeab58121f25565c353
IEDL.DBID	RIE
ISICitedReferencesCount	216
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000399347500052&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1545-5955
IngestDate	Sat Nov 29 04:12:45 EST 2025 Tue Nov 18 22:16:38 EST 2025 Tue Aug 26 17:03:04 EDT 2025
IsPeerReviewed	false
IsScholarly	true
Issue	2
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c265t-24e6a135c3f011e3707e08d68595f09aa743f67d9e35cadeab58121f25565c353
ORCID	0000-0002-4042-6044
PageCount	13
ParticipantIDs	crossref_primary_10_1109_TASE_2016_2549552 crossref_citationtrail_10_1109_TASE_2016_2549552 ieee_primary_7462208
PublicationCentury	2000
PublicationDate	2017-April 2017-4-00
PublicationDateYYYYMMDD	2017-04-01
PublicationDate_xml	– month: 04 year: 2017 text: 2017-April
PublicationDecade	2010
PublicationTitle	IEEE transactions on automation science and engineering
PublicationTitleAbbrev	TASE
PublicationYear	2017
Publisher	IEEE
Publisher_xml	– name: IEEE
References	ref13 ref56 ref12 ref15 ref14 ref53 ref52 ref55 ref11 ref54 ref10 ref17 ref16 ref19 ref18 ref51 lederman (ref25) 2004 ref46 ref45 ref48 zhou (ref57) 2009 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 nene (ref32) 1996 ref6 tuzel (ref47) 2006 ref5 ref40 jia (ref22) 2010; 7 ref35 ref34 ref37 ref36 ref30 ref33 ref2 ref1 ref39 ref38 xiao (ref50) 2013; 215 ref24 ref23 ref26 ref20 ref21 ref27 ref29 morales (ref31) 2007 liu (ref28) 2015
References_xml	– start-page: 1 year: 2009 ident: ref57 article-title: Canonical time warping for alignment of human behavior publication-title: Proc Neural Inf Process Syst (NIPS) – ident: ref21 doi: 10.1109/TASE.2012.2206075 – ident: ref8 doi: 10.1109/ICHR.2008.4756005 – ident: ref2 doi: 10.1109/TIM.2014.2315734 – ident: ref27 doi: 10.1109/MFI.2012.6343036 – ident: ref4 doi: 10.1109/ROMAN.2010.5598659 – ident: ref3 doi: 10.1109/TASE.2013.2252617 – ident: ref19 doi: 10.1109/TASE.2012.2194143 – ident: ref5 doi: 10.1109/TRO.2011.2132870 – ident: ref55 doi: 10.1109/TII.2016.2542208 – ident: ref15 doi: 10.1016/j.robot.2012.07.021 – ident: ref34 doi: 10.1109/ROBOT.2006.1641793 – ident: ref38 doi: 10.1007/978-3-642-25116-0_28 – ident: ref14 doi: 10.1109/TRO.2009.2033627 – ident: ref29 doi: 10.1109/TIM.2016.2514779 – ident: ref44 doi: 10.1109/IROS.2014.6942622 – start-page: 107 year: 2004 ident: ref25 publication-title: Multisensory texture perception – start-page: 3678 year: 2015 ident: ref28 article-title: Robust kernel dictionary learning using a whole sequence convergent algorithm publication-title: Proc Int Conf Artif Intell (IJCAI) – ident: ref49 doi: 10.1109/JPROC.2010.2044470 – ident: ref42 doi: 10.1109/TSP.2011.2157912 – ident: ref36 doi: 10.1109/HUMANOIDS.2014.7041493 – ident: ref40 doi: 10.1109/TPAMI.2013.109 – ident: ref16 doi: 10.1109/TIP.2012.2215620 – ident: ref7 doi: 10.1109/ICRA.2015.7139443 – ident: ref37 doi: 10.1109/IROS.2009.5354648 – ident: ref53 doi: 10.1109/TASE.2012.2214772 – ident: ref11 doi: 10.1016/j.ins.2014.08.066 – ident: ref12 doi: 10.1016/j.sigpro.2012.09.011 – ident: ref43 doi: 10.1109/TASE.2014.2371852 – volume: 215 start-page: 845 year: 2013 ident: ref50 article-title: Dexterous robotic-hand grasp learning using piecewise linear dynamic systems model publication-title: Found Pract Appl Congnitive Syst Inf Process – ident: ref1 doi: 10.1177/027836498800700603 – ident: ref39 doi: 10.1109/TIP.2014.2324290 – ident: ref35 doi: 10.1109/ROBOT.2009.5152515 – ident: ref52 doi: 10.1109/TCYB.2015.2481713 – ident: ref17 doi: 10.1109/ROBOT.2009.5152709 – ident: ref41 doi: 10.1109/IROS.2012.6385992 – ident: ref45 doi: 10.1109/TNNLS.2015.2424995 – ident: ref20 doi: 10.1109/ICRA.2013.6631074 – volume: 7 start-page: 400 year: 2010 ident: ref22 article-title: Surface patch reconstruction from 'one-dimensional' tactile data publication-title: IEEE Trans Autom Sci Eng doi: 10.1109/TASE.2009.2020994 – ident: ref56 doi: 10.1007/s11432-015-5404-9 – start-page: 589 year: 2006 ident: ref47 article-title: Region covariance: A fast descriptor for detection and classification publication-title: Proc Eur Conf Comput Vis (ECCV) – ident: ref24 doi: 10.1068/p5850 – ident: ref23 doi: 10.1109/TRO.2011.2121130 – ident: ref10 doi: 10.1016/j.jfranklin.2015.07.002 – ident: ref51 doi: 10.1109/ROBOT.1993.291939 – ident: ref48 doi: 10.1109/TST.2013.6449409 – ident: ref54 doi: 10.1109/TIP.2012.2205006 – ident: ref30 doi: 10.1109/ICRA.2014.6907172 – ident: ref6 doi: 10.1109/IROS.2011.6094878 – ident: ref18 doi: 10.1109/ROBOT.2010.5509553 – ident: ref33 doi: 10.1109/TIP.2013.2282078 – ident: ref9 doi: 10.1109/TASE.2014.2320157 – start-page: 1 year: 2007 ident: ref31 article-title: An experiment in the use of manipulation primitives and tactile perception for reactive grasping publication-title: Proc Robot Sci Syst Workshop Robot Manipulation Sens Adapt Real World – ident: ref26 doi: 10.1109/ICCV.2013.202 – ident: ref13 doi: 10.1109/TRO.2011.2134130 – ident: ref46 doi: 10.1109/TIM.2012.2210460 – year: 1996 ident: ref32 article-title: Columbia object image library
SSID	ssj0024890
Score	2.554239
Snippet	The camera provides rich visual information regarding objects and becomes one of the most mainstream sensors in the automation community. However, it is often...
SourceID	crossref ieee
SourceType	Enrichment Source Index Database Publisher
StartPage	996
SubjectTerms	Automation Joint sparse coding Manipulators Object recognition tactile perception Tactile sensors Training visual perception Visualization
Title	Visual-Tactile Fusion for Object Recognition
URI	https://ieeexplore.ieee.org/document/7462208
Volume	14
WOSCitedRecordID	wos000399347500052&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-3783 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0024890 issn: 1545-5955 databaseCode: RIE dateStart: 20040101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEA61eNCDryrWF3vwJE27m2yS3WORFg9SRav0tmSTWSiUrfTh7zeTbmsFEbwtywSWL1nmm8nMN4TcgmIy5VFOC5UqGvM8oTpPsHCNWW2F4tb4nX5Ug0EyGqXPNdLa9MIAgC8-gzY--rt8OzVLTJV1VCwZw87eHaXkqlfrW1cv8fkUZARUpEJUN5hRmHaG3dceFnHJNkZDQrAfPmhrqIr3Kf3D_33NETmouGPQXW32MalBeUL2txQFG6T1Pp4v9YQOsV9hAkF_idmwwDHT4CnHlEvwsq4Ympan5K3fG94_0GogAjVMigVlMUgdcWF44X5L4CpUECZWokZZEaZaOzpQSGVTcDbags6F899RgTJjbpHgZ6ReTks4J0GMcZlkUFhjY8dSEssFRMKqwjE0Y_MmCdcQZaZSC8ehFZPMRw1hmiGqGaKaVag2yd1mycdKKuMv4wYiujGswLz4_fUl2WPoTn3FzBWpL2ZLuCa75nMxns9u_EH4Ap9Grgk
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fS8MwEA5DBfXBX1OcP_vgkyxbmzRJ8zhkY-KcolX2VtokhcHYZFv9-81l3Zwggm-lXKB8SbnvLnffIXRjBOGSBhnOhRQ4pFmE0yyCwjWiU80E1crtdE_0-9FgIJ8rqL7qhTHGuOIz04BHd5evJ6qAVFlThJwQ6OzdZGFI_EW31reyXuQyKsAJMJOMlXeYgS-bceu1DWVcvAHxEGPkhxdaG6vivEpn_3_fc4D2SvbotRbbfYgqZnyEdtc0Bauo_j6cFekIx9CxMDJep4B8mGe5qfeUQdLFe1nWDE3Gx-it047vurgciYAV4WyOSWh4GlCmaG5_TEOFL4wfaQ4qZbkv09QSgpwLLY21SbVJM2Y9eJCD0JhdxOgJ2hhPxuYUeSFEZpyYXCsdWp4SacpMwLTILUdTOqshfwlRokq9cBhbMUpc3ODLBFBNANWkRLWGbldLPhZiGX8ZVwHRlWEJ5tnvr6_Rdjd-7CW9-_7DOdoh4Fxd_cwF2phPC3OJttTnfDibXrlD8QXfYrFQ
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Visual%E2%80%93Tactile+Fusion+for+Object+Recognition&rft.jtitle=IEEE+transactions+on+automation+science+and+engineering&rft.au=Liu%2C+Huaping&rft.au=Yu%2C+Yuanlong&rft.au=Sun%2C+Fuchun&rft.au=Gu%2C+Jason&rft.date=2017-04-01&rft.issn=1545-5955&rft.eissn=1558-3783&rft.volume=14&rft.issue=2&rft.spage=996&rft.epage=1008&rft_id=info:doi/10.1109%2FTASE.2016.2549552&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TASE_2016_2549552
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1545-5955&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1545-5955&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1545-5955&client=summon