Embedded real-time speed limit sign recognition using image processing and machine learning techniques

The number of traffic accidents in Brazil has reached alarming levels and is currently one of the leading causes of death in the country. With the number of vehicles on the roads increasing rapidly, these problems will tend to worsen. Consequently, huge investments in resources to increase road safe...

Full description

Saved in:

Bibliographic Details
Published in:	Neural computing & applications Vol. 28; no. Suppl 1; pp. 573 - 584
Main Authors:	Gomes, Samuel L., Rebouças, Elizângela de S., Neto, Edson Cavalcanti, Papa, João P., Albuquerque, Victor H. C. de, Rebouças Filho, Pedro P., Tavares, João Manuel R. S.
Format:	Journal Article
Language:	English
Published:	London Springer London 01.12.2017 Springer Nature B.V
Subjects:	Artificial Intelligence Classifiers Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Embedded systems Feature recognition Frames per second Image detection Image processing Image Processing and Computer Vision Kernel functions Least mean squares Least mean squares algorithm Machine learning Multilayer perceptrons Object recognition Optical character recognition Original Article Probability and Statistics in Computer Science Real time Roads & highways Signs Speed limits Street signs Support vector machines Traffic accidents Traffic accidents & safety Traffic safety Traffic signs Traffic speed Cascade haar-like features Computer vision Pattern recognition Automotive applications
ISSN:	0941-0643, 1433-3058
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	The number of traffic accidents in Brazil has reached alarming levels and is currently one of the leading causes of death in the country. With the number of vehicles on the roads increasing rapidly, these problems will tend to worsen. Consequently, huge investments in resources to increase road safety will be required. The vertical R-19 system for optical character recognition of regulatory traffic signs (maximum speed limits) according to Brazilian Standards developed in this work uses a camera positioned at the front of the vehicle, facing forward. This is so that images of traffic signs can be captured, enabling the use of image processing and analysis techniques for sign detection. This paper proposes the detection and recognition of speed limit signs based on a cascade of boosted classifiers working with haar-like features. The recognition of the sign detected is achieved based on the optimum-path forest classifier (OPF), support vector machines (SVM), multilayer perceptron, k-nearest neighbor (kNN), extreme learning machine, least mean squares, and least squares machine learning techniques. The SVM, OPF and kNN classifiers had average accuracies higher than 99.5 %; the OPF classifier with a linear kernel took an average time of 87 μ s to recognize a sign, while kNN took 11,721 μ s and SVM 12,595 μ s. This sign detection approach found and recognized successfully 11,320 road signs from a set of 12,520 images, leading to an overall accuracy of 90.41 %. Analyzing the system globally recognition accuracy was 89.19 %, as 11,167 road signs from a database with 12,520 signs were correctly recognized. The processing speed of the embedded system varied between 20 and 30 frames per second. Therefore, based on these results, the proposed system can be considered a promising tool with high commercial potential.
AbstractList	The number of traffic accidents in Brazil has reached alarming levels and is currently one of the leading causes of death in the country. With the number of vehicles on the roads increasing rapidly, these problems will tend to worsen. Consequently, huge investments in resources to increase road safety will be required. The vertical R-19 system for optical character recognition of regulatory traffic signs (maximum speed limits) according to Brazilian Standards developed in this work uses a camera positioned at the front of the vehicle, facing forward. This is so that images of traffic signs can be captured, enabling the use of image processing and analysis techniques for sign detection. This paper proposes the detection and recognition of speed limit signs based on a cascade of boosted classifiers working with haar-like features. The recognition of the sign detected is achieved based on the optimum-path forest classifier (OPF), support vector machines (SVM), multilayer perceptron, k-nearest neighbor (kNN), extreme learning machine, least mean squares, and least squares machine learning techniques. The SVM, OPF and kNN classifiers had average accuracies higher than 99.5 %; the OPF classifier with a linear kernel took an average time of 87 μ s to recognize a sign, while kNN took 11,721 μ s and SVM 12,595 μ s. This sign detection approach found and recognized successfully 11,320 road signs from a set of 12,520 images, leading to an overall accuracy of 90.41 %. Analyzing the system globally recognition accuracy was 89.19 %, as 11,167 road signs from a database with 12,520 signs were correctly recognized. The processing speed of the embedded system varied between 20 and 30 frames per second. Therefore, based on these results, the proposed system can be considered a promising tool with high commercial potential. The number of traffic accidents in Brazil has reached alarming levels and is currently one of the leading causes of death in the country. With the number of vehicles on the roads increasing rapidly, these problems will tend to worsen. Consequently, huge investments in resources to increase road safety will be required. The vertical R-19 system for optical character recognition of regulatory traffic signs (maximum speed limits) according to Brazilian Standards developed in this work uses a camera positioned at the front of the vehicle, facing forward. This is so that images of traffic signs can be captured, enabling the use of image processing and analysis techniques for sign detection. This paper proposes the detection and recognition of speed limit signs based on a cascade of boosted classifiers working with haar-like features. The recognition of the sign detected is achieved based on the optimum-path forest classifier (OPF), support vector machines (SVM), multilayer perceptron, k-nearest neighbor (kNN), extreme learning machine, least mean squares, and least squares machine learning techniques. The SVM, OPF and kNN classifiers had average accuracies higher than 99.5 %; the OPF classifier with a linear kernel took an average time of 87 μs to recognize a sign, while kNN took 11,721 μs and SVM 12,595 μs. This sign detection approach found and recognized successfully 11,320 road signs from a set of 12,520 images, leading to an overall accuracy of 90.41 %. Analyzing the system globally recognition accuracy was 89.19 %, as 11,167 road signs from a database with 12,520 signs were correctly recognized. The processing speed of the embedded system varied between 20 and 30 frames per second. Therefore, based on these results, the proposed system can be considered a promising tool with high commercial potential.
Author	Rebouças, Elizângela de S. Papa, João P. Rebouças Filho, Pedro P. Albuquerque, Victor H. C. de Gomes, Samuel L. Neto, Edson Cavalcanti Tavares, João Manuel R. S.
Author_xml	– sequence: 1 givenname: Samuel L. surname: Gomes fullname: Gomes, Samuel L. organization: Laboratório de Processamento Digital de Imagens e Simulação Computacional, Instituto Federal de Federal de Educação, Ciência e Tecnologia do Ceará (IFCE) – sequence: 2 givenname: Elizângela de S. surname: Rebouças fullname: Rebouças, Elizângela de S. organization: Laboratório de Processamento Digital de Imagens e Simulação Computacional, Instituto Federal de Federal de Educação, Ciência e Tecnologia do Ceará (IFCE) – sequence: 3 givenname: Edson Cavalcanti surname: Neto fullname: Neto, Edson Cavalcanti organization: Laboratório de Processamento Digital de Imagens e Simulação Computacional, Instituto Federal de Federal de Educação, Ciência e Tecnologia do Ceará (IFCE) – sequence: 4 givenname: João P. surname: Papa fullname: Papa, João P. organization: Departamento de Ciência da Computação, Universidade Estadual Paulista – sequence: 5 givenname: Victor H. C. de surname: Albuquerque fullname: Albuquerque, Victor H. C. de organization: Programa de Pós-Graduação em Informática Aplicada, Laboratório de Bioinformática, Universidade de Fortaleza – sequence: 6 givenname: Pedro P. orcidid: 0000-0002-1878-5489 surname: Rebouças Filho fullname: Rebouças Filho, Pedro P. email: pedrosarf@ifce.edu.br organization: Laboratório de Processamento Digital de Imagens e Simulação Computacional, Instituto Federal de Federal de Educação, Ciência e Tecnologia do Ceará (IFCE) – sequence: 7 givenname: João Manuel R. S. surname: Tavares fullname: Tavares, João Manuel R. S. organization: Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto
BookMark	eNp9kD1PwzAQhi0EEm3hB7BFYg6c7Xw4I6rKh1SJBWbLsS-pq8Qpdjrw73EIA0Kik3V37-M7PUty7gaHhNxQuKMA5X0AyBlNgRYp40Kk_IwsaMZ5yiEX52QBVRanRcYvyTKEPQBkhcgXpNn0NRqDJvGounS0PSbhgLHubG_HJNjWxZEeWmdHO7jkGKxrE9urFpODHzSG74ZyJumV3lmHSYfKu6k5ot45-3HEcEUuGtUFvP55V-T9cfO2fk63r08v64dtqjOajWluOFKhQIEuC4EINWClcsFrSqFuDFYVZQaY0UwoZhqmRV42NYhC1bVSgq_I7fxvPG3aO8r9cPQurpScQcFYXrLiVIpWRZlllJY8pso5pf0QgsdGajuqScLole0kBTmpl7N6GdXLSb2cSPqHPPhozH-eZNjMhJh1LfpfN_0LfQHvaJjP
CitedBy_id	crossref_primary_10_1002_tee_23215 crossref_primary_10_1007_s00521_020_04956_x crossref_primary_10_3390_su132212722 crossref_primary_10_1007_s11554_016_0662_3 crossref_primary_10_1007_s00521_017_3048_y crossref_primary_10_1109_ACCESS_2020_2976513 crossref_primary_10_1007_s10489_018_1199_x crossref_primary_10_1016_j_compeleceng_2019_04_001 crossref_primary_10_1155_2018_7613282 crossref_primary_10_1007_s00521_018_3931_1 crossref_primary_10_1109_TITS_2019_2897990 crossref_primary_10_1016_j_micpro_2024_105101 crossref_primary_10_3390_s19092093 crossref_primary_10_1088_1757_899X_850_1_012027 crossref_primary_10_1007_s00500_018_3491_4 crossref_primary_10_1016_j_future_2019_05_074 crossref_primary_10_1016_j_trpro_2021_01_070 crossref_primary_10_1007_s11042_021_11369_y crossref_primary_10_1007_s00521_018_3736_2 crossref_primary_10_1111_exsy_12550 crossref_primary_10_1007_s00521_021_06081_9 crossref_primary_10_7717_peerj_cs_883 crossref_primary_10_1186_s40537_020_00324_7 crossref_primary_10_59176_kjcs_v1i1_2180 crossref_primary_10_1007_s00521_018_03994_w crossref_primary_10_3390_bdcc7020076 crossref_primary_10_3390_jimaging9100207 crossref_primary_10_1088_1742_6596_1577_1_012009
Cites_doi	10.1111/j.1365-2818.2010.03384.x 10.1016/j.physa.2015.01.064 10.3390/ma8073864 10.1002/ima.20188 10.1006/cgip.1993.1040 10.1109/72.80266 10.1145/361237.361242 10.1016/0262-8856(89)90017-6 10.1007/s13042-011-0019-y 10.1007/s00521-011-0748-6 10.1109/TPAMI.1986.4767851 10.4322/rbeb.2013.041 10.1016/j.eswa.2015.09.025 10.1016/j.patcog.2011.07.013 10.3390/s150612474 10.1109/72.788640 10.1016/j.eswa.2014.07.013 10.1109/ICNN.1993.298625 10.1016/j.procs.2010.12.169 10.1007/978-3-642-88163-3 10.1016/j.measurement.2015.03.039 10.1109/ICNN.1993.298623 10.1109/TLA.2015.7040658 10.1007/s10044-011-0265-3 10.1023/A:1009715923555 10.1016/j.tre.2013.12.011 10.1016/j.compeleceng.2015.01.002 10.1109/TNN.2006.875977 10.1016/j.neucom.2011.12.045 10.1109/2.29 10.1016/j.compeleceng.2013.03.020 10.1109/5.58323 10.1109/TPAMI.2004.1261076 10.1016/j.neucom.2005.12.126 10.1145/1961189.1961199 10.1007/978-3-319-19665-7_5 10.1007/978-3-540-88636-5_90 10.1109/ISBI.2013.6556694 10.1016/B978-0-12-397041-1.00009-1 10.1109/ICIP.2002.1038171 10.1007/978-3-642-53842-1_26 10.1109/CVPR.2014.126 10.1109/IVS.2012.6232222 10.1109/ICCIC.2012.6510256 10.1007/978-3-319-18833-1_19
ContentType	Journal Article
Copyright	The Natural Computing Applications Forum 2016 Copyright Springer Science & Business Media 2017 Copyright Springer Nature B.V. Dec 2017
Copyright_xml	– notice: The Natural Computing Applications Forum 2016 – notice: Copyright Springer Science & Business Media 2017 – notice: Copyright Springer Nature B.V. Dec 2017
DBID	AAYXX CITATION 8FE 8FG AFKRA ARAPS BENPR BGLVJ CCPQU DWQXO HCIFZ P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS
DOI	10.1007/s00521-016-2388-3
DatabaseName	CrossRef ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central - New (Subscription) ProQuest Technology Collection ProQuest One Community College ProQuest Central SciTech Premium Collection Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China
DatabaseTitle	CrossRef Advanced Technologies & Aerospace Collection Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest One Academic Eastern Edition SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New)
DatabaseTitleList	Advanced Technologies & Aerospace Collection
Database_xml	– sequence: 1 dbid: P5Z name: ProQuest advanced technologies & aerospace journals url: https://search.proquest.com/hightechjournals sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	1433-3058
EndPage	584
ExternalDocumentID	10_1007_s00521_016_2388_3
GrantInformation_xml	– fundername: CNPq
GroupedDBID	-4Z -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29N 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 53G 5QI 5VS 67Z 6NX 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACUHS ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. B0M BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EAD EAP EBLON EBS ECS EDO EIOEI EJD EMI EMK EPL ESBYG EST ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW LAS LLZTM M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9O PF0 PT4 PT5 QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z5O Z7R Z7S Z7V Z7W Z7X Z7Y Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8P Z8Q Z8R Z8S Z8T Z8U Z8W Z92 ZMTXR ~8M ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB DWQXO PKEHL PQEST PQQKQ PQUKI PRINS
ID	FETCH-LOGICAL-c414t-5d3e18a0a0c768ee0b0e9a583b110bfde9912d02dc28a2df2c857fb086abbaa83
IEDL.DBID	RSV
ISICitedReferencesCount	28
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000417319700047&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0941-0643
IngestDate	Wed Nov 05 08:47:48 EST 2025 Tue Nov 04 16:34:44 EST 2025 Tue Nov 18 21:55:47 EST 2025 Sat Nov 29 02:59:02 EST 2025 Fri Feb 21 02:34:23 EST 2025
IsDoiOpenAccess	false
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	Suppl 1
Keywords	Cascade haar-like features Computer vision Pattern recognition Automotive applications
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c414t-5d3e18a0a0c768ee0b0e9a583b110bfde9912d02dc28a2df2c857fb086abbaa83
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-1878-5489
OpenAccessLink	http://hdl.handle.net/11449/165920
PQID	3206225726
PQPubID	2043988
PageCount	12
ParticipantIDs	proquest_journals_3206225726 proquest_journals_1967441173 crossref_citationtrail_10_1007_s00521_016_2388_3 crossref_primary_10_1007_s00521_016_2388_3 springer_journals_10_1007_s00521_016_2388_3
PublicationCentury	2000
PublicationDate	2017-12-01
PublicationDateYYYYMMDD	2017-12-01
PublicationDate_xml	– month: 12 year: 2017 text: 2017-12-01 day: 01
PublicationDecade	2010
PublicationPlace	London
PublicationPlace_xml	– name: London – name: Heidelberg
PublicationTitle	Neural computing & applications
PublicationTitleAbbrev	Neural Comput & Applic
PublicationYear	2017
Publisher	Springer London Springer Nature B.V
Publisher_xml	– name: Springer London – name: Springer Nature B.V
References	Burges (CR10) 1998; 2 Yu, Shi (CR61) 2015; 428 CR38 Wu, Huang, Chen, Chen, Chang, Chen (CR59) 2013; 39 CR33 Vapnik (CR55) 1999; 10 CR31 CR30 Rebouças Filho, Moreira, de Lima Xavier, Gomes, Santos, Freitas, Freitas (CR45) 2015; 8 Chang, Lin (CR13) 2011; 2 Neto, Rebouças, Moraes, Gomes, Rebouças Filho (CR37) 2015; 13 Schimidt (CR51) 1993; 1 Widrow (CR57) 1990; 78 Albuquerque, Rebouças Filho, da Silveira Cavalcanti, Tavares (CR2) 2010; 240 Widrow, Winter (CR58) 1988; 21 Canny (CR11) 1986; 6 Papa, Falcão, de Albuquerque, Tavares (CR39) 2012; 45 Rebouças Filho, Cortez, Félix, Cavalcante, Holanda (CR46) 2013; 29 CR3 CR5 CR8 CR7 Minsky, Papert (CR34) 1969 CR9 Huang, Zhu, Siew (CR27) 2006; 70 Glasbey (CR20) 1993; 55 CR47 CR43 CR42 Papa, Falcão, Suzuki (CR41) 2009; 19 Moreira, Kleinberg, Arruda, Freitas, Parente, de Albuquerque, Rebouças Filho (CR35) 2016; 45 Medeiros, Barreto (CR32) 2013; 22 Schölkopf, Smola (CR52) 2002 Carrese, Mantovani, Nigro (CR12) 2014; 65 Barreto, Frota (CR6) 2013; 16 CR19 Russell, Norvig (CR50) 2009 Huang, Chen, Siew (CR25) 2006; 17 CR17 CR16 Duda, Hart (CR15) 1972; 15 Haykin (CR22) 2008 Kohonen (CR29) 1989 Yuen, Illingworth, Kittler (CR62) 1989; 7 Neto, Gomes, Filho, de Albuquerque (CR36) 2015; 70 Albuquerque, Barbosa, Silva, Moura, Rebouças Filho, Papa, Tavares (CR1) 2015; 15 Viola, Jones (CR56) 2001; 1 Falcão, Stolfi, Lotufo (CR18) 2004; 26 Huang, Wang, Lan (CR26) 2011; 2 Rebouças Filho, Cortez, da Silva Barros, Albuquerque (CR44) 2014; 41 Riedmiller, Braun (CR48) 1993; 1 Cortes, Vapnik (CR14) 1995; 20 Gomes, Rebouças, Rebouças Filho (CR21) 2014; 9 CR23 Kocer, Cevik (CR28) 2011; 3 CR64 Yi, Chen, Chang (CR60) 2015; 42 CR63 Papa, Falcao, Suzuki (CR40) 2009; 19 Arbib (CR4) 2003 Tu, van Wyk, Hamam, Djouani, Du (CR54) 2013; 4 Horata, Chiewchanwattana, Sunat (CR24) 2013; 102 Ruck, Rogers, Kabrisky, Oxley, Suter (CR49) 1990; 1 Tavares, Rebouças Filho, Cavalcante, de Albuquerque (CR53) 2009; 38 SC Yi (2388_CR60) 2015; 42 GB Huang (2388_CR27) 2006; 70 P Horata (2388_CR24) 2013; 102 C Cortes (2388_CR14) 1995; 20 BF Wu (2388_CR59) 2013; 39 2388_CR30 2388_CR31 B Schölkopf (2388_CR52) 2002 VHC Albuquerque (2388_CR1) 2015; 15 2388_CR33 HE Kocer (2388_CR28) 2011; 3 C Medeiros (2388_CR32) 2013; 22 GB Huang (2388_CR26) 2011; 2 JP Papa (2388_CR39) 2012; 45 2388_CR38 C Tu (2388_CR54) 2013; 4 S Carrese (2388_CR12) 2014; 65 VHC Albuquerque (2388_CR2) 2010; 240 JMR Tavares (2388_CR53) 2009; 38 S Yu (2388_CR61) 2015; 428 W Schimidt (2388_CR51) 1993; 1 B Widrow (2388_CR58) 1988; 21 2388_CR42 2388_CR43 2388_CR47 GB Huang (2388_CR25) 2006; 17 VN Vapnik (2388_CR55) 1999; 10 2388_CR5 2388_CR8 T Kohonen (2388_CR29) 1989 2388_CR7 CA Glasbey (2388_CR20) 1993; 55 AX Falcão (2388_CR18) 2004; 26 2388_CR9 J Canny (2388_CR11) 1986; 6 SL Gomes (2388_CR21) 2014; 9 DW Ruck (2388_CR49) 1990; 1 RO Duda (2388_CR15) 1972; 15 2388_CR3 G Barreto (2388_CR6) 2013; 16 PP Rebouças Filho (2388_CR45) 2015; 8 2388_CR16 2388_CR17 SJ Russell (2388_CR50) 2009 2388_CR19 FDL Moreira (2388_CR35) 2016; 45 M Riedmiller (2388_CR48) 1993; 1 C Burges (2388_CR10) 1998; 2 JP Papa (2388_CR40) 2009; 19 SO Haykin (2388_CR22) 2008 JP Papa (2388_CR41) 2009; 19 CC Chang (2388_CR13) 2011; 2 2388_CR63 2388_CR64 PP Rebouças Filho (2388_CR46) 2013; 29 2388_CR23 EC Neto (2388_CR37) 2015; 13 MA Arbib (2388_CR4) 2003 P Viola (2388_CR56) 2001; 1 HK Yuen (2388_CR62) 1989; 7 EC Neto (2388_CR36) 2015; 70 PP Rebouças Filho (2388_CR44) 2014; 41 M Minsky (2388_CR34) 1969 B Widrow (2388_CR57) 1990; 78
References_xml	– volume: 240 start-page: 50 issue: 1 year: 2010 end-page: 59 ident: CR2 article-title: New computational solution to quantify synthetic material porosity from optical microscopic images publication-title: J Microsc doi: 10.1111/j.1365-2818.2010.03384.x – volume: 428 start-page: 206 year: 2015 end-page: 223 ident: CR61 article-title: The effects of vehicular gap changes with memory on traffic flow in cooperative adaptive cruise control strategy publication-title: Phys A Stat Mech Appl doi: 10.1016/j.physa.2015.01.064 – volume: 8 start-page: 3864 issue: 7 year: 2015 ident: CR45 article-title: New analysis method application in metallographic images through the construction of mosaics via speeded up robust features and scale invariant feature transform publication-title: Materials doi: 10.3390/ma8073864 – ident: CR16 – volume: 19 start-page: 120 issue: 2 year: 2009 end-page: 131 ident: CR40 article-title: Supervised pattern classification based on optimum-path forest publication-title: Int J Imaging Syst Technol doi: 10.1002/ima.20188 – volume: 55 start-page: 532 year: 1993 end-page: 537 ident: CR20 article-title: Analysis of histogram-based thresholding algorithms publication-title: CVGIP Graph Models Image Process doi: 10.1006/cgip.1993.1040 – volume: 1 start-page: 296 issue: 4 year: 1990 end-page: 298 ident: CR49 article-title: The multilayer perceptron as an approximation to a bayes optimal discriminant function publication-title: IEEE Trans Neural Netw doi: 10.1109/72.80266 – volume: 15 start-page: 11 issue: 1 year: 1972 end-page: 15 ident: CR15 article-title: Use of the hough transformation to detect lines and curves in pictures publication-title: Commun ACM doi: 10.1145/361237.361242 – ident: CR8 – year: 2003 ident: CR4 publication-title: The handbook of brain theory and neural networks – volume: 7 start-page: 31 issue: 1 year: 1989 end-page: 37 ident: CR62 article-title: Detecting partially occluded ellipses using the hough transform publication-title: Image Vis Comput doi: 10.1016/0262-8856(89)90017-6 – volume: 2 start-page: 107 year: 2011 end-page: 122 ident: CR26 article-title: Extreme learning machines: a survey publication-title: Int J Mach Learn Cybern doi: 10.1007/s13042-011-0019-y – ident: CR42 – ident: CR19 – year: 2008 ident: CR22 publication-title: Neural networks and learning machines – year: 2009 ident: CR50 publication-title: Artificial intelligence: a modern approach – volume: 22 start-page: 71 issue: 1 year: 2013 end-page: 84 ident: CR32 article-title: A novel weight pruning method for mlp classifiers based on the maxcore principle publication-title: Neural Comput Appl doi: 10.1007/s00521-011-0748-6 – volume: 6 start-page: 679 year: 1986 end-page: 698 ident: CR11 article-title: A computational approach to edge detection publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.1986.4767851 – volume: 29 start-page: 363 issue: 4 year: 2013 end-page: 376 ident: CR46 article-title: Adaptive 2d crisp active contour model applied to lung segmentation in ct images of the thorax of healthy volunteers and patients with pulmonary emphysema publication-title: Revista Brasileira de Engenharia Biomédica doi: 10.4322/rbeb.2013.041 – volume: 45 start-page: 294 year: 2016 end-page: 306 ident: CR35 article-title: A novel vickers hardness measurement technique based on adaptive balloon active contour method publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2015.09.025 – volume: 45 start-page: 512 issue: 1 year: 2012 end-page: 520 ident: CR39 article-title: Efficient supervised optimum-path forest classification for large datasets publication-title: Pattern Recognit doi: 10.1016/j.patcog.2011.07.013 – volume: 19 start-page: 120 issue: 2 year: 2009 end-page: 131 ident: CR41 article-title: Supervised pattern classification based on optimum-path forest publication-title: Int J Imaging Syst Technol doi: 10.1002/ima.20188 – volume: 15 start-page: 12,474 issue: 6 year: 2015 ident: CR1 article-title: Ultrasonic sensor signals and optimum-path forest classifier for the microstructural characterization of thermally-aged inconel 625 alloy publication-title: Sensors doi: 10.3390/s150612474 – ident: CR9 – volume: 10 start-page: 988 issue: 5 year: 1999 end-page: 999 ident: CR55 article-title: An overview of statistical learning theory publication-title: IEEE Trans Neural Netw doi: 10.1109/72.788640 – volume: 41 start-page: 7707 issue: 17 year: 2014 end-page: 7721 ident: CR44 article-title: Novel adaptive balloon active contour method based on internal force for image segmentation - a systematic evaluation on synthetic and real images publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2014.07.013 – volume: 1 start-page: 598 year: 1993 end-page: 604 ident: CR51 article-title: Initialization, backpropagation and generalization of feed-forward classifiers publication-title: IEEE Int Conf Neural Netw doi: 10.1109/ICNN.1993.298625 – ident: CR5 – volume: 3 start-page: 1033 year: 2011 end-page: 1037 ident: CR28 article-title: Artificial neural networks based vehicle license plate recognition publication-title: Proc Comput Sci doi: 10.1016/j.procs.2010.12.169 – year: 1989 ident: CR29 publication-title: Self-organization and associative memory doi: 10.1007/978-3-642-88163-3 – volume: 70 start-page: 36 year: 2015 end-page: 46 ident: CR36 article-title: Brazilian vehicle identification using a new embedded plate recognition system publication-title: Measurement doi: 10.1016/j.measurement.2015.03.039 – volume: 1 start-page: 586 year: 1993 end-page: 591 ident: CR48 article-title: A direct adaptive method for faster backpropagation learning: the RPROP algorithm publication-title: IEEE Int Conf Neural Netw doi: 10.1109/ICNN.1993.298623 – ident: CR64 – volume: 13 start-page: 272 year: 2015 end-page: 276 ident: CR37 article-title: Development control parking access using techniques digital image processing and applied computational intelligence. IEEE Transactions on Latin publication-title: IEEE Trans Latin America doi: 10.1109/TLA.2015.7040658 – ident: CR43 – ident: CR47 – volume: 16 start-page: 83 issue: 1 year: 2013 end-page: 97 ident: CR6 article-title: A unifying methodology for the evaluation of neural network models on novelty detection tasks publication-title: Pattern Anal Appl doi: 10.1007/s10044-011-0265-3 – volume: 2 start-page: 121 issue: 2 year: 1998 end-page: 167 ident: CR10 article-title: A tutorial on support vector machines for pattern recognition publication-title: Data Mining Knowl Discov doi: 10.1023/A:1009715923555 – volume: 65 start-page: 35 year: 2014 end-page: 49 ident: CR12 article-title: A security plan procedure for heavy goods vehicles parking areas: an application to the lazio region (Italy) publication-title: Transp Res E Logist Transp Rev doi: 10.1016/j.tre.2013.12.011 – ident: CR30 – volume: 42 start-page: 23 year: 2015 end-page: 29 ident: CR60 article-title: A lane detection approach based on intelligent vision publication-title: Comput Electr Eng doi: 10.1016/j.compeleceng.2015.01.002 – ident: CR33 – volume: 9 start-page: 9 year: 2014 end-page: 12 ident: CR21 article-title: Reconhecimento Óptico de caracteres para reconhecimento das sinalizações verticais das vias de trânsito publication-title: Rev SODEBRAS – year: 1969 ident: CR34 publication-title: Perceptrons – volume: 17 start-page: 879 year: 2006 end-page: 892 ident: CR25 article-title: Universal approximation using incremental constructive feedforward networks with random hidden nodes publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2006.875977 – volume: 102 start-page: 31 year: 2013 end-page: 44 ident: CR24 article-title: Robust extreme learning machine publication-title: Neurocomputing doi: 10.1016/j.neucom.2011.12.045 – ident: CR63 – ident: CR23 – volume: 21 start-page: 25 year: 1988 end-page: 39 ident: CR58 article-title: Neural nets for adaptative filtering and adaptative pattern recognition publication-title: IEEE Comput doi: 10.1109/2.29 – volume: 20 start-page: 273 issue: 3 year: 1995 end-page: 297 ident: CR14 article-title: Support vector networks publication-title: Mach Learn – volume: 4 start-page: 316 year: 2013 end-page: 322 ident: CR54 article-title: Vehicle position monitoring using hough transform publication-title: Int Conf Electron Eng Comput Sci (EECS 2013) – ident: CR3 – ident: CR38 – volume: 39 start-page: 846 issue: 3 year: 2013 end-page: 862 ident: CR59 article-title: A vision-based blind spot warning system for daytime and nighttime driver assistance publication-title: Comput Electr Eng doi: 10.1016/j.compeleceng.2013.03.020 – volume: 38 start-page: 1 issue: 1 year: 2009 end-page: 7 ident: CR53 article-title: Brinell and vickers hardness measurement using image processing and analysis techniques publication-title: J Test Eval – ident: CR17 – ident: CR31 – volume: 1 start-page: 511 year: 2001 end-page: 518 ident: CR56 article-title: Rapid object detection using a boosted cascade of simple features publication-title: IEEE Comput Soc Conf Comput Vision Pattern Recognit – volume: 78 start-page: 1415 year: 1990 end-page: 1442 ident: CR57 article-title: 30 years of adaptive neural networks: perceptron, madaline, and backpropagation publication-title: Proc IEEE doi: 10.1109/5.58323 – ident: CR7 – volume: 26 start-page: 19 issue: 1 year: 2004 end-page: 29 ident: CR18 article-title: The image foresting transform theory, algorithms, and applications publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2004.1261076 – volume: 70 start-page: 489 year: 2006 end-page: 501 ident: CR27 article-title: Extreme learning machine: theory and applications publication-title: Neurocomputing doi: 10.1016/j.neucom.2005.12.126 – year: 2002 ident: CR52 publication-title: Learning with kernels – volume: 2 start-page: 27:1 issue: 3 year: 2011 end-page: 27:27 ident: CR13 article-title: Libsvm: a library for support vector machines publication-title: ACM Trans Intell Syst Technol doi: 10.1145/1961189.1961199 – volume-title: Learning with kernels year: 2002 ident: 2388_CR52 – volume: 38 start-page: 1 issue: 1 year: 2009 ident: 2388_CR53 publication-title: J Test Eval – volume: 70 start-page: 489 year: 2006 ident: 2388_CR27 publication-title: Neurocomputing doi: 10.1016/j.neucom.2005.12.126 – volume: 2 start-page: 107 year: 2011 ident: 2388_CR26 publication-title: Int J Mach Learn Cybern doi: 10.1007/s13042-011-0019-y – volume: 1 start-page: 598 year: 1993 ident: 2388_CR51 publication-title: IEEE Int Conf Neural Netw doi: 10.1109/ICNN.1993.298625 – volume: 1 start-page: 511 year: 2001 ident: 2388_CR56 publication-title: IEEE Comput Soc Conf Comput Vision Pattern Recognit – volume: 6 start-page: 679 year: 1986 ident: 2388_CR11 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.1986.4767851 – volume: 13 start-page: 272 year: 2015 ident: 2388_CR37 publication-title: IEEE Trans Latin America doi: 10.1109/TLA.2015.7040658 – ident: 2388_CR23 – volume: 19 start-page: 120 issue: 2 year: 2009 ident: 2388_CR41 publication-title: Int J Imaging Syst Technol doi: 10.1002/ima.20188 – volume-title: Self-organization and associative memory year: 1989 ident: 2388_CR29 doi: 10.1007/978-3-642-88163-3 – volume: 9 start-page: 9 year: 2014 ident: 2388_CR21 publication-title: Rev SODEBRAS – volume: 55 start-page: 532 year: 1993 ident: 2388_CR20 publication-title: CVGIP Graph Models Image Process doi: 10.1006/cgip.1993.1040 – ident: 2388_CR7 – volume: 20 start-page: 273 issue: 3 year: 1995 ident: 2388_CR14 publication-title: Mach Learn – ident: 2388_CR64 – volume: 2 start-page: 121 issue: 2 year: 1998 ident: 2388_CR10 publication-title: Data Mining Knowl Discov doi: 10.1023/A:1009715923555 – volume: 22 start-page: 71 issue: 1 year: 2013 ident: 2388_CR32 publication-title: Neural Comput Appl doi: 10.1007/s00521-011-0748-6 – volume-title: Neural networks and learning machines year: 2008 ident: 2388_CR22 – volume: 45 start-page: 294 year: 2016 ident: 2388_CR35 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2015.09.025 – ident: 2388_CR17 doi: 10.1007/978-3-319-19665-7_5 – volume: 70 start-page: 36 year: 2015 ident: 2388_CR36 publication-title: Measurement doi: 10.1016/j.measurement.2015.03.039 – volume-title: The handbook of brain theory and neural networks year: 2003 ident: 2388_CR4 – volume: 21 start-page: 25 year: 1988 ident: 2388_CR58 publication-title: IEEE Comput doi: 10.1109/2.29 – volume: 42 start-page: 23 year: 2015 ident: 2388_CR60 publication-title: Comput Electr Eng doi: 10.1016/j.compeleceng.2015.01.002 – volume: 7 start-page: 31 issue: 1 year: 1989 ident: 2388_CR62 publication-title: Image Vis Comput doi: 10.1016/0262-8856(89)90017-6 – ident: 2388_CR16 doi: 10.1007/978-3-540-88636-5_90 – ident: 2388_CR3 doi: 10.1109/ISBI.2013.6556694 – ident: 2388_CR38 – ident: 2388_CR8 doi: 10.1016/B978-0-12-397041-1.00009-1 – volume: 78 start-page: 1415 year: 1990 ident: 2388_CR57 publication-title: Proc IEEE doi: 10.1109/5.58323 – volume: 240 start-page: 50 issue: 1 year: 2010 ident: 2388_CR2 publication-title: J Microsc doi: 10.1111/j.1365-2818.2010.03384.x – volume: 26 start-page: 19 issue: 1 year: 2004 ident: 2388_CR18 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2004.1261076 – ident: 2388_CR30 doi: 10.1109/ICIP.2002.1038171 – volume: 17 start-page: 879 year: 2006 ident: 2388_CR25 publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2006.875977 – ident: 2388_CR47 doi: 10.1007/978-3-642-53842-1_26 – volume: 39 start-page: 846 issue: 3 year: 2013 ident: 2388_CR59 publication-title: Comput Electr Eng doi: 10.1016/j.compeleceng.2013.03.020 – volume: 102 start-page: 31 year: 2013 ident: 2388_CR24 publication-title: Neurocomputing doi: 10.1016/j.neucom.2011.12.045 – volume: 29 start-page: 363 issue: 4 year: 2013 ident: 2388_CR46 publication-title: Revista Brasileira de Engenharia Biomédica doi: 10.4322/rbeb.2013.041 – volume: 4 start-page: 316 year: 2013 ident: 2388_CR54 publication-title: Int Conf Electron Eng Comput Sci (EECS 2013) – ident: 2388_CR63 doi: 10.1109/CVPR.2014.126 – volume: 16 start-page: 83 issue: 1 year: 2013 ident: 2388_CR6 publication-title: Pattern Anal Appl doi: 10.1007/s10044-011-0265-3 – volume: 15 start-page: 12,474 issue: 6 year: 2015 ident: 2388_CR1 publication-title: Sensors doi: 10.3390/s150612474 – ident: 2388_CR42 – volume-title: Artificial intelligence: a modern approach year: 2009 ident: 2388_CR50 – volume: 1 start-page: 296 issue: 4 year: 1990 ident: 2388_CR49 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.80266 – volume: 15 start-page: 11 issue: 1 year: 1972 ident: 2388_CR15 publication-title: Commun ACM doi: 10.1145/361237.361242 – volume-title: Perceptrons year: 1969 ident: 2388_CR34 – volume: 45 start-page: 512 issue: 1 year: 2012 ident: 2388_CR39 publication-title: Pattern Recognit doi: 10.1016/j.patcog.2011.07.013 – volume: 10 start-page: 988 issue: 5 year: 1999 ident: 2388_CR55 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.788640 – volume: 2 start-page: 27:1 issue: 3 year: 2011 ident: 2388_CR13 publication-title: ACM Trans Intell Syst Technol doi: 10.1145/1961189.1961199 – ident: 2388_CR9 – ident: 2388_CR19 doi: 10.1109/IVS.2012.6232222 – volume: 19 start-page: 120 issue: 2 year: 2009 ident: 2388_CR40 publication-title: Int J Imaging Syst Technol doi: 10.1002/ima.20188 – volume: 8 start-page: 3864 issue: 7 year: 2015 ident: 2388_CR45 publication-title: Materials doi: 10.3390/ma8073864 – ident: 2388_CR5 – volume: 1 start-page: 586 year: 1993 ident: 2388_CR48 publication-title: IEEE Int Conf Neural Netw doi: 10.1109/ICNN.1993.298623 – volume: 65 start-page: 35 year: 2014 ident: 2388_CR12 publication-title: Transp Res E Logist Transp Rev doi: 10.1016/j.tre.2013.12.011 – ident: 2388_CR43 doi: 10.1109/ICCIC.2012.6510256 – volume: 428 start-page: 206 year: 2015 ident: 2388_CR61 publication-title: Phys A Stat Mech Appl doi: 10.1016/j.physa.2015.01.064 – ident: 2388_CR31 – ident: 2388_CR33 doi: 10.1007/978-3-319-18833-1_19 – volume: 41 start-page: 7707 issue: 17 year: 2014 ident: 2388_CR44 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2014.07.013 – volume: 3 start-page: 1033 year: 2011 ident: 2388_CR28 publication-title: Proc Comput Sci doi: 10.1016/j.procs.2010.12.169
SSID	ssj0004685
Score	2.3231926
Snippet	The number of traffic accidents in Brazil has reached alarming levels and is currently one of the leading causes of death in the country. With the number of...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	573
SubjectTerms	Artificial Intelligence Classifiers Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Embedded systems Feature recognition Frames per second Image detection Image processing Image Processing and Computer Vision Kernel functions Least mean squares Least mean squares algorithm Machine learning Multilayer perceptrons Object recognition Optical character recognition Original Article Probability and Statistics in Computer Science Real time Roads & highways Signs Speed limits Street signs Support vector machines Traffic accidents Traffic accidents & safety Traffic safety Traffic signs Traffic speed
SummonAdditionalLinks	– databaseName: Advanced Technologies & Aerospace Database dbid: P5Z link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV07T8MwELagMLBQnqJQkAcmkIVj5zkhhFoxVR1Aqlgiv1JVatPSFH4_Z9dpCwIW1iROIt357jv7830IXcuCZVoZTaLQCBImNCFCRxkxWaZDJo2VRnFiE0mvlw4GWd8vuFWeVlnHRBeo9VTZNfI7zmgMvpew-H72RqxqlN1d9RIa22jHdkmw0g396HXjXKST5IQKxrJ7Ql7valLXRBQSFxTSMYGklRL-NS-twea3_VGXdrrN__7wAdr3gBM_LD3kEG2Z8gg1azEH7Of2MSo6E2kgCmkMMHJMrOY8rmaQ2_DYnoHCluiBV3SjaYktY36IRxMISHi2PG5gL4hS44ljaBrsJSmGeNUptjpBL93O8-MT8SIMRIVBuCCR5iZIBRVUQWViDJXUZCJKuQTgIAsNRg2YpkwrlgqmC6bSKCkkVEpCSiFSfooa5bQ0ZwhnmeKJBB8wXIWcKhlBcI0AoYhYcpmqFqK1CXLlO5RboYxxvuqt7KyWW1aatVrOW-hmNWS2bM_x18Pt2lK5n6lVDhEoAUgYJD_fXluxhW5rV9gY_du3zv9-2QXaYxYfOF5MGzUW83dziXbVx2JUza-cF38C9V75TA priority: 102 providerName: ProQuest
Title	Embedded real-time speed limit sign recognition using image processing and machine learning techniques
URI	https://link.springer.com/article/10.1007/s00521-016-2388-3 https://www.proquest.com/docview/1967441173 https://www.proquest.com/docview/3206225726
Volume	28
WOSCitedRecordID	wos000417319700047&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: PRVPQU databaseName: ProQuest advanced technologies & aerospace journals customDbUrl: eissn: 1433-3058 dateEnd: 20241209 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: P5Z dateStart: 20120101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1433-3058 dateEnd: 20241209 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: BENPR dateStart: 20120101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1433-3058 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07T8MwED5RYGDhjSiUygMTyJJrJ40zAipiqqryEGKJ_EpVqQ0VKfx-zmkSCgIkWBM7ie7se8Tf3QdwqlMeW-MsDQOnaBCxiCobxtTFsQ24dp4apSCbiPp9-fgYD8o67rxCu1dHkoWlrovd_B9Mn_p2KboZSUUD1tDbSc_XMLx9WCqGLHg4MW3xkJ5AVEeZ3z3iszP6iDC_HIoWvuZ6619fuQ2bZWhJLhZrYQdWXLYLWxVtAyl38R6kval2aG8swYBxQj27PMln6MXIxFc7EQ_pIDWw6DkjHhs_IuMpmh4yWxQW-Asqs2RaYDEdKcknRqTuCZvvw_117-7qhpZ0C9QEnWBOQytcRyqmmMEcxDmmmYtVKIXGEEGnFtXX4ZZxa7hU3KbcyDBKNeZESmulpDiA1ew5c4dA4tiISKO2nTCBYEaHaEZDjEVUVwstTRNYJffElL3IPSXGJKm7KBdyTDz-zMsxEU04q6fMFo04fhvcqpSZlHsyT9DWRBj8daLvbwvOumjcIt5twnml26XZP73r6E-jj2GD-8CgAMS0YHX-8upOYN28zcf5SxvWLnv9wbANjUH41C7W9TublfFA
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT9wwEB5RQGov5S2W8vABLiALr-1skkNVVTwEAlYcQEJcgl9BK-1mt2Rp1T_V39ixN1keAm4cuCax8_Dnmc_xzHwAmzrnqTXO0kg6RWXMYqpslFKXplZy7bw0ShCbiNvt5OoqPZ-Af3UujA-rrG1iMNS2b_w_8l3BWQuxF_PWj8Ev6lWj_O5qLaExgsWJ-_sHl2zl9-N9HN8tzg8PLvaOaKUqQI1syiGNrHDNRDHFDFJt55hmLlVRIjR6Qp1bfMomt4xbwxPFbc5NEsW5RuqvtFYqEdjvJ5iSkjM_i86j60d5mEECFFdMPppIinoXlYWipegoceHeougkEyqe-sEHcvtsPza4ucOZj_aBZuFrRajJz9EMmIMJV8zDTC1WQSrbtQD5QU87tLKWIE3u0mGn50g5QN9Nuj7Hi_hAFjIOp-oXxGcE3JJODw0uGYzSKfwBVVjSCxGojlSSG7dkXAm3XITLd3nbJZgs-oVbBpKmRsQaMe6EkYIZHaHziJCBqZYWOjENYPWQZ6aqwO6FQLrZuHZ0QEnmo-48SjLRgO1xk8Go_MhbF6_WyMgqS1RmaGFjpLzN-OXTD6hpwE4NvUetX7vXytudbcDno4uz0-z0uH3yDb5wz4VCDNAqTA7v7t0aTJvfw055tx5mEIGb90bkfzViWHc
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1JSwMxFH5oFfFiXbFaNQdPSjBNZj2KtihKKbjQ25BtSqGdlk7195vMZpUqiNeZZGbIS94y73vvAzgXMQ2V1Aq7jubY8YmPuXJDrMNQOVRoS42SkU343W7Q74e9guc0LdHuZUoyr2mwXZqS-dVUxVdV4Zv9m2nDYA8bkxNgtgprjsXR23D96XWhMDLj5DQhjIX3OKxMay57xFfD9OltfkuQZnanU__3F2_DVuFyout8j-zAik52oV7SOaDidO9B3B4LbfSQQsaRHGHLOo_SqbFuaGSroJCFeqAKcDRJkMXMD9BwbFQSmuYFB_YCTxQaZxhNjQpSigGqesWm-_DSaT_f3OGChgFLp-XMsauYbgWccCJNbKI1EUSH3A2YMK6DiJURa4sqQpWkAacqpjJw_ViYWIkLwXnADqCWTBJ9CCgMJfOF2QWaSYcRKVyjXl3jo3BPMBHIBpBSBpEsepRbqoxRVHVXztYxsrg0u44Ra8BFNWWaN-j4bXCzFGxUnNU0MjrIN05hy19-m1HiGaXnU68Bl6WcF2b_9K6jP40-g43ebSd6vO8-HMMmtb5DhplpQm0-e9MnsC7f58N0dppt8A_FFfpu
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=Embedded+real-time+speed+limit+sign+recognition+using+image+processing+and+machine+learning+techniques&rft.jtitle=Neural+computing+%26+applications&rft.au=Gomes%2C+Samuel+L&rft.au=Eliz%C3%A2ngela+de+S.+Rebou%C3%A7as&rft.au=Edson+Cavalcanti+Neto&rft.au=Papa%2C+Jo%C3%A3o+P&rft.date=2017-12-01&rft.pub=Springer+Nature+B.V&rft.issn=0941-0643&rft.eissn=1433-3058&rft.volume=28&rft.issue=1&rft.spage=573&rft.epage=584&rft_id=info:doi/10.1007%2Fs00521-016-2388-3&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0941-0643&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0941-0643&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0941-0643&client=summon