Final Program WVC2015 v2

Transcrição

XIWorkshopdeVisãoComputacional
WVC´2015
October05th–07th,2015
SãoCarlos–SP-Brazil
Program
UniversityofSãoPaulo
SãoCarlosSchoolofEngineering
XIWorkshopdeVisãoComputacional-October05th–07th,2015
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Summary
WelcometoWVC2015..................................................................................................5
GeneralInformation......................................................................................................6
AuthorandPresenterInformation................................................................................7
OralPresenters..........................................................................................................7
PosterPresenters......................................................................................................7
Committees....................................................................................................................8
OrganizingCommittee..............................................................................................8
SteeringCommittee..................................................................................................8
WorkTeam................................................................................................................9
ScientificCommittee.................................................................................................9
Keynote1.....................................................................................................................12
Keynote2.....................................................................................................................14
Keynote3.....................................................................................................................16
OralSession1...............................................................................................................18
OralSession2...............................................................................................................19
OralSession3...............................................................................................................20
OralSession4...............................................................................................................21
OralSession5...............................................................................................................22
PosterSession1...........................................................................................................24
PosterSession2...........................................................................................................28
UsefulPhoneNumbers................................................................................................32
Annotations..................................................................................................................33
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WelcometoWVC2015
TheSãoCarlosSchoolofEngineering,attheUniversityofSãoPaulo(EESC/USP),
has the pleasure to welcome you to the XI Workshop on Computer Vision (WVC
2015)andtothecityofSãoCarlos.
After four consecutive editions outside the state of São Paulo (Curitiba-PR in
2011,Goiânia-GOin2012,RiodeJaneiro-RJin2013andUberlândia-MGin2014)this
edition of the WVC will be held again at the city of São Carlos-SP. The academic,
technologicandindustrialforceofSãoCarlosconferredtothecitythetitleof"Capital
ofTechnology"inBrazil.TheUniversityofSãoPaulo(USP),theFederalUniversityof
São Carlos (UFSCar) and the Brazilian Agricultural Research Corporation (Embrapa)
arerecognizedfortheirexcellenceinteachingandresearch,makingthecityapoleof
scientific and technological development. São Carlos has a great concentration of
scientistsandresearchers,approximatelyonePhDforevery180inhabitants.
WVC2015wasplannedverycarefullybytheOrganizingCommittee,whichsought
toprovidetheparticipantswithacompletescientificprogram,withseverallectures,
oralsessionsandposterpresentations.Wehopethateveryonecantakeadvantageof
the activities offered by this event, broadening their horizons through the rich
scientificexchangeofexperiencesamongtheparticipants.
I would like to thank the WVC Steering Committee for trusting our team and
approvingourproposalfororganizingtheWVC2015inSãoCarlos.Iwouldalsoliketo
thankallthestudentsfromtheLaboratoryofComputerVision(LAVI)whohavebeen
workingsohardplanningandorganizingthiseventsincetheearlybeginning.
IamalsogratefultotheDepartmentofElectricalandComputerEngineering,the
São Carlos School of Engineering, FAPESP, CAPES and all ours sponsors for the
financialandoperationalsupport.
Finally, I would like to thank all the authors who submitted their work for this
eventandallthereviewerswhosacrificedpartoftheirtimetoevaluatemorethan
100paperssubmittedtotheworkshop.
IhopethatyouenjoyWVC2015andthatweallhaveagreatworkshop.
MarceloAndradedaCostaVieira
ChairofWVC2015
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GeneralInformation
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Yourbadgeispersonalandnottransferable.Pleaseuseyourbadgetoaccess
allactivitiesoftheworkshop.
Please turn off your mobile phones or switch them to silent/vibrate mode
duringallscientificactivities.
Allcertificateswillbesentviae-mailaftertheconference.
TheWVC2015proceedingswillbepublishedonlinesoonaftertheworkshop.
Only papers presented during the workshop will be published in the final
versionoftheproceedings.
The ticket for the Conference Dinner must be purchased at the event’s
office.
Ifyouneedaregistrationreceiptpleaserefertotheevent’soffice.
AuthorandPresenterInformation
OralPresenters
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Eachoralpresentationwillhave15minutes+5minutesforquestions.
YourpresentationcanbeinPortuguese,butyourslidesshouldbewrittenin
English.
Please refer to the workshop program to check the day and time of your
presentation.
Please make sure to upload your presentation to the computer in the
meetingroomonthedayofyourpresentation.Thebesttimeisbeforethe
firstsessionofthedayorduringbreaks.
PosterPresenters
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Posterspresentationsarehard-copy(paper/poster)formatonly.
YourpresentationcanbeinPortuguese,butyourpostershouldbewrittenin
English.
Poster dimensions should not exceed 180 cm (approx. 70 inch) high x 200
cm(80inch)wide.
Pleasehangyourposterinthepanelwiththesamenumberprovidedbythe
WVC2015program.
Pushpinswillbeprovidedtohangyourposter.
Please refer to the workshop program to check the day and time of your
presentation.
Postersauthorsarerequiredto1)displaytheposterduringthefirstcoffee
break of the day of your session 2) attend the Poster Session to answer
questions.
Remember that during your poster presentation you will have the
opportunity to discuss your research with more details. Thus, prepare the
best poster you possibly can, aiming at both attracting attention of the
readersandhavingenoughmaterialtoanswertheirquestions.
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Committees
OrganizingCommittee
MarceloAndradedaCostaVieira EESC/USP
ChairofWVC2015
EvandroLuisLinhariRodrigues
SteeringCommitteeChair
EESC/USP
AdilsonGonzaga EESC/USP
MaximiliamLuppe EESC/USP
ValdirGrassiJunior EESC/USP
MauricioCunhaEscarpinati FACOM/UFU
SteeringCommittee
EESC/USP
AparecidoNilceuArana FC/UNESP
InêsAparecidaGasparotoBoaventura IBILCE/UNESP
MaurílioBoaventura IBILCE/UNESP
MaurícioMarengoni MACKENZIE/SP
LuizAntonioPereiraNeves UFPR
MarcoAntônioPiteri FCT/UNESP
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WorkTeam
MarceloAndradedaCostaVieira
EESC/USP
ChairofWVC2015
EESC/USP
MaximiliamLuppe EESC/USP
ValdirGrassiJunior EESC/USP
HelderCesarR.deOliveira EESC/USP
LucasRodriguesBorges EESC/USP
PolyanaFerreiraNunes EESC/USP
TamirisNegri EESC/USP
RaissaTavares EESC/USP
CarolinaToledo EESC/USP
ScientificCommittee
AdilsonGonzaga
AlessandraAparecidaPaulino
AlexAffonso
AnaCláudiaMartinez
AndersonSoares
AndreBindilatti
AndréBackes
AndréMartins
AnfranseraiDias
AnselmoPaiva
AntonioMariaTomaseli
AntôniodaLuz
AntônioApolinário
AparecidoNilceuMarana
AyltonPagamisse
BrunoBarufaldi
EESC/USP
UNESP
EESC/USP
UFU
UFGO
UFSCar
UFU
EESC/USP
UEFS
UFMA
UNESP
IFTO
UFBA
UNESP
UNESP
EESC/USP
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BrunoMatheus
BrunoTravençolo
CarlosThomaz
CarolinaFerraz
CeliaBarcelos
CelsoOlivete
CésarCastañon
ChidambaramChidambaram
ClaudioGoes
ClodoaldoLima
CristinaNaderVasconcelos
DanielAbdala
DaniloEler
DelmarCarvalho
DenisSalvadeo
EmersonPedrino
EvandroL.L.Rodrigues
FabrizzioSoares
FátimaMedeiros
FatimaNunes
FlávioBortolozzi
GiovaniChiachia
GustavoB.Borba
HelioPedrini
HemersonPistori
HomeroSchiabel
HugoVieiraNeto
IálisPaulaJunior
InêsA.G.Boaventura
IvanNunesdaSilva
JacobScharcanski
JacquesFacon
JanderMoreira
JarbasSáJunior
JoãoBatistaNeto
JoãoMarar
JoãoManuelTavares
JoãoPauloPapa
JoséAlfredoF.Costa
JoséEduardoCastanho
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EESC/USP
UFU
FEI
EESC/USP
UFU
UNESP
PUC/Peru
UDESC
UEFS
UNICAMP
UFF
UFU
UNESP
UEFS
UNESP
UFSCar
EESC/USP
UFGO
UFCE
EACH/USP
CESUMAR
UNICAMP
UTFPR
UNICAMP
UCDB
EESC/USP
UTFPR
UFC
UNESP
EESC/USP
UFRGS
PUCPR
UFSCar
UFC
ICMC/USP
UNESP
FEUP/Portugal
UNESP
UFRN
UNESP
JoséRobertoNogueira
JoséSaito
JulioCesarNievola
JurandydeAlmeida
LeandroOliveira
LeonardoBatista
LeonardoMatos
LucasFerrarideOliveira
LucianoLulio
LucianoSilva
LucioJorge
LuizAntonioPereiraNeves
MarceloA.C.Vieira
MarceloZanchetta
MárcioAlexandreMarques
MarcoAntônioPiteri
MauricioC.Escarpinati
MauricioGalo
MauricioMarengoni
MaurilioBoaventura
MaximiliamLuppe
MessiasMeneguetiJunior
MicheleF.Angelo
MoacirPontiJúnior
MurilloHomem
NelsonMascarenhas
OdemirBruno
PauloEduardoAmbrósio
PauloM.A.Marques
RaissaTavaresVieira
RicardoFerrari
RonaldoCosta
SilviaMartiniRodrigues
TamirisNegri
ThiagoRibeiro
ValdineiBelini
ValdirGrassiJunior
WilliamSchwartz
UNESP
UFSCar
PUCPR
UNIFESP
UFGO
UFPB
UFS
UFPR
EESC/USP
UFPR
EMBRAPA
UFPR
EESC/USP
UFU
UNESP
UNESP
UFU
UNESP
Mackenzie
UNESP
EESC/USP
UNESP
UEFS
ICMC/USP
UFSCar
UFSCar
IFSC/USP
UESC
FMRP/USP
EESC/USP
UFSCar
UFGO
UMC
EESC/USP
UFU
UFSCar
EESC/USP
UFMG
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Keynote1
th
Monday-October05 –10:30hto12:00h
ComputerVisioninMedicalImagingand
Measurements
JacobScharcanski,Ph.D.
UFRGS-UniversidadeFederaldoRioGrandedoSul
InstitutodeInformática,RioGrandedoSul,Brasil
http://www.inf.ufrgs.br/~jacobs/
Abstract: In this talk, computer vision in medical imaging and measurements is
proposed as a way to facilitate the interpretation of phenomena based on medical
imagery, or to make inferences based on models of such phenomena. In order to
illustrate this presentation, several modeling issues in medical imaging and
measurements are discussed, and illustrated by examples. When modeling imaging
measurements, usually we are trying to describe the world (or a real world
phenomenon) using one or more images, and reconstruct some of its properties
based on imagery data (like shape, texture or color). Actually, this is an ill-posed
problemthathumanscanlearntosolveeffortlessly,butcomputeralgorithmsoften
arepronetoerrors.Nevertheless,insomecasescomputerscansurpasshumansand
helpinterpretimagerymoreaccurately,giventheproperchoiceofmodels,aswewill
discuss in this talk. Modeling medical imaging measurements often involves errors,
andestimatingtheexpectederrorofamodelcanbeimportantinsomeapplications
(e.g.whenestimatingatumorsizeanditspotentialgrowth,orshrinkage,inresponse
to treatment). Typically, a model has tuning parameters, and these tuning
parametersmaychangethemodelcomplexity.Wewishtominimizemodelingerrors
andthemodelcomplexity,inotherwords,togetthe‘bigpicture’weoftensacrifice
some of the small details. For example, estimating tumor growth (or shrinkage) in
response to treatment requires modeling the tumor shape and size, which can be
challenging for real tumors, and simplified models may be justifiable if the
predictions obtained are informative (e.g. to evaluate the treatment effectiveness).
This issue is closely related to machine learning and pattern recognition, and
techniques of these areas can be adapted to resolve problems in medical imaging
measurements. To conclude this talk, open problems in medical imaging
measurementsandmodelselectionarediscussedinsomedetail.
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Short Bio: Jacob Scharcanski is a (Full) Professor in Computer
Science at the Federal University of Rio Grande do Sul (UFRGS),
Brasil. He holds a cross appointment with the Department of
ElectricalEngineeringatUFRGS,andalsoisanAdjunctProfessor
withtheDepartmentofSystemsDesignEngineering,Universityof
Waterloo, Canada. He authored and co-authored over 150
refereed journal and conference papers, book chapters and books, and delivered
over 30 invited presentations worldwide. He serves as an Associate Editor for two
journals, and has served on dozens of International Conference Committees. In
additiontohisacademicactivities,hehasseveraltechnologytransferstotheprivate
sector. Professor Scharcanski is a licensed Professional Engineer (PEO, Canada),
SeniorMemberoftheIEEE,MemberofSPIE,andservesasCo-ChairoftheTechnical
CommitteeIEEEIMSTC-17(ImagingMeasurementsandSystems).
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Keynote2
th
Tuesday-October06 –10:30hto12:00h
PatchFoveationinNonlocalImageFiltering
AlessandroFoi,Ph.D.
DepartmentofSignalProcessing
TampereUniversityofTechnology,Tampere,Finland
http://www.cs.tut.fi/~foi/
Abstract:Whenwegazeascene,ourvisualacuityismaximalatthefixationpoint(imagedby
the fovea, the central part of the retina) and decreases rapidly towards the periphery of the
visualfield.Thisphenomenonisknownasfoveation.Toformacompleteimageofthescene,
the human visual system (HVS) typically processes a multitude of foveated retinal images
gathered at different fixation points. In this talk we look at the analogies and connections
betweenthisfeatureoftheHVSandmodernnonlocal(NL)imagefilters.
NL filters rely on the assumption that natural images contain a large number of mutually
similarpatchesatdifferentlocationswithintheimage:similarpatchesarefirstidentified,and
then used into adaptive weighted averages or more sophisticated nonlinear shrinkage. Such
approachisatthecoreofseveralofthemosteffectiveimagerestorationmethodstodate.
Crucial elements in the design of NL filters are the metric or distance used for assessing the
patch similarity, and the size of the patch. Large patches guarantee stability of the distance
with respect to degradations such as noise; however, the mutual similarity between pairs of
patches typically decreases as the patch size grows. Thus, a windowed Euclidean distance is
commonly employed to balance these two conflicting aspects, assigning lower weights to
pixels far from the patch center. Choosing a metric for patch similarity corresponds to
assuming a specific model for describing natural images and their self-similarity: the
effectivenessofNLmethodsdependsstronglyonthevalidityofsuchunderlyingmodel.
We particularly investigate a different form of self-similarity: the foveated self-similarity.
Foveationherecorrespondstoaspatiallyvariantbluroperator,characterizedbyblurkernels
whosebandwidthdecreaseswiththespatialdistancefromthepatchcenter.Incontrastwith
theconventionalwindowing,whichisonlyspatiallyselectiveandattenuatessharpdetailsand
smoothareasinequalway,patchfoveationprovidesselectivityinbothspaceandfrequency,
mimickingtheHVSinabilitytoperceivedetailsattheperipheryofthecenterofattention.
Throughout the talk, we adopt the image denoising problem as a simple means of assessing
the effectiveness of descriptive models for natural images. We show that, in nonlocal image
filtering,thefoveatedself-similarityisfarmoreeffectivethantheconventionalwindowedselfsimilarity. To facilitate the use of foveation in nonlocal imaging, we present a general
frameworkfordesigningfoveationoperators,i.e.linearoperatorsproducingfoveatedpatches
by means of spatially variant blur. Within this framework, several parametrized families of
foveation operators are demonstrated, including anisotropic ones. Strikingly, the operators
enabling the best denoising performance on complex natural images are the radial ones, in
completeagreementwiththeorientationpreferenceoftheHVS.
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Short Bio: Alessandro Foi received the M.Sc. degree in
Mathematics from the Università degli Studi di Milano, Italy, in
2001, the Ph.D. degree in Mathematics from the Politecnico di
Milano in 2005, and the D.Sc.Tech. degree in Signal Processing
from Tampere University of Technology, Finland, in 2007. He is
currently an Academy Research Fellow with the Academy of
Finland, at the Department of Signal Processing, Tampere University of Technology,
whereheisalsoAssociateProfessor.Hisresearchinterestsincludemathematicaland
statistical methods for signal processing, functional and harmonic analysis, and
computational modeling of the human visual system. His recent work focuses on
spatially adaptive (anisotropic, nonlocal) algorithms for the restoration and
enhancement of digital images, on noise modeling for imaging devices, and on the
optimaldesignofstatisticaltransformationsforthestabilization,normalization,and
analysisofrandomdata.HeisaSeniorMemberoftheIEEE,MemberoftheImage,
Video, and Multidimensional Signal Processing Technical Committee of the IEEE
SignalProcessingSociety,andanAssociateEditorfortheIEEETransactionsonImage
ProcessingandforthenewIEEETransactionsonComputationalImaging.
15
Keynote3
th
Wednesday-October07 –10:30hto12:00h
UsingComputerVisionwithdronesinAgriculture
LúcioAndrédeCastroJorge,Ph.D.
EMBRAPA-EmpresaBrasileiradePesquisaAgropecuária.
SãoCarlos,Brasil.
http://lattes.cnpq.br/3036476562950521
Abstract: The use of drones in agriculture increase the use of advanced sensors to
evaluate the development of different crops providing additional agricultural
knowledgetodealwiththevariabilityinthefieldinordertocontributetoincreased
crop yields. The technological development of remote sensing techniques using
dronesorUAVs,havebeenimprovingthecropmanagementwithgreaterspatialand
temporalresolution.Therefore,themainchallengeinthisareaisthedevelopmentof
methodsofprocessingimagesquicklyandaccuratelyworkingwithlargevolumesof
multidimensional and temporal data. However, despite the enormous potential of
thisremotesensing,theirimplementationinpracticeisverylimited.Thisworkwillbe
presented the state of the art at all stages, from image acquisition, processing,
segmentation,classificationamongothersforapplicationsinagriculture.
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Short Bio: Degree in Electrical Engineering - Electronics and
Electrical Engineering at the School of Barretos Engineering
(1987); Master's degree in Computational Mathematics and
Computer Science at Institute of Mathematics and Computer
SciencesattheUniversityofSãoPaulo,ICMC-USP(2001);PhDin
Signal Processing and Instrumentation from the School of
Engineering, University of São Paulo, SEL-EESC-USP (2011); LatoSensu in image
processing from the University of Campinas - Unicamp (1990); LatoSensu in
Geografical Information Systems from the Federal University of São Carlos - UFSCar
(2005); Researcher at Embrapa Instrumentation since 1990; Professor of Image
Processing, Computer Graphics and Artificial Intelligence at UNISEB- Colleges COC
since2006.ExperienceinComputerScience,workingonthedevelopmentofimage
processingsoftware,embeddedsystems,mobiledevices(PDAs),patternrecognition
and intelligence computing, computer graphics and geo-referenced systems.
Experience applied in several projects in Agriculture, Precision Agriculture, GIS,
agricultural monitoring, remote sensing, study of roots, leaves, plant diseases and
deficiencies,developmentofUAV(unmannedaerialvehicle)foragriculturaluse.
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OralSession1
th
Monday-October05 -14:00hto15:40h
Chair:AdilsonGonzaga
Co-Chair:MarcoA.Piteri
PatternRecognition
1
MakinganImageWorthaThousandVisualWords-Glauco
14h00-14h20 VitorPedrosa,USP;AgmaTraina,ICMC/USP;
2
A Study of Filtering Approaches for Sliding Window
14h20-14h40 Pedestrian Detection - Artur Correia, UFMG; Victor Hugo
Melo,UFMG;WilliamSchwartz,UFMG;
3
A Bipartite Graph Model Approach For Discriminant
Features Evaluation - Pamela Iupi Peixinho, Centro
14h40-15h00 Universitário da FEI; Paulo Silva Rodrigues, Centro
Universitário da FEI; Guilherme Wachs Lopes, Centro
UniversitáriodaFEI;
4
LocalMappedPatternforSpoofFingerprintDetection–Inês
15h00-15h20 Boaventura, UNESP; Maurilio Boaventura, UNESP; Rodrigo
Contreras,UNESP;
5
Multi-scale Local Mapped Pattern for Image Texture
Analysis - Maurilio Boaventura, UNESP; Rodrigo Contreras,
15h20-15h40 UNESP;InêsBoaventura,UNESP;
THISPRESENTATIONWASMOVEDTOORALSESSION4
5
Detection and Classification of the Periorbital Wrinkles in
2D Images - Daniel Costa, UFBA; Angelo Duarte, UEFS;
15h20-15h40 Deborah Duarte, Clínica Deborah Duarte; Leizer Schnitman,
UFBA;
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OralSession2
th
Tuesday-October06 -08:20hto10:00h
Chair:MarceloA.C.Vieira
Co-Chair:MaurílioBoaventura
FilteringandRestoration
1
2
Filtering Poisson Noise in Digital Breast Tomosynthesis
Using an Interactive Non-Local Means Scheme Based on
08h20-08h40 Stochastic Distances - Andre Bindilatti, UFSCar; Marcelo
Vieira, USP; Predrag Bakic, UPENN; Andrew Maidment,
UPENN;NelsonMascarenhas,UFSCar;
Using SSIM as Convergence Criteria in a Variational
08h40-09h00 Superresolution Bayesian Approach - Thais Nascimento,
UFES;EvandroSalles,UFES;
3
A New Prior for Inverse Problems Based Demosaicking -
09h00-09h20 RomárioKeitiFugita,UTFPR;MarceloVictorZibetti,;Daniel
Pipa;
4
Evolving Convolutional Kernels Using Evolutionary
09h20-09h40 Computing - José Augusto Stuchi, CPqD; Marcus Angeloni,
CPqD;MateusCarniatto;BrunoCereser;
5
Denoising Computed Tomography Projections Using
Contextual Filters With Statistical Estimation from a Non09h40-10h00 Local Approach on Anscombe Domain - Vinicius Assis,
UNESP; Denis Salvadeo, UNESP; Nelson Mascarenhas,
UFSCar;AlexandreLevada,UFSCar;
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OralSession3
th
Chair:MauricioCunhaEscarpinati
Co-Chair:EvandroLuisLinhariRodrigues
MedicalandBiomedicalApplications
1
Using the Non-local Means Algorithm to Denoise
Mammogaphic Images Acquired with Reduced Radiation
Dose-PolyanaNunes,USP;AndreBindilatti,UFSCar;Helder
14h00-14h20 Oliveira, USP; Lucas Borges, USP; Predrag Bakic, UPENN;
Andrew Maidment, UPENN; Nelson Mascarenhas, UFSCar;
MarceloA.C.Vieira,USP;
2
Graph Measures for Cell Tissue Classification - Letícia
14h20-14h40 Oliveira,UFABC;FranciscoZampirolli,UFABC;
3
GPU-Optimized Pulmonary Nodule Retrieval Based on 3D
14h40-15h00 MarginSharpnessDescriptors-JoséFerreira,UFAl;Marcelo
Oliveira,UFAl;
4
ProposalofLocalAutomaticWeighingAttributetoRetrieve
Similar Lung Cancer Nodules - David Jones Lucena, UFAl;
15h00-15h20 Marcelo Oliveira UFAl; Aydano Pomponet, UFAl; José
Ferreira,UFAl;
5
AutomaticDetectionofLeukocytesfromIntravitalVideo
MicroscopyusingthePhaseCongruencyTechnique-
15h20-15h40
KathianiSouza,UFSCar;BrunoGregóriodaSilva,UFSCar;
JulianaCarvalho-Tavares,UFMG;RicardoFerrari,UFSCar;
20
OralSession4
th
Wednesday-October07 -08:20hto10:00h
Chair:AparecidoNilceuMarana
Co-Chair:InêsA.G.Boaventura
FeatureExtraction
1
Combining Wavelets and 2D Gabor Descriptors for Iris
Recognition in Noncooperative Environments - Sirlene
08h20-08h40 Peixoto, UFOP; Pedro Silva, UFOP; Alvaro Guarda, UFOP;
EduardoLuz,UFOP;DavidMenotti,UFOP;
2
TextureAnalysisUsingLocalFractalDimensionofComplex
Networks - Diogo Gonçalves, UFMS; Lucas Silva, UFMS;
08h40-09h00 Reinaldo Felipe Araujo, UFMS; Bruno Machado, UFMS;
WesleyGonçalves,UFMS–CPPP;
3
Detection and Classification of the Periorbital Wrinkles in
2D Images - Daniel Costa, UFBA; Angelo Duarte, UEFS;
09h00-09h20 Deborah Duarte, Clínica Deborah Duarte; Leizer Schnitman,
UFBA;
THISPRESENTATIONWASMOVEDTOORALSESSION1
3
Multi-scale Local Mapped Pattern for Image Texture
09h00-09h20 Analysis - Maurilio Boaventura, UNESP; Rodrigo Contreras,
UNESP;InêsBoaventura,UNESP;
4
Multimodal Discriminant Analysis of Biomarkers in
09h20-09h40 Alzheimer’s Disease - Samantha Castro, FEI; Luciano
Sanchez;GeraldoBusattoFilho;CarlosThomaz,FEI;
5
ColorTextureClassificationbyaLocalMultiscaleDescriptor
09h40-10h00 -TamirisNegri,EESC/USP;AdilsonGonzaga,EESC/USP;
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OralSession5
th
Wednesday-October07 -14:00hto15:40h
Chair:MauricioMarengoni
Co-Chair:LuizA.P.Neves
ApplicationsinComputerVision
1
Automated Method for Determining Grid Lines Using
14h00-14h20 MAMA-CDM Phantom Images - Bruno Barufaldi, USP;
HomeroSchiabel,USP;LeonardoBatista,UFPB;
2
Shape Aligment Using ASM and SVM in Vehicle Images -
14h20-14h40 Maria Aragão, UFS; Jovan Fernandes Junior, UFS; Leonardo
Matos,UFS;
3
Information Theoretic Approaches for Adaptive Wavelet
14h40-15h00 ShrinkageinImageDenoising-AlexandreLevada,UFSCar;
4
Omnidirectional Vision Architecture for Embedded Robot
15h00-15h20 NavigationwithRaspberryPi-AndersonNascimento,UFBA;
PauloFarias,UFBA;
5
MaximumResponseFiltersandTextureTechniquesApplied
15h20-15h40 to Classification of Mammographic Breast Mass - Danilo
Fistarol,UFMS-CPPP;WesleyGonçalves,UFMS–CPPP;
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23
PosterSession1
th
Monday-October05 -15:40hto17:30h
Chair: MaximiliamLuppe
Co-Chairs:
AdilsonGonzaga
MarceloA.C.Vieira
MauricioCunhaEscarpinati
AparecidoNilceuMarana
MarcoA.Piteri
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1.
Analysis of Iris Texture Under Pupil Contraction/Dilation for Biometric
Recognition-JonesSouza,USP;AdilsonGonzaga,USP;RaissaTavaresVieira,
USP;
2.
Unconstrained Face Recognition using Weber Local Descriptor and SVM -
AlexAffonso,USP;EvandroRodrigues,EESC/USP;
3.
Comparison Between Isotropic and Adaptive Pore Detection Methods for
Fingerprint Recognition - Murilo Varges da Silva, UNESP/IFSP; João Paulo
Luiz,UNESP;MarcusAngeloni,CPqD;AlessandraPaulino,UNESP;Aparecido
Marana,UNESP;
4.
Wrist Veins Texture Analysis for Biometric Systems - Vitor Barbedo, IFSP;
JonesSouza,USP;
5.
PerformanceEvaluationof3DTextureAttributesand3DMarginSharpness
intheRetrievalof Lung Nodules Similars - Lucas Lima, UFAL; José Ferreira
Junior,UFAL;MarceloOliveira,UFAL;
6.
Face Recognition with Uniform Local Binary Patterns - Luiz D Amore,
Universidade Presbiteriana Mackenzie; Maurício Marengoni, Universidade
PresbiterianaMackenzie;
7.
Patterns Detection in ECG Signal Applied to Biometric Recognition -
Henrique Passos, USP; Daniel Martins da Costa, USP; Sarajane Peres, USP;
ClodoaldoLima,USP;
8.
IrisRecognitionusingSupportVectorMachineandLeastSquaresSupport
Vector Machine: A Comparative Study - Daniel Martins da Costa, USP;
HenriquePassos,USP;SarajanePeres,USP;ClodoaldoLima,USP;
9.
ComparisonofCharacteristicsofDescriptorsfortheConstructionofDigital
ImagesMosaic-DaviFernandes,IFSPBoituva;AndréTarallo,IFSPBoituva;
10. Texture Analysis by Grouping Similar Vertices in Complex Networks -
LeonardoScabini,UFMS;WesleyGonçalves,UFMS/CPPP;AmauryCastroJr,
UFMS;
11. Improving Image Classification Performance by Descriptor Size Reduction
andBag-of-Features-CarolinaFerraz,USP;AdilsonGonzaga,USP;
12. BrazilianLicensePlateCharacterRecognitionusingDeepLearning-Sirlene
Peixoto,UFOP;GabrielGonçalves,UFMG;GuillermoCámara-Chávez,UFOP;
WilliamSchwartz,UFMG;DavidMenotti,UFOP;
13. Gabor Filter Parameter Optimization for Localization Step of Plate
RecognitionSystem-OzgurAltun,ProlineBilisimSistemleri;FarukCanKaya,
ProlineBilisimSistemleri;TuranMuratGuvenc,ProlineBilisimSistemleri;
14. EvaluationofTechnicalofImageEnhancementwithObjetivesMetricsand
Execution Average Time - Jonas Rodrigues Vieira dos Santos, UFC; Paulo
CésarCortez;RodrigoFernandesFreitas;EdsonCavalcantiNeto;
15. Evaluation of Block-Matching 3D and Wavelet Transform with ShrinkThresholding Technique for Digital Mammography Denoising - Helder
Oliveira, USP; Polyana Nunes, USP; Lucas Borges, USP; Predrag Bakic,
UPENN;AndrewMaidment,UPENN;MarceloVieira,USP;
16. AnalysisoftheWisconsinBreastCancerDatasetandMachineLearningfor
BreastCancerDetection-LucasBorges,USP;
17. Analysis of the Influence of Distance Metrics on the Semi-supervised
Algorithm of Particle Competition and Cooperation - Lucas Guerreiro,
UNESP;FabrícioBreve,UNESP
18. Preprocessing Images to Improve Deep Neural Networks Classification -
Horst Erdmann, Boolabs; Fernando Ito, UFSCar; Danilo Santos, Boolabs;
DanielTakabayashi,Boolabs;JanderMoreira,UFSCar;
19. Parallelization of the Particle Competition and Cooperation Approach for
Semi-SupervisedLearning-RaulSouza,UNESP;FabrícioBreve,UNESP;
20. System for Corneal Ulcer Analysis - Luciana Almansa, DCM/USP; Sidney
Sousa,FFCLRP/USP;Jean-JacquesDeGroote,Estácio;
21. Information Portal to Support Research in Bone Age Estimation - André
Silva; Celso Olivete, FCT/UNESP; Rogério Garcia, FCT/UNESP; Ronaldo
MessiasCorreia,FCT/UNESP;
THISPRESENTATIONWASMOVEDTOPOSTERSESSION2
25
22. Implementation and Research of Parallel Computing Algorithms for the
CharacterizationofMedicalImages-MatheuSantos,UESC;PedroOliveira,
UESC;MarceloHonda,UESC;
23. Automatic Translation of Brazilian Sign Language (LIBRAS) with Hidden
MarkovModels(HMM):Usingsamplesofdeaf,interpretersandastudent
ofLIBRAS-DiegoDias,UFSCar;EdnaldoPizollato,UFSCar;
24. Radial Search Algorithm: A Gesture Recognition Algorithm to Real-time
Systems-VirgilioLima,UEFS;JoãoGertrudes,UEFS;
25. Supervised Traffic Signs Recognition in Digital Images using Interest
Points- Matheus Gutoski, UDESC; Chidambaram Chidambaram, UDESC;
GilmáriodosSantos,UDESC;
26. ApplicationofanApproachBasedonToleranceNearSetsinMangoColor
Detection-DiegoSaqui,UFSCAR;JoséSaito,UFSCAR;LucioJorge,Embrapa;
RodrigoPiassi;
27. FishSpeciesRecognitionusingTemplateMatchingandLocalDescriptors-
Gercina Silva, INOVISAO/UCDB; Ueliton Freitas, UFMS; Rafael Telles, UCDB;
HemersonPistori,UCDB;
26
28. An Automatic Methodology for Face Shape Identification on Images -
MagjeanderSilva,UFMG;EricksonNascimento,UFMG;
27
PosterSession2
th
Chair: ValdirGrassiJr.
Co-Chairs:
MaurílioBoaventura
InêsA.G.Boaventura
LuizA.P.Neves
MauricioMarengoni
28
1.
Content-BasedImageRetrievalinMedicalImagestosupportaComputerAided Diagnosis - Ronaldo Costa, UFG; Elias Macena, INF/UFG; Rogerio
Salvini,INF/UFG;LeandroOliveira,INF/UFG;FátimaNunes,EACH/USP;
2.
Automatic Identification of Trees from Aerial Images of the Internet to
Prevent Failures in Power Distribution System - Ronaldo Costa, UFG;
Heuber Lima, INF/UFG; Anderson Soares, INF/UFG; Gustavo Laureano,
INF/UFG;
3.
Automatic Correction of Multiple-Choice Tests on Android Devices -
FranciscoZampirolli,UFABC;RodrigoChina,UFABC;RogérioNeves,UFABC;
JoséGuilici-Gonzalez,UFABC;
4.
Recognition of Vehicles Logos using SURF - Cristiano Macedo, UFSCAR;
MarcioFernandes;
5.
Aq-GaussianSpatialFiltering-CelsoGallão,FEI;PauloSilvaRodrigues,FEI;
6.
IdentificationofFoliarSoybeanDiseasesusingLocalDescriptors-Jonatan
Patrick Oruê, UFMS; Wesley Gonçalves, UFMS/CPPP; Wesley Eiji Kanashiro,
UFMS/FACOM; Rillian Diello Pires, UFMS/FACOM; Bruno Machado,
UFMS/CPPP;MauroArruda,UFMS/CPPP;
7.
RecognitionofSoybeanInsectPestsusingSURFandTemplateMatching-
Diogo Soares, FACOM - UFMS; Gercina Silva, INOVISAO/UCDB; Ariadne
Gonçalves,INOVISAO/UCDB;LucasTorres,UCDB;HemersonPistori,UCDB;
8.
Comparison of Feature Spaces in Fruit Recognition - Marcela Nishida,
UNESP;DaniloEler,UNESP;AlmirArtero,UNESP;MaurícioDias,UNESP;
9.
Comparison of Computer Techniques for Handwritten Character
Recognition - Priscila Macanhã, UNESP; Danilo Eler, UNESP; Almir Artero,
UNESP;
10. Classification of Diaphorina citri into Microscopy Images - José Leonardo
Melo, UEFS; Michele F. Angelo, DTEC-UEFS; Marcelo Miranda,
FUNDECITRUS;
11. ALowCostEmbeddedSystemwithComputerVisionandVideoStreaming-
AndréCurvello,USP;EvandroRodrigues,USP;ThiagoLima;
12. PDIExp: Web Platform for Direct Experimentation of Computer Vision
Methods-LenonFachiano;CelsoOlivete;PedroReis,UNESP;RafaelSantos;
RonaldoMessiasCorreia,FCT/UNESP;RogérioGarcia,FCT/UNESP;
13. AverageGrainSizeEstimationonMetallicMaterialsusingImageAnalysis-
Diego Araujo, Federal University of Ouro Preto; Geraldo Faria, Federal
UniversityofOuroPreto;GladstonMoreira,UFOP;DavidMenotti,UFOP;
14. Automatic Counting and Measuring Fish Oocytes from Microscopic
Images- Jonathan Ramos, USP; Carolina Watanabe, UNIR; Agma Traina,
ICMC/USP;DiogoHungria,UNIR;TallesColaço,UNIR;CarolinaDória,UNIR;
15. InvestigatingColorModelsforCellularSegmentationofWhiteBloodCells-
Thaína Tosta, UFU; Andrêssa de Abreu, UFU; Diogo Vilela, USP; Leandro
Neves, USP; Bruno Travençolo, FACOM/UFU; Marcelo Zanchetta,
FACOM/UFU;
16. Auto Feature Weight for Interactive Image Segmentation using Particle
CompetitionandCooperation-FabrícioBreve,UNESP;
17. Thermal Image Segmentation in Studies of Wildlife Animals - Mauro de
Arruda,UFMS;BrunoMachado,UFMS;WesleyGonçalves,UFMS/CPPP;João
HenriqueDias,CESP;LauryCullen,CESP;CristinaGarcia,CESP;JoseJr,USP;
18. Method Based in Corner and Edge Detection to Separate Soybean
SeedlingsStructures-DanielLima,USP;EvandroRodrigues,EESC/USP;Lucio
Jorge,Embrapa;
19. Comparative Study on Otsu, EICAMM and Level Set Techniques to
Automatic Segmentation of Breast Lesions in Digital Mammography -
KaremMarcomi,USP;HomeroSchiabel,USP;
20. Ship Segmentation in Sluice - Fagner Pimentel, UFBA; Michele Angelo,
DTEC/UEFS;DiegoFrias,UNEB;
21. Illumination-invariant Image Segmentation for Robot Soccer - Sávio
Cantero,UFMS;WesleyGonçalves,UFMS/CPPP;
22. Application of Gaussian Markov Random Fields in Citrus Segmentation -
JoãoHerrera,Embrapa;LeandroCandido,Embrapa;LucioJorge,Embrapa;
29
23. IndoorSimulationforControlofUnmannedAerialVehicles-RicardoVergili
Filho,UFABC;OswaldoFratiniFilho;FranciscoZampirolli,UFABC;
24. Visão Computacional Aplicada a Dispositivos Móveis para Automação
Robótica-GuilhermeG.Moreira,SENAC;MarioL.P.Toledo,SENAC;FábioR.
deMiranda,SENAC;
25. The Use of Computing Vision and Affective Computing in Building
Humanoid Robots - Luiz Pereira Neves, UFPR; Reginaldo Silveira, UniBrasil;
Maria Costa, UFPR; Andreia Jesus, UFPR; Rafaela Otemaier, UFPR; José
Feger,UFPR;
26. A Visual Attention Approach for the Tracking of Vehicles Through UAV -
Raphael Montanari, USP; Daniel Tozadore, USP; Eduardo Fraccaroli, USP;
AlcidesBenicasa,UFS;RoseliRomero,USP;
27. Traffic Sign Detection and Recognition using the AdaBoost and Transition
Between Pixels - Francisco Silva, UNOESTE; João Paulo Masiero, UNOESTE;
DanilloPereira,UNOESTE;AlmirArtero,UNESP;MarcoPiteri,UNESP;
28. Vehicles Classification Using Optical Flow - Fernando Castro, UNOESTE;
Francisco Silva, UNOESTE; Danillo Pereira, UNOESTE; Almir Artero, UNESP;
MarcoPiteri,UNESP;
29. Identification and Tracking of People Using Digital Images Captured from
VideoCameras-LucasManuel,UNOESTE;FranciscoSilva,UNOESTE;Danillo
Pereira,UNOESTE;AlmirArtero,UNESP;MarcoPiteri,UNESP;
30. Information Portal to Support Research in Bone Age Estimation - André
Silva; Celso Olivete, FCT/UNESP; Rogério Garcia, FCT/UNESP; Ronaldo
MessiasCorreia,FCT/UNESP;
30
31
UsefulPhoneNumbers
SEL/EESC/USP
(16)3373-9371
CampusSecurityStaff
(16)3373-9112
PoliceDept.
190
MedicalEmergency(SAMU)
192
FireDept.andRescue
193
MedicalEmergency(SantaCasa)
(16)3509-1100
Taxi
(16)3415-6005
32
Annotations
33 XIWorkshopdeVisãoComputacional-October05th–07th,2015
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34
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36
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40

Final Program WVC2015 v2

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