Smart Computer Vision

359 Pages·2023·14.08 MB·English

by  B. Vinoth Kumar, P. Sivakumar, B. Surendiran, Junhua Ding

EAI/Springer Innovations in Communication and Computing
B. Vinoth Kumar
P. Sivakumar
B. Surendiran
Junhua Ding   Editors
Smart
Computer
Vision
EAI/Springer Innovations in Communication
and Computing
SeriesEditor
ImrichChlamtac,EuropeanAllianceforInnovation,Ghent,Belgium
The impact of information technologies is creating a new world yet not fully
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arenotlimitedto,wirelessnetworks;mobilecommunication;designandlearning;
gaming;interaction;e-healthandpervasivehealthcare;energymanagement;smart
grids;internetofthings;cognitiveradionetworks;computation;cloudcomputing;
ubiquitousconnectivity,andinmodegeneralsmartliving,smartcities,Internetof
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from hosted and sponsored European Alliance for Innovation (EAI) conferences
that present cutting edge, global research as well as provide new perspectives on
traditional related engineering fields. This content, complemented with open calls
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B. Vinoth Kumar • P. Sivakumar • B. Surendiran •
Junhua Ding
Editors
Smart Computer Vision
Editors
B.VinothKumar P.Sivakumar
PSGCollegeofTechnology PSGCollegeofTechnology
Coimbatore,TamilNadu,India Coimbatore,TamilNadu,India
B.Surendiran JunhuaDing
Thiruvettakudy UniversityofNorthTexas
NationalInstituteofTechnology Denton,TX,USA
Puducherry,Karaikal,India
ISSN2522-8595 ISSN2522-8609 (electronic)
EAI/SpringerInnovationsinCommunicationandComputing
ISBN978-3-031-20540-8 ISBN978-3-031-20541-5 (eBook)
https://doi.org/10.1007/978-3-031-20541-5
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Preface
Computer vision is a field of computer science that works on enabling computers
to see, identify, and process images in the same way that human vision does,
and then provide appropriate output. It is like imparting human intelligence and
instincts to a computer. It is an interdisciplinary field that trains computers to
interpretandunderstandthevisualworldfromdigitalimagesandvideos.Themain
objectiveofthiseditedbookistoaddressanddisseminatestate-of-the-artresearch
and development in the applications of intelligent techniques for computer vision.
Thisbookprovidescontributionswhichincludetheory,casestudies,andintelligent
techniquespertainingtothecomputervisionapplications.Thiswillhelpthereaders
tograsp the extensive point of view and the essence of the recent advances inthis
field. The prospective audience would be researchers, professionals, practitioners,
andstudentsfromacademiaandindustrywhoworkinthisfield.
Wehopethechapterspresentedwillinspirefutureresearchbothfromtheoretical
and practical viewpoints to spur further advances in the field. A brief introduction
toeachchapterisasfollows.
Chapter1discussesthemachinelearningapproachesappliedtoautomaticsports
video summarization. Chapter 2 proposes a new technique for lecture video seg-
mentation and key frame extraction. The results are compared against six existing
state-of-the-arttechniquesbasedoncomputationaltimeandshottransitions.
Chapter 3 presents a system to detect the potholes in the pathways/roadways
using machine learning and deep learning approaches. It uses HOG (histogram
of oriented gradients) and LBP (local binary pattern) features to enhance the
classificationalgorithmsperformance.
Chapter 4 aims to explore various feature extraction techniques and shape
detection approaches required for image retrieval. It also discusses the real-time
applications of shape feature extraction and object recognition techniques with
examples.
Chapter5describesanapproachfortextureimageclassificationbasedonGray
Level Co-occurrence Matrix (GLCM) features and machine learning algorithm.
Chapter6presentsanoverviewofunimodalandmultimodalaffectivecomputing.It
alsodiscussesthevariousmachinelearninganddeeplearningtechniquesforaffect
recognition. Chapter 7 proposes a deep learning model for content-based image
v
vi Preface
retrieval. It uses K-Means clustering algorithm and Hamming distance for faster
retrievaloftheimage.
Chapter 8 provides a bio-inspired convolutional neural network (CNN)-based
model for COVID-19 diagnosis. A cuckoo search algorithm is used to improve
theperformanceoftheCNNmodel.Chapter9presentsconvolutionalCapsNetfor
detectingCOVID-19diseaseusingchestX-rayimages.Themodelobtainsfastand
accuratediagnosticresultswithlesstrainableparameters.
Chapter 10proposes adeep learning frameworkforanautomated hand gesture
recognition system. The proposed framework classifies the input hand gestures,
each represented by a set of histogram-oriented gradient feature vector into some
predefinednumberofgestureclasses.
Chapter11presentsanewhierarchicaldeeplearningbasedapproachforseman-
tic segmentation of 3D point cloud. It involves nearest neighbor search for local
featureextractionfollowedbyanauxiliarypretrainednetworkforclassification.
Chapter 12 summarizes that the proposed model acts as a better automatic
colorization for colored and grayscale images without human intervention. The
proposed model predicted the color for the new images with good prediction
accuracyclosetotherealimages.Infuture,suchautomaticcolorizationtechniques
helptoidentifyvintageimagesormovieswithgrayscaleimageswiththeirdetails
inaveryclearmanner.
Chapter 13 proposes a generative adversarial network (GAN) for hyperspectral
image classification. It uses dynamic mode decomposition (DMD) to reduce the
redundantfeaturesinordertoattainbetterclassification.Chapter14presentsabrief
introduction about the methodologies used for identifying diabetic retinopathy. It
alsousesconvolutionalneuralnetworkmodelstoachieveaneffectiveclassification
fordiabeticdetectionofretinalfundusimages.
Chapter 15 proposes a modified differential evolution (DE), best neighborhood
DE(BNDE), to solve discrete-valued benchmarking and real-world optimization
problems.Theproposedalgorithmincreasestheexploitationandexplorationcapa-
bilitiesoftheDEandtoreachtheoptimalsolutionfaster.Inaddition,theproposed
algorithmisappliedtograyscaleimageenhancement.
Chapter 16 presents an overview of the main swarm-based solutions proposed
to solve problems related to computer vision. It presents a brief description of
the principles behind swarm algorithms, as well as the basic operations of swarm
methodsthathavebeenappliedincomputervision.
Wearegratefultotheauthorsandreviewersfortheirexcellentcontributionsfor
making this book possible. Our special thanks go to Mary James (EAI/Springer
InnovationsinCommunicationandComputing)fortheopportunitytoorganizethis
editedvolume.
Preface vii
We are grateful to Ms. Eliška Vlcˇková (Managing Editor at EAI – European
AllianceforInnovation)fortheexcellentcollaboration.
We hope the chapters presented will inspire researchers and practitioners from
academiaandindustrytospurfurtheradvancesinthefield.
Coimbatore,TamilNadu,India B.VinothKumar
Coimbatore,TamilNadu,India P.Sivakumar
Puducherry,Karaikal,India B.Surendiran
Denton,TX,USA JunhuaDing
January2023
Contents
ASystematicReviewonMachineLearning-BasedSportsVideo
SummarizationTechniques ..................................................... 1
VaniVasudevanandMohanS.Gounder
ShotBoundaryDetectionfromLectureVideoSequencesUsing
HistogramofOrientedGradientsandRadiometricCorrelation........... 35
T. Veerakumar, Badri Narayan Subudhi, K. Sandeep Kumar,
NikhilO.F.DaRocha,andS.Esakkirajan
DetectionofRoadPotholesUsingComputerVisionandMachine
LearningApproachestoAssisttheVisuallyChallenged..................... 61
U.AkshayaDeviandN.Arulanand
ShapeFeatureExtractionTechniquesforComputerVision
Applications....................................................................... 81
E.Fantin IrudayaRajandM.Balaji
GLCM Feature-Based Texture Image Classification Using
MachineLearningAlgorithms ................................................. 103
R.Anand,T.Shanthi,R.S.Sabeenian,andS.Veni
ProgressinMultimodalAffectiveComputing:FromMachine
LearningtoDeepLearning ..................................................... 127
M.ChanchalandB.VinothKumar
Content-Based Image Retrieval Using Deep Features
andHammingDistance.......................................................... 151
R. T.AkashGunaandO. K.Sikha
BioinspiredCNNApproachforDiagnosingCOVID-19Using
ImagesofChestX-Ray .......................................................... 181
P. Manju Bala, S. Usharani, R. Rajmohan, T. Ananth Kumar,
andA.Balachandar
ix
x Contents
InitialStageIdentificationofCOVID-19UsingCapsuleNetworks ........ 203
ShamikaGanesan,R.Anand,V.Sowmya,andK.P.Soman
DeepLearninginAutoencoderFrameworkandShapePriorfor
HandGestureRecognition...................................................... 223
Badri Narayan Subudhi, T. Veerakumar, Sai Rakshit Harathas,
RohanPrabhudesai,VenkatanareshbabuKuppili,andVinitJakhetiya
Hierarchical-BasedSemanticSegmentationof3DPointCloud
UsingDeepLearning ............................................................ 243
J.Narasimhamurthy,KarthikeyanVaiapury,RamanathanMuthuganapathy,
andBalamuralidharPurushothaman
ConvolutionNeuralNetworkandAuto-encoderHybridScheme
forAutomaticColorizationofGrayscaleImages............................. 253
A.Anitha,P.Shivakumara,ShreyanshJain,andVidhiAgarwal
DeepLearning-BasedOpenSetDomainHyperspectralImage
ClassificationUsingDimension-ReducedSpectralFeatures ................ 273
C. S.Krishnendu,V.Sowmya,andK. P.Soman
An Effective Diabetic Retinopathy Detection Using Hybrid
ConvolutionalNeuralNetworkModels........................................ 295
NiteeshKumar,RashadAhmed,B.H.Venkatesh,M.AnandKumar
ModifiedDiscreteDifferentialEvolutionwithNeighborhood
ApproachforGrayscaleImageEnhancement................................ 307
AnishaRadhakrishnanandG.Jeyakumar
Swarm-BasedMethodsAppliedtoComputerVision........................ 331
María-LuisaPérez-Delgado
Index............................................................................... 357