Recent trends in image processing and pattern recognition : 4th International Conference, RTIP2R 2021, Msida, Malta, December 8-10, 2021, revised selected papers / / edited by K. C. Santosh, Ravindra Hegadi, Umapada Pal

• Pubblicazione: Cham, Switzerland : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (406 pages)
• Disciplina: 621.367
• Soggetto topico: Image processing
• Persona (resp. second.): HegadiRavindra; PalUmapada; SantoshK. C.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Healthcare: Medical Imaging and Informatics -- Cleaning Highly Unbalanced Multisource Image Dataset for Quality Control in Cervical Precancer Screening -- 1 Introduction -- 2 Image Data -- 3 Method -- 4 Experiments and Discussions -- 5 Conclusion and Future Work -- References -- Detection of Male Fertility Using AI-Driven Tools -- 1 Introduction -- 2 Related Works -- 3 Methods -- 3.1 Features Detailing -- 3.2 Feature Selection -- 3.3 Feature Selection -- 3.4 The Workflow of Proposed Model -- 4 Experiments -- 4.1 Data -- 4.2 Evaluation Protocols -- 4.3 Results -- 5 Conclusion -- References -- An Empirical Study of Vision Transformers for Cervical Precancer Detection -- 1 Introduction -- 2 Related Work -- 3 Experiments -- 3.1 Data Preparation -- 3.2 Architecture -- 3.3 Training Setup -- 4 Results and Discussions -- 5 Conclusion and Future Work -- References -- CheXNet for the Evidence of Covid-19 Using 2.3K Positive Chest X-rays -- 1 Introduction -- 2 Chest X-ray Dataset -- 3 CheXNet and Implementation -- 4 Experimental Results -- 4.1 Evaluation Protocol and Performance Metrics -- 4.2 Results Analysis -- 4.3 Comparative Study -- 5 Conclusion and Future Work -- References -- An Enhanced Deep Convolution Neural Network Model to Diagnose Alzheimer's Disease Using Brain Magnetic Resonance Imaging -- 1 Introduction -- 2 Proposed Methodology -- 2.1 Data Pre-processing -- 2.2 Deep CNN: Architecture and Implementation -- 2.3 Hyper Parameters in Deep CNN -- 3 Experiments -- 3.1 Dataset -- 3.2 Validation Protocol and Evaluation Metrics -- 3.3 Results and Discussion -- 4 Conclusion -- References -- Automatic Knee Osteoarthritis Stages Identification -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Acquisition -- 3.2 Left and Right Knee Segmentation -- 3.3 ROI Detection. ; 3.4 DNN-Based Classification System -- 4 Experimental Setup and Result Discussion -- 4.1 Comparison of OACnet with Other DNN-Based Models -- 5 Conclusion -- References -- Stacked Dark COVID-Net: A Multi-class Multi-label Classification Approach for Diagnosing COVID-19 Using Chest X-Ray Images -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Image Processing -- 3.2 Layered DarkNet (LDN) -- 3.3 Novel Stacked Dark COVID-Net -- 4 Experimental Setup and Discussions -- 4.1 Experimental Setup -- 4.2 Dataset Description -- 5 Conclusions -- References -- Image Augmentation for Improving Automated Eligibility-Classification for Cervical Precancer Ablation Treatment -- 1 Introduction -- 2 Methods -- 2.1 Dataset and Annotation -- 2.2 CycleGan -- 2.3 Customized CNN -- 3 Experiments -- 3.1 Augmented Images -- 3.2 Treatability Classification Performance -- 3.3 Comparing the Visualization -- 4 Conclusion -- References -- Osteoarthritis Detection Using Densely Connected Neural Network -- 1 Introduction -- 2 Related Work -- 3 Architecture of Dense Net -- 4 Design of OA Detection and Classification Model -- 4.1 Input Image Acquisition -- 4.2 Pre-processing -- 4.3 Feature Extraction -- 4.4 Image Classification -- 5 Results and Discussion -- 6 Conclusion -- References -- Generic Foreign Object Detection in Chest X-rays -- 1 Introduction -- 2 Related Works -- 3 YOLOv4, Architecture and Implementation -- 4 Experiments -- 4.1 Dataset -- 4.2 Results and Analysis -- 5 Conclusions -- References -- Mammogram Mass Classification: A CNN-Based Technique Applied to Different Age Groups -- 1 Contribution Outline -- 1.1 Traditional Classifiers -- 1.2 CNN Architecture -- 2 Experiments -- 2.1 Dataset and Pre-processing -- 2.2 Age Grouping -- 2.3 Evaluation Metrics -- 2.4 Evaluation Strategy and System Configuration -- 2.5 Results and Analysis. ; 2.6 Comparative Analysis with Previous Relevant Work -- 3 Conclusions and Future Work -- References -- Computer Vision and Pattern Recognition -- Complex Object Detection Using Light-Field Plenoptic Camera -- 1 Introduction -- 2 Plenoptic Camera Calibration -- 3 Methods -- 3.1 Feature Detection -- 3.2 Feature Matching -- 3.3 Transform Model Estimation -- 3.4 Results -- 3.5 Conclusions -- References -- Real-Time Face Recognition for Organisational Attendance Systems -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 4 Experimental Results and Discussion -- 5 Conclusion -- References -- Harnessing Sustainable Development in Image Recognition Through No-Code AI Applications: A Comparative Analysis -- 1 Introduction -- 2 Literature Review -- 2.1 Previous Research -- 3 Methodology -- 3.1 Dataset -- 3.2 Hardware -- 3.3 Software -- 3.4 ResNet - 50 V2 -- 3.5 MobileNetV2 -- 4 Results -- 5 Discussion -- References -- Evaluating Performance of Adam Optimization by Proposing Energy Index -- 1 Introduction -- 2 Related Work -- 2.1 AdaGrad -- 2.2 AdaDelta -- 2.3 RMSProp -- 2.4 Adam -- 2.5 Energy Index Based Optimization Method (EIOM) -- 3 Introducing EI to Adam -- 4 Experiments and Analysis -- 4.1 Logistic Regression -- 4.2 Support Vector Machine -- 5 Conclusion and Future Direction -- References -- An Alignment-Free Fingerprint Template Protection Technique Based on Minutiae Triplets -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Technique -- 3.1 Non-invertible Feature Extraction -- 3.2 Secured Template Generation -- 3.3 Security Enhancement of the Generated Template -- 3.4 Matching -- 4 Experimental Analysis -- 4.1 Revocability -- 4.2 Diversity -- 4.3 Security -- 4.4 Performance -- 5 Conclusion -- References -- Early Prediction of Complex Business Processes Using Association Rule Based Mining -- 1 Introduction -- 2 Related Work. ; 3 Association Rule Mining -- 4 The CBA-Based Prediction Model -- 5 Experimental Results -- 5.1 Comparative Analysis -- 6 Conclusion -- References -- A Framework for Masked-Image Recognition System in COVID-19 Era -- 1 Introduction -- 1.1 Objectives -- 2 Literature Review -- 3 Problem Formulation -- 4 Proposed Solutions -- 4.1 Programming Languages and Tools -- 4.2 Web Application Frameworks -- 4.3 Computer Vision Frameworks -- 4.4 Algorithms -- 4.5 Approaches -- 5 Specification and Design -- 5.1 System Requirements -- 5.2 Principle System Components and Requirements -- 6 Implementations and Result Analysis -- 6.1 Home Component -- 6.2 Camera Component -- 6.3 Login Component -- 6.4 Profile Component -- 6.5 Results Analysis -- 7 Conclusions and Future Work -- References -- A Deep-Learning Based Automated COVID-19 Physical Distance Measurement System Using Surveillance Video -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Human Detection -- 3.2 Human Tracking -- 3.3 Distance Measurement -- 4 Experimental Results and Discussions -- 4.1 Dataset and Training Details -- 4.2 Results and Discussions -- 5 Conclusion -- References -- Face Mask Detection Using Deep Hybrid Network Architectures -- 1 Introduction and Problem Understanding -- 2 Related Works -- 3 Dataset and Data Preprocessing -- 3.1 Data Description -- 3.2 Resizing -- 4 Description of the Network Architectures and Machine Learning Algorithms -- 4.1 ResNet50 -- 4.2 VGG16 -- 4.3 Support Vector Machines -- 4.4 GBDecision Trees -- 4.5 Neural Networks -- 4.6 Hybrid Models -- 5 Proposed Model: ResNet50 + SVM -- 6 Results and Discussion -- 7 Conclusion -- References -- A Super Feature Transform for Small-Size Image Forgery Detection -- 1 Introduction -- 2 Related Works and Problem Context -- 2.1 Preliminaries of Super Resolution Algorithms -- 2.2 Review of Forgery Detection Algorithms. ; 3 Proposed Detection Model -- 3.1 Super Feature Transform (SFT) -- 3.2 Feature Filtering and Forged Region Localization -- 4 Experimentation and Analysis -- 4.1 Evaluation of Super Feature Transform -- 4.2 Detection Results with Post-processing Actions -- 5 Conclusion -- References -- Document Analysis and Recognition -- UHTelHwCC: A Dataset for Telugu Off-line Handwritten Character Recognition -- 1 Introduction -- 2 Related Work -- 3 UHTelHwCC Creation -- 4 UHTelHwCC Characteristics -- 4.1 Sample Distribution -- 4.2 Data Splits -- 4.3 Writers Details -- 5 Experiments -- 6 Conclusion -- References -- Inflectional and Derivational Hybrid Stemmer for Sentiment Analysis: A Case Study with Marathi Tweets -- 1 Introduction -- 2 Related Work -- 3 Marathi Morphology -- 4 Marathi Stemming Algorithm -- 4.1 Data Collection -- 4.2 Cleaning -- 4.3 Stop Word Removal -- 4.4 Marathi Subjective Lexicon (Adjective-Adverb) -- 4.5 Stemming -- 5 Experiments and Results -- 5.1 Performance of the Stemmer -- 5.2 Tweet Classification and Stemmer Validation -- 5.3 Comparison with Existing Marathi Stemmers -- 6 Conclusion -- References -- Adaptive Threshold-Based Database Preparation Method for Handwritten Image Classification -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Deep Neural Network -- 3.2 Feature Extraction with Adaptive Threshold -- 3.3 Classification and Recognition -- 4 Experimental Setup and Evaluation Protocol -- 4.1 Dataset and Pre-processing -- 4.2 Evolution Protocol -- 5 Conclusion and Future Scope -- References -- A Graph-Based Holistic Recognition of Handwritten Devanagari Words: An Approach Based on Spectral Graph Embedding -- 1 Introduction -- 2 Proposed Model -- 2.1 Pre-processing -- 2.2 Proposed Graph Extraction/Representation -- 2.3 Edge Connecting, Labelling and Selection of Graph Associated Matrices. ; 2.4 Spectral Decomposition of Word Graph Associated Matrices.
• Titolo autorizzato: Recent Trends in Image Processing and Pattern Recognition
• ISBN: 3-031-07005-4
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996475763803316
• Lo trovi qui: Univ. di Salerno
• Serie: Communications in Computer and Information Science Ser.