Digital techniques for heritage presentation and preservation / / edited by Jayanta Mukhopadhyay [and four others]

• Pubblicazione: Cham, Switzerland : , : Springer, , [2021]; ©2021
• Descrizione fisica: 1 online resource (275 pages) : illustrations
• Disciplina: 004
• Soggetto topico: Historic preservation - Technological innovations; Cultural property - Protection - Technological innovations
• Persona (resp. second.): MukhopādhyāẏaJaẏanta
• Nota di contenuto: Intro -- Preface -- Acknowledgments -- Organization -- Contents -- Part I Classification and Retrieval of Heritage Data -- Introduction to Heritages and Heritage Management: A Preview -- 1 Introduction -- 2 Classification of Heritages -- 3 Mapping of World Heritages -- 4 Digital Heritage -- 5 Public, Classified, and Personal Digital Heritage -- 6 Science and Digital Heritages -- 7 Computer-Based Processing of Digital Heritage -- 8 Threats to Digital Heritage -- 9 Motivation for Digital Heritage -- References -- Language-Based Text Categorization: A Survey -- 1 Introduction -- 1.1 Monolingual Text Categorization -- 1.2 Multilingual Text Classification -- 1.2.1 Automatic Language Identification -- 1.2.2 Preprocessing -- 1.2.3 Feature Extraction -- 1.2.4 Learning/Training Phase -- 1.3 Language and Document Models -- 1.4 Applications of Language Identification -- 1.5 Supervised Learning Methods for Text Categorization -- 1.5.1 Naïve Bayes Method -- 1.5.2 K-Nearest Neighbor -- 1.5.3 Support Vector Machine -- 1.5.4 Decision Tree Classifier -- 1.5.5 Neural Networks -- 1.5.6 Performance Measures -- 2 Related Work -- 2.1 Categorization of Monolingual Text Documents -- 2.2 Multilingual Text Categorization -- 2.3 Way Forward -- 3 Conclusion -- References -- Classification of Yoga Asanas from a Single Image by Learning the 3D View of Human Poses -- 1 Introduction -- 2 Related Work -- 2.1 Multiple-View 3D Human Pose Estimation -- 2.2 Single-View 3D Human Pose Estimation -- 3 Proposed Method -- 3.1 3D Pose Estimation -- 3.2 Classification of Yoga Asanas Based on Poses -- 4 Dataset and Experiments -- 5 Results -- 6 Observations/Conclusion -- References -- IHIRD: A Data Set for Indian Heritage Image Retrieval -- 1 Introduction -- 2 Description of the Data Set -- 3 Data Set Preparation -- 4 Development of Content-Based Image Retrieval System. ; 4.1 Selection of Feature Descriptor and Distance Function -- 4.2 Indexing of the Image Database -- 4.3 Ontology-Driven Content-Based Image Search -- 5 Experimental Results -- 6 Conclusion -- References -- Object Spotting in Historical Documents -- 1 Introduction -- 2 Related Work -- 2.1 Word Spotting -- 2.2 Figure Spotting -- 3 Proposed Method -- 3.1 Keypoint Detection -- 3.2 Keypoint Descriptor -- 3.3 Estimation of Laplace-Beltrami Operator -- 3.4 Object Spotting -- 3.4.1 Candidate Keypoint Selection -- 3.4.2 Selection of Essential Keypoints (for Word Spotting) -- 3.4.3 Spotting Zones -- 3.4.4 Ranking of Spotted Zone -- 4 Experimental Results -- 4.1 Datasets and Ground Truth for Word Spotting -- 4.2 Datasets and Ground Truth for Figure Spotting -- 4.3 Determining the Value of σ -- 4.4 Selecting the Value of k -- 4.5 Results and Analysis -- 5 Conclusion -- References -- Part II Restoration and Reconstruction of Digital Heritage Artifacts -- Text Extraction and Restoration of Old Handwritten Documents -- 1 Introduction -- 1.1 Dataset -- 2 Literature Review -- 3 Proposed Method -- 3.1 Training Data Generation -- 3.1.1 Binarized Text Image Generation -- 3.1.2 Restored Foreground Image Generation -- 3.1.3 Restored Background Image Generation -- 3.2 Proposed Method-1 -- 3.2.1 CNN for Text Extraction -- 3.2.2 Post-processing and Document Image Reconstruction -- 3.3 Proposed Method-2 -- 3.3.1 CNN for Foreground Restoration -- 3.3.2 CNN for Background Restoration -- 3.4 Final Image Restoration -- 4 Experimental Results -- 4.1 Implementation Details -- 4.2 Results and Discussion -- 5 Conclusion -- References -- Categorization and Selection of Crowdsourced Images Towards 3D Reconstruction of Heritage Sites -- 1 Introduction -- 2 Categorization and Selection of Crowdsourced Images -- 2.1 Categorization of Crowdsourced Data. ; 2.2 Selection of Images for 3D Reconstruction -- 2.2.1 Parameters for Image Similarity -- 2.2.2 Confidence Score Using DSCR -- 3 Results and Discussions -- 3.1 Categorization of Images -- 3.2 Selection of Images -- 4 Conclusions -- References -- Deep Learning-Based Filtering of Images for 3D Reconstruction of Heritage Sites -- 1 Introduction -- 2 Filtering of Images Towards 3D Reconstruction -- 2.1 Pruning of Images -- 2.2 Selection of Images for 3D Reconstruction -- 3 Results and Discussions -- 4 Conclusions -- References -- Improving Landmark Recognition Using Saliency Detection and Feature Classification -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 Dataset -- 5 Methodology -- 5.1 Graph-Based Visual Saliency (GBVS) -- 5.2 Transfer Learning -- 5.3 Supervised Feature Classification -- 5.3.1 K-Nearest Neighbor Algorithm (KNN) -- 5.3.2 Random Forest Algorithm -- 5.4 Architecture -- 6 Experiments -- 6.1 Implementation Details -- 6.1.1 Parameters -- 7 Performance Analysis -- 8 Conclusion and Future Work -- References -- Part III Applications of Modern Tools in Digital Heritage -- Bharatanatyam Dance Transcription Using Multimedia Ontology and Machine Learning -- 1 Introduction -- 2 Indian Classical Dance: Bharatanatyam and Its Adavus -- 2.1 Sollukattus and Bols: The Music of Adavus -- 2.2 Adavus: The Postures and Movements -- 3 Object-Based Modeling of Adavus -- 3.1 Ontology of Bharatanatyam Adavus: Top Level -- 3.2 Ontology of Sollukattus -- 3.3 Ontology of Adavus -- 3.3.1 Vocabulary of Positions and Formations -- 3.3.2 Ontology of Key Postures -- 3.4 Ontology of Audio-Visual Sync Between Sollukattu and Adavu -- 4 Event-Based Modeling of Adavus -- 4.1 Events of Adavus -- 4.2 Characterization of Audio Events -- 4.3 Characterization of Video Events -- 4.4 Characterization of Synchronization -- 5 Ontology of Events and Streams. ; 6 Representation of Adavus in Labanotation -- 7 Laban Encoding of an Adavu Posture -- 7.1 LabanXML -- 7.2 Tool Overview -- 7.2.1 Posture Recognizer -- 7.2.2 Indexing Laban Descriptor by Posture ID -- 7.2.3 LabanXML Generator -- 7.2.4 Laban Visualizer -- 7.3 Results and Discussion -- 8 Conclusion -- References -- Evolution and Interconnection: Geometry in Early TempleArchitecture -- 1 Introduction -- 2 Background -- 2.1 Indian Temple Architecture -- 2.2 Digital Archetypes -- 3 3D Reconstruction -- 4 Case Studies -- 4.1 Daṣāvatāra Temple at Deogarh, Central India -- 4.2 Hanchey, Kampong Cham, Cambodia -- 4.2.1 Temple B, Hanchey, Cambodia -- 4.2.2 Kuk Preah Thiet, Hanchey, Cambodia -- 4.3 Temples in the Dieng Plateau, Central Java -- 4.3.1 Candi Gatotkaca, Dieng Plateau, Central Java -- 4.3.2 Candi Arjuna, Dieng Plateau, Central Java -- 4.4 Temple 1, Roda, Sabarkantha, Gujarat -- 5 Spatio-temporal Mapping -- 6 Discussion -- References -- Computer Vision for Capturing Flora -- 1 Introduction -- 1.1 Motivation -- 1.2 Related Works -- 1.3 Existing Datasets: Problems and a Solution -- 2 Indic-Leaf Dataset -- 2.1 Images -- 2.2 Image Tags -- 2.3 Challenges in Data Creation -- 2.4 Other Datasets -- 3 Computer Vision for Plant Recognition -- 3.1 Methods -- 3.1.1 VGG-16 -- 3.1.2 ResNet -- 3.1.3 Grad-CAM -- 3.2 Experiments, Results, and Discussion -- 3.2.1 Data Augmentation -- 3.2.2 Experimental Setup -- 3.2.3 Results -- 3.3 Discussion -- 4 Applications -- 4.1 Web Application: Community Collaborative Approach -- 4.2 Mobile Adaptation -- 4.2.1 Implementation -- 4.2.2 Quantization -- 5 Conclusion -- References.
• Titolo autorizzato: Digital techniques for heritage presentation and preservation
• ISBN: 3-030-57907-7
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910484920203321
• Lo trovi qui: Univ. Federico II