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ویرایش: [13052, 1 ed.] نویسندگان: Nicolas Tsapatsoulis, Andreas Panayides, Theo Theocharides, Andreas Lanitis, Constantinos Pattichis, Mario Vento سری: Lecture Notes in Computer Science ISBN (شابک) : 3030891275, 9783030891275 ناشر: Springer سال نشر: 2021 تعداد صفحات: 524 [516] زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 69 Mb
در صورت تبدیل فایل کتاب Computer Analysis of Images and Patterns: 19th International Conference, CAIP 2021, Virtual Event, September 28–30, 2021, Proceedings, Part I به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب تجزیه و تحلیل کامپیوتری تصاویر و الگوها: نوزدهمین کنفرانس بین المللی، CAIP 2021، رویداد مجازی، 28 تا 30 سپتامبر 2021، مجموعه مقالات، قسمت اول نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
The two volume set LNCS 13052 and 13053 constitutes the refereed proceedings of the 19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021, held virtually, in September 2021. The 87 papers presented were carefully reviewed and selected from 129 submissions. The papers are organized in the following topical sections across the 2 volumes: 3D vision, biomedical image and pattern analysis; machine learning; feature extractions; object recognition; face and gesture, guess the age contest, biometrics, cryptography and security; and segmentation and image restoration.
Preface Organization Contents – Part I Contents – Part II 3D Vision Simultaneous Bi-directional Structured Light Encoding for Practical Uncalibrated Profilometry 1 Introduction 2 Related Work 3 Mathematical Investigation 3.1 Background: Sinusoidal Phase Shifting Method 3.2 Amplitude of Superposition 3.3 Combined Patterns 3.4 Mathematical Solution to the Problem 4 Application to Real World 4.1 Swapping Step 5 Evaluation 6 Conclusions References Joint Global ICP for Improved Automatic Alignment of Full Turn Object Scans 1 Introduction 2 Related Work 3 Background: Rigid Point Cloud Alignment 3.1 Orthogonal Procrustes Problem 3.2 Iterative Closest Point (ICP) 3.3 Full Turn Registration: Pulli's Approach 4 Joint Rigid Point Cloud Alignment 5 Outlier Rejection 6 Evaluation 6.1 Stopping Criterion 7 Conclusion References Fast Projector-Driven Structured Light Matching in Sub-pixel Accuracy Using Bilinear Interpolation Assumption 1 Introduction 2 Related Work 3 Fast Projector Driven Matching (FPDM) 3.1 Matching Integer Pixel Quads 3.2 Topological Consistency Check (TCC) 4 Bilinear Sub-pixel Matching 4.1 Sub-pixel Position in Unit Patch 4.2 Mapping to Convex Quad 5 Results 6 Conclusions References Pyramidal Layered Scene Inference with Image Outpainting for Monocular View Synthesis 1 Introduction 2 Proposed Method 2.1 Outpainting 2.2 Pyramidal Network Architecture 3 Results 4 Conclusions References Out of the Box: Embodied Navigation in the Real World 1 Introduction 2 Related Work 3 Real-World Navigation with Habitat 3.1 Baseline Architecture 3.2 Training in Simulation 3.3 LoCoNav: Adapting for Real World 4 Experiments 5 Conclusion References Toward a Novel LSB-based Collusion-Secure Fingerprinting Schema for 3D Video 1 Introduction 2 Related Work 2.1 Overview on the Existing 3D Video Watermarking Techniques 2.2 The Tracing Traitor: A Brief Review 3 The General Tracing System 3.1 The Copyright Registration Step 3.2 The Collusion Attacks 3.3 The Copyright Identification Step 4 The Proposed Traitor Tracing Framework 4.1 The Proposed Copyright Registration Step 4.2 The Proposed Copyright Identification Scheme 5 Experimental Results 5.1 The Watermarking Results 5.2 The Tracing Results 6 Conclusions and Future Work References A Combinatorial Coordinate System for the Vertices in the Octagonal C4C8(R) Grid 1 Introduction 2 Related Work 2.1 The 2-Valued Labelling by Ashrafi and Loghman ch7AshrafiLsps2008 2.2 The 4-Valued Coordinate System by Taghizadeh et al. ch7TaghizadehAGsps2012 2.3 The 2-Valued Coordinate System by Taghizadeh et al. ch7TaghizadehAGsps2012 2.4 The 3-Valued Coordinate System by Siddiqui et al. ch7SiddiquiNRIsps2016 and Naeem et al. ch7NaeemSGGsps2018 2.5 The 3-Valued Coordinate System by Heydari and Taeri ch7HeydariTspsRsps2007 3 The Combinatorial Coordinate System 3.1 Definition 3.2 Connection with the Cartesian Coordinates 3.3 Neighbors 4 Conversion to/from Existing Coordinate Systems 4.1 The 2-Valued Labelling by Ashrafi and Loghman ch7AshrafiLsps2008 4.2 The 4-Valued Coordinate System by Taghizadeh et al. ch7TaghizadehAGsps2012 4.3 The 3-Valued Coordinate System by Siddiqui et al. ch7SiddiquiNRIsps2016 and Naeem et al. ch7NaeemSGGsps2018 4.4 The 3-Valued Coordinate System by Heydari and Taeri ch7HeydariTspsRsps2007 5 Discussion References Bilingual Speech Recognition by Estimating Speaker Geometry from Video Data 1 Introduction 2 3D Speaker Geometry Estimation 3 Methodology 3.1 Object Detection 3.2 Speech Recognition System 4 Results 5 Conclusions and Future Work References Cost-Efficient Color Correction Approach on Uncontrolled Lighting Conditions 1 Introduction 2 Previous Work 3 Methodology 3.1 Data 3.2 Color Correction 4 Results 5 Conclusions References HPA-Net: Hierarchical and Parallel Aggregation Network for Context Learning in Stereo Matching 1 Introduction 2 Related Work 2.1 Deep Neural Networks for Stereo Matching 2.2 Multi-scale Information 3 Proposed Method 3.1 Network Architecture 3.2 Hierarchical Aggregation (HA) Network 3.3 Parallel Aggregation (PA) Network 3.4 Output Module and Loss Function 4 Experimental Results 4.1 Datasets and Evaluation Metrics 4.2 Implementation Details 4.3 Ablation Studies 4.4 KITTI Datasets Results 5 Conclusion References MTStereo 2.0: Accurate Stereo Depth Estimation via Max-Tree Matching 1 Introduction 2 MTStereo 2.0 2.1 Steps Performed by MTStereo 2.0 3 Experiments 3.1 Evaluation 3.2 Results and Comparison 4 Conclusions References Biomedical Image and Pattern Analysis H-OCS: A Hybrid Optic Cup Segmentation of Retinal Images 1 Introduction 2 Proposed Method 2.1 Region of Interest Extraction 2.2 Network Architecture 2.3 Loss Function 2.4 Transfer Learning 2.5 Postprocessing 3 Experimental Analysis 3.1 Datasets 3.2 Implementation Details 3.3 Effectiveness of TL and IA 3.4 Effectiveness of Loss Functions 3.5 Comparison with Other Approaches 4 Conclusion References Retinal Vessel Segmentation Using Blending-Based Conditional Generative Adversarial Networks 1 Introduction 2 Proposed Method 2.1 Datasets 2.2 Blending and Enhancement-Based Strategy 2.3 GAN Synthesization 2.4 CNN-Based Segmentation 3 Experimental Evaluation 3.1 Qualitative Result 3.2 Quantitative Result 4 Conclusion References U-Shaped Densely Connected Convolutions for Left Ventricle Segmentation from CMR Images 1 Introduction 2 Related Works 3 Proposed Method 3.1 Dataset 3.2 Preprocessing 3.3 Architecture 3.4 Post-processing 3.5 Evaluation Metrics 4 Experiments and Results 5 Conclusion References Deep Learning Approaches for Head and Operculum Segmentation in Zebrafish Microscopy Images 1 Introduction 2 Methodology 2.1 Image Acquisition and Dataset Description 2.2 Two Deep Learning Strategies 2.3 U-Net Implementation 2.4 Experimental Protocol 2.5 Model Training with Different Loss Functions 3 Results and Discussion 3.1 Robustness to Image Acquisition with Another Microscope 4 Conclusions References Shape Analysis Approach Towards Assessment of Cleft Lip Repair Outcome 1 Introduction 2 Method 2.1 Dataset and Tools 2.2 Feature Description and Detection 2.3 Symmetrical Axis Detection and Measurement 2.4 Conversion of Similarity Measure to a Numeric Score 3 Experimental Results 3.1 Image Segmentation 3.2 Evaluation of Aesthetic Assessment 4 Conclusion References MMEC: Multi-Modal Ensemble Classifier for Protein Secondary Structure Prediction 1 Introduction 2 Methodology 2.1 Convolutional Neural Networks 2.2 BERT 2.3 Inception Recurrent Networks 2.4 Multi-Modal Ensemble Classifier 2.5 Genetic Algorithm 3 Datasets and Evaluation Metric 3.1 Datasets 3.2 Evaluation Metric 4 Experimental Evaluation 4.1 CB6133 4.2 CB513 5 Conclusions and Future Work References Patch-Level Nuclear Pleomorphism Scoring Using Convolutional Neural Networks 1 Introduction 1.1 Related Work 2 Data and Materials 3 Methods 3.1 Model Training 4 Experimental Set-Up 5 Results 5.1 Validation Results 5.2 Test Results 6 Discussion 7 Conclusion References Automatic Myelofibrosis Grading from Silver-Stained Images 1 Introduction 2 Related Work and Open Issues 3 Materials and Methods 3.1 Data Set 3.2 Image Classification 4 Experimental Evaluation 4.1 Experimental Set-Up 4.2 Results 5 Conclusions References A Deep Learning-Based Pipeline for Celiac Disease Diagnosis Using Histopathological Images 1 Introduction 2 Materials and Methods 2.1 Data 2.2 Methodology 3 Results 4 Discussion 5 Conclusion References HEp-2 Cell Image Recognition with Transferable Cross-Dataset Synthetic Samples 1 Introduction 2 Related Work 3 Datasets 4 Proposed Method 5 Evaluation and Discussion 6 Conclusion References Clinically Guided Trainable Soft Attention for Early Detection of Oral Cancer 1 Introduction 2 Related Work 3 Materials 4 Method 4.1 Attention Network 4.2 Guided Attention 4.3 Technical Details 5 Results 6 Discussion and Conclusion References Small and Large Bile Ducts Intrahepatic Cholangiocarcinoma Classification: A Preliminary Feature-Based Study 1 Introduction 2 Proposed Classification Algorithm 2.1 Tumor Segmentation 2.2 Feature Extraction 2.3 Feature Selection 3 Performance Evaluation 3.1 Patients Dataset 3.2 Classification Procedure and Results 4 Conclusions References A Review on Breast Cancer Brain Metastasis: Automated MRI Image Analysis for the Prediction of Primary Cancer Using Radiomics 1 Introduction 2 Literature Review 2.1 Image Preprocessing and Brain Metastasis Segmentation 2.2 Prediction of Brain Metastasis: Breast Cancer Origin 3 Discussion References An Adaptive Semi-automated Integrated System for Multiple Sclerosis Lesion Segmentation in Longitudinal MRI Scans Based on a Convolutional Neural Network 1 Introduction 2 Materials and Methods 2.1 Acquisition of Brain MRI Images 2.2 Image Pre-processing 2.3 CNN Model Architecture (U-Net) 2.4 Image Post-processing 2.5 Proposed CNN System 2.6 Software Implementation 2.7 Evaluation Metrics 3 Experimental Results 3.1 Manual and Semi-automated Lesions Segmentation on Brain MRI Images 3.2 Evaluation of the Proposed System 4 Discussion 5 Concluding Remarks References A Three-Dimensional Reconstruction Integrated System for Brain Multiple Sclerosis Lesions 1 Introduction 2 Material and Methods 2.1 Acquisition of Brain MRI Images 2.2 MRI MS Lesion Preprocessing and Semi-automated Segmentation 2.3 Image Normalization and Contour Lesion Generation 2.4 3D Lesion Volume Estimation 2.5 3D Reconstruction 2.6 Evaluation Metrics 3 Results 4 Discussion 5 Conclusions and Future Trends References Rule Extraction in the Assessment of Brain MRI Lesions in Multiple Sclerosis: Preliminary Findings 1 Introduction 2 Materials and Methods 2.1 Study Group and MRI Acquisition 2.2 Intensity Normalization and Manual Lesion Delineation 2.3 Texture Feature Extraction 2.4 Classification Analysis 2.5 Rule Extraction 3 Results 4 Discussion 5 Conclusion References Invariant Moments, Textural and Deep Features for Diagnostic MR and CT Image Retrieval 1 Introduction 2 Related Works 3 Our Image Retrieval System 3.1 Feature Extraction 3.2 Feature Selection 3.3 Image Ranking 4 Experimental Evaluation 4.1 Data Sets 4.2 Experimental Setup 5 Results 6 Conclusions References Toward Multiwavelet Haar-Schauder Entropy for Biomedical Signal Reconstruction 1 Introduction 2 HSCH Multiwavelet Modeling and Associated Entropy 3 Experimentations 3.1 Biosignal Reconstruction 3.2 Multiwavelet Entropy 4 Conclusion References Machine Learning Handling Missing Observations with an RNN-based Prediction-Update Cycle 1 Introduction and Related Work 2 RNN-based Prediction-Update-Cycle 3 Data Generation and Evaluation 4 Conclusion References eGAN: Unsupervised Approach to Class Imbalance Using Transfer Learning 1 Introduction 2 Related Work 3 Methodology and Experimental Design 3.1 Addressing Class Imbalance with eGAN 3.2 Selection of Pre-trained Image Classification Weights 3.3 Dataset 4 Results and Discussion 4.1 Imbalance Ratios 5 Conclusion References Progressive Contextual Excitation for Smart Farming Application 1 Introduction 2 Related Work 3 Method 3.1 Feature Extraction 3.2 Progressive Contextual Excitation 4 Experiments 4.1 Dataset 4.2 Implementation Details 4.3 Comparison with Other Approaches 4.4 Visualization 5 Conclusion References Fine-Grained Image Classification for Pollen Grain Microscope Images 1 Introduction 2 Related Works 3 Method and Materials 3.1 CutOcclusion: Training Data Augmentation 3.2 Pipeline 3.3 Test Time Augmentation. 4 Dataset 5 Experiments 6 Results and Discussion 7 Conclusions 8 Future Works References Adaptive Style Transfer Using SISR 1 Introduction 2 Related Work 3 Proposed Scheme 3.1 SISR Network 3.2 Style Transfer Network 3.3 Loss Function 4 Experiments 4.1 Experimental Setup 4.2 Results 4.3 Conclusion References Object-Centric Anomaly Detection Using Memory Augmentation 1 Introduction 2 Related Work 2.1 Reconstruction, Prediction and Hybrid Approaches 2.2 Memory Augmentation 2.3 Object-Centric Anomaly Detection 3 ObjMemAE Method 3.1 Object Detection 3.2 Autoencoder: Encoder - Memory Module - Decoder 3.3 Anomaly Score 4 Experiments 4.1 Dataset 4.2 Evaluation Metric 4.3 Implementation Details 5 Results and Discussion 5.1 Quantitative Results 5.2 Ablation Study 5.3 Qualitative Results 6 Conclusion References Document Language Classification: Hierarchical Model with Deep Learning Approach 1 Introduction 1.1 Literature Review 2 Proposed Approach 2.1 Preprocessing 2.2 Convolutional Neural Network Model 3 Experimental Results 3.1 Dataset 3.2 Performance of the Proposed Approach 4 Conclusion References Parsing Digitized Vietnamese Paper Documents 1 Introduction 2 Related Work 2.1 Existing Datasets 2.2 Parsing Digitized Paper Documents Problem 3 UIT-DODV Dataset 3.1 Dataset Collection 3.2 Category Selection 3.3 Dataset Description 4 Computational Model 4.1 Object Detector 4.2 Loss Function 5 Experimental Results and Discussion 5.1 Experimental Setting 5.2 Analysis Results 6 Conclusion and Future Work References EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task 1 Introduction 1.1 Related Work 2 Methodology 2.1 Network Architecture 2.2 Loss Functions 3 Experiments 3.1 Results 3.2 Visualization Analysis 4 Conclusions References When Deep Learners Change Their Mind: Learning Dynamics for Active Learning 1 Introduction 2 Related Work 3 Active Learning for Image Classification 3.1 Label-Dispersion Acquisition Function 3.2 Informativeness Analysis 4 Experimental Results 4.1 Experimental Setup 4.2 Results 5 Conclusion References Learning to Navigate in the Gaussian Mixture Surface 1 Introduction 2 Related Works 3 Gaussian Mixture Models 4 Gaussian Mixture Centers Loss 4.1 Scheme 5 Experiments 5.1 Datasets and Implementation Details 5.2 Ablation Experiments 5.3 Visualization Analysis 6 Conclusions References A Deep Hybrid Approach for Hate Speech Analysis 1 Introduction 2 Materials and Methods 2.1 Dataset Description and Construction 2.2 Proposed Model Architecture and Experimental Setup 3 Results and Discussion 4 Conclusion References On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator 1 Introduction 2 Related Works 3 Methods 3.1 Overview 3.2 CORF Operator with Push-Pull Inhibition 3.3 AlexNet 3.4 Image Perturbations 4 Experiments and Results 4.1 Data Set 4.2 Experiments 5 Discussion 6 Conclusion References Fast Hand Detection in Collaborative Learning Environments 1 Introduction 2 Methodology 2.1 Hand Detection Method 2.2 Optimal Data Augmentation 3 Results 3.1 Results for Optimal Data Augmentation 3.2 Hand Detection Results 4 Conclusion References Assessing the Role of Boundary-Level Objectives in Indoor Semantic Segmentation 1 Introduction 2 Related Work 3 Method 3.1 Boundary Loss 3.2 Active Boundary Loss 4 Experiments 4.1 Dataset 4.2 Implementation Details and Evaluation Protocol 4.3 Quantitative Evaluation 5 Conclusion References Skin Lesion Classification Using Convolutional Neural Networks Based on Multi-Features Extraction 1 Introduction 2 Related Works 3 Methodology and Materials 3.1 PH2 Dataset 3.2 Convolutional Neural Networks 3.3 Classifiers 3.4 Evaluation Metrics 4 Experimental Results and Discussion 5 Conclusion References Recursively Refined R-CNN: Instance Segmentation with Self-RoI Rebalancing 1 Introduction 2 Related Works 3 Recursively Refined R-CNN 4 Experiments 4.1 Dataset and Evaluation Metrics 4.2 Implementation Details 4.3 Analysis of R3-CNN 4.4 Ablation Study on the Evaluation Phase 4.5 Ablation Study on the Training Phase 4.6 Extensions on R3-CNN 5 Conclusions References Layer-Wise Relevance Propagation Based Sample Condensation for Kernel Machines 1 Introduction 2 Kernel Machines 3 Sample Condensation Method 3.1 LRP for Relevance Measure of Kernel Machine 3.2 Relevance-Based Sample Condensation 4 Experimental Validation 4.1 Data Sets 4.2 Results 5 Conclusion References Author Index