Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Name: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications | 26th Iberoamerican Congress, CIARP 2023, Coimbra, Portugal, November 27-30, 2023, Proceedings, Part I
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26th Iberoamerican Congress, CIARP 2023, Coimbra, Portugal, November 27-30, 2023, Proceedings, Part I

Verónica Vasconcelos Inês Domingues Simão Paredes(Herausgeber*in)

Springer (Verlag)

Erschienen am 26. November 2023

XVII, 747 Seiten

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Inhalt

Intro
Preface
Organization
Contents - Part I
Contents - Part II
Deblur Capsule Networks
1 Introduction
2 Related Works
3 Deblur Capsule Networks
3.1 Blur Type Classification
3.2 Point Spread Function Reconstruction
3.3 Image Deep Regularized Deconvolution
4 Experiments and Analysis
4.1 DbCN Optimization Procedure
4.2 Synthetic Camera Motion Blur
4.3 Synthetic Multi-domain Blur
4.4 Ablation Study
5 Conclusions and Future Works
References
Graph Embedding of Almost Constant Large Graphs
1 Introduction
2 Graphs and Graph Embedding
3 GraphFingerprint: An Embedding for Almost Constant Graphs
3.1 Algorithm Input Parameters
3.2 Local Substructures
3.3 GraphFingerprint Definition
3.4 GraphFingerprint Examples
4 Experimental Section
4.1 From Metal-Oxide Nanocompound to GraphFingerprint
4.2 Toxicity Prediction Based on Global Features
4.3 Toxicity Prediction Based on GraphFingerprints
4.4 Toxicity Prediction Based on Global Features and GraphFingerprints
5 Conclusions
6 Future Work
References
Feature Importance for Clustering
1 Introduction
2 Cluster Analysis
3 Proposed Methods
3.1 Prototype-Based Feature Importance
3.2 SHAP-Based Feature Importance
4 Experimental Simulations
5 Concluding Remarks
References
Uncovering Manipulated Files Using Mathematical Natural Laws
1 Introduction
2 Related Work
3 Benford's Law Fundamentals
3.1 Benford's Law Statement
4 Dataset
5 Benford's Law-Based Method
5.1 Pre-processing
5.2 Processing
5.3 Median Absolute Deviation
5.4 Evaluation Metrics
6 Results
6.1 Analysis of Results
7 Conclusions and Future Work
References
History Based Incremental Singular Value Decomposition for Background Initialization and Foreground Segmentation
1 Introduction
2 Related Work
2.1 Identification of SFOs
2.2 Background Dependency
3 Methodology
3.1 Notation and Preliminaries
3.2 Computation of Incremental SVD
3.3 History Based Incremental SVD (hi-SVD)
4 Experimental Results
4.1 Datasets
4.2 SFO Status Identification Experiment
4.3 Foreground Segmentation Experiment
5 Conclusion
References
Vehicle Re-Identification Based on Unsupervised Domain Adaptation by Incremental Generation of Pseudo-Labels
1 Introduction
2 Related Work
3 Proposed Method
3.1 DBSCAN Pseudo-Labels
3.2 Fine-Tuning
3.3 Unsupervised Domain Adaptation
4 Experimental Validation
4.1 Dataset and Evaluation Environment
4.2 Implementation Details
4.3 Ablation Study for Eps-Neighborhood in DBSCAN
4.4 Ablation Study for Increasing the Number of Cycles in the Generation of Pseudo-Labels
5 Conclusions
References
How to Turn Your Camera into a Perfect Pinhole Model
1 Introduction
2 Methods
2.1 Gaussian Processes
2.2 Constructing an Ideal Pinhole Camera
2.3 The Datasets
3 Results
3.1 Collineation Assumption
3.2 Reprojection Error
3.3 Distortion Removal
4 Discussion
5 Conclusion
A Zhang's Method
B Simplified Zhang's Method
References
Single Image HDR Synthesis with Histogram Learning
1 Introduction
2 Method
2.1 LDR2EDR by Histogram and Resolution difference
2.2 EDR2HDR by Cumulative Histogram Learning
2.3 Fine-Tuning with Reinforcement Learning
3 Experiments
4 Conclusion
References
But That's Not Why: Inference Adjustment by Interactive Prototype Revision
1 Introduction
2 Prototype-Based Learning
3 Interactive Prototype Revision
4 Results
5 Conclusion
References
Teaching Practices Analysis Through Audio Signal Processing
1 Introduction
2 Related Work
3 Dataset Description
4 Classroom Activity Detection
4.1 Training and Testing Data
4.2 Unsupervised Diarization Approach
4.3 Supervised Audio Classification
4.4 Experiments and Results
5 Additional Tools for English Lessons Analysis
5.1 Language Detection
5.2 Key Phrases Matching
5.3 User Interface for Education Technicians
6 Conclusions and Further Work
References
Time Distributed Multiview Representation for Speech Emotion Recognition
1 Introduction
2 Related Work
3 Proposed Strategy
3.1 Database Description
3.2 General Architecture
3.3 Step 1 - Initial Procedures
3.4 Steps 2 and 3 - Algorithms and Combiner
3.5 Steps 4 and 5 - LSTM and Emotion Classification
4 Experimental Results
4.1 Experiment 1 - Results for All RAVDESS Database
4.2 Experiment 2 - Database Divided by Intensity
4.3 Experiment 3 - LOSO Protocol
5 Discussions
6 Conclusion
References
Detection of Covid-19 in Chest X-Ray Images Using Percolation Features and Hermite Polynomial Classification
1 Introduction
2 Materials and Methods
2.1 Image Database
2.2 Methodology
3 Results and Discussion
3.1 Feature Evaluation
3.2 Performance of the HP Classifier
4 Conclusion
References
Abandoned Object Detection Using Persistent Homology
1 Introduction
2 From Background Subtraction to Simplicial Complex
3 Surveillance Points
4 Filtration
5 Persistent Homology and Topological Signature
6 Detecting Abandoned Objects
7 Experimental Results
8 Conclusion and Future Works
References
Interactive Segmentation with Incremental Watershed Cuts
1 Introduction
2 Watershed Cuts
3 Semi-supervised Watershed Cut Algorithm with Interactions
3.1 Tree-Node Marking
3.2 Pixel Labeling
3.3 Incremental Workflow
4 Experiments
4.1 Experiment with User Generated Seeds
4.2 Experiment with Randomly Generated Seeds
5 Conclusion
References
Supervised Learning of Hierarchical Image Segmentation
1 Introduction
2 Ultrametric Dataset
3 Model
4 Evaluation Metrics
5 Experiments
6 Conclusion
References
Unveiling the Influence of Image Super-Resolution on Aerial Scene Classification
1 Introduction
2 Super-Resolution
2.1 Super-Resolution Convolution Neural Network (SRCNN)
2.2 Modified 3D Residual-in-Residual Dense Block (m3DRRDB)
2.3 SwinIR Transformer
3 Scene Classification
4 Experiments
4.1 Datasets
4.2 Experimental Settings
5 Results and Discussion
5.1 Experiment 1: Ranking of Super-Resolution Methods
5.2 Experiment 2: Impact of Super-Resolution on Aerial Scene Classification
6 Conclusion
References
Weeds Classification with Deep Learning: An Investigation Using CNN, Vision Transformers, Pyramid Vision Transformers, and Ensemble Strategy
1 Introduction
2 Related Work
3 Methodology
3.1 Datasets
3.2 Models
3.3 Ensemble
3.4 Evaluation
3.5 Training Configuration
3.6 Execution Environment
4 Results and Discussion
5 Conclusions
References
Leveraging Question Answering for Domain-Agnostic Information Extraction
1 Introduction
2 Related Work
3 Question Answering for Information Extraction
3.1 Models and Questions
3.2 Approach
3.3 Visual Explainability on Evaluation
4 Case Studies
4.1 Application to Toxicology Analysis
4.2 Application to Finance
5 Conclusion
References
Towards a Robust Solution for the Supermarket Shelf Audit Problem: Obsolete Price Tags in Shelves
1 Introduction
2 Dataset Collection
3 Proposed Approach
3.1 Image-to-Text Track
3.2 Localization Track
3.3 Selection Track
3.4 Report Generation
4 Results
5 Conclusions and Future Works
References
A Self-Organizing Map Clustering Approach to Support Territorial Zoning
1 Introduction
2 Related Work
2.1 Zoning with Self-Organizing Maps
2.2 Clustering Ordinal Categorical Data
3 Material and Methods
3.1 The Alto Taquari Basin - MS/MT, Brazil
3.2 Clustering Categorical Ordinal Data
3.3 Clustering Assessing
4 Results and Discussion
5 Conclusions
References
Spatial-Temporal Graph Transformer for Surgical Skill Assessment in Simulation Sessions
1 Introduction
2 Proposed Approach
2.1 Spectral Graph Convolutional Networks
2.2 Transformer Encoder
2.3 Surgical Skill Classifier
3 Experimental Results
3.1 Dataset
3.2 Data Preprocessing
3.3 Results
4 Conclusion
References
Deep Learning in the Identification of Psoriatic Skin Lesions
1 Introduction
2 Background and Literature Review
3 Methodology
3.1 Understanding the Problem
3.2 Deep Learning
3.3 Dataset
3.4 Classification Architectures
3.5 Training
4 Results
4.1 CLAHE
4.2 Type of Input Image
4.3 Data Augmentation
5 Discussion
6 Mobile Application
7 Conclusion
References
WildFruiP: Estimating Fruit Physicochemical Parameters from Images Captured in the Wild
1 Introduction
2 Related Work
2.1 Detection Methods
2.2 Segmentation Methods
2.3 Methods for Estimating Fruit Ripeness in Images
3 Proposed Method
3.1 Fruit Detection and Segmentation
3.2 Image Alignment
3.3 Determination of Physicochemical Parameters
4 Dataset
5 Experiments
5.1 Implementation Details
5.2 Metrics
5.3 Performance of the Proposed Approach
5.4 Impact of Alignment Phase
5.5 Impact of Model Architecture
5.6 Hard Samples
6 Conclusion and Future Work Prospects
References
Depression Detection Using Deep Learning and Natural Language Processing Techniques: A Comparative Study
1 Introduction
2 Related Work
3 Methodology
3.1 Dataset
3.2 Data Pre-processing
3.3 Manual Validation of Label
3.4 Exploratory Data Analysis
3.5 Feature Generation
3.6 Experimental Setup
3.7 Evaluation
4 Results
5 Discussion
6 Conclusion
References
Impact of Synthetic Images on Morphing Attack Detection Using a Siamese Network
1 Introduction
2 Related Work
3 Database
3.1 SYN-MAD-2022
3.2 SOTA Databases
3.3 Metrics
4 Method
5 Experiments and Results
5.1 Exp1 - Train SYN-MAD/TEST SDD-Bechmark
5.2 Exp2 - Train SYN-MAD/TEST SOTA
5.3 Exp3 - Train SOTA/TEST SDD-Benchmark
5.4 Exp4 - Train Mix/TEST SDD-Benchmark
6 Conclusions
References
Face Image Quality Estimation on Presentation Attack Detection
1 Introduction
2 Relatwd Work
2.1 Face PAD
2.2 Face Image Quality Assessment
2.3 Face Image Quality Applied to PAD
2.4 Datasets
3 Method
3.1 Training of PAD Algorithms
4 Experiments and Results
4.1 FIQA Effect on Filtering PAs
4.2 PAD Performance Versus Input Face Quality
4.3 FIQA Filtering of the Training Dataset
5 Conclusions and Future Work
References
Knowledge Distillation of Vision Transformers and Convolutional Networks to Predict Inflammatory Bowel Disease
1 Introduction
2 Methodology and Data
2.1 Dataset
2.2 Experimental Setup
2.3 Pre-processing
2.4 Data Augmentation
2.5 Deep Learning Models
2.6 Knowledge Distillation
2.7 Evaluation
3 Results
4 Discussion
5 Conclusion
References
Analysis and Impact of Training Set Size in Cross-Subject Human Activity Recognition
1 Introduction
2 Related Work
3 Materials and Methods
3.1 Data Collection
3.2 Data Processing
3.3 Model Architecture
3.4 Performance Evaluation
4 Results
5 Discussion
6 Conclusion
References
Efficient Brazilian Sign Language Recognition: A Study on Mobile Devices
1 Introduction
2 Related Works
3 Methodology
3.1 Dataset
3.2 Data Preprocessing
3.3 Proposed Model
3.4 Training Details
3.5 Hardware Specifications
3.6 Experiments Conducted
4 Results
5 Discussion
6 Conclusion
References
Presumably Correct Undersampling
1 Introduction
2 Presumably Correct Decision Sets
3 Presumably Correct Undersampling
4 Experimental Simulations
5 Concluding Remarks
References
Leveraging Longitudinal Data for Cardiomegaly and Change Detection in Chest Radiography
1 Introduction
2 Related Work
3 Methodology
3.1 Dataset
3.2 Experimental Settings
3.3 Rigid CXR Alignment
3.4 Longitudinal Data Augmentation
3.5 Evaluation and Metrics
4 Results and Discussion
4.1 Rigid CXR Alignment
4.2 Pathology and Change Prediction
4.3 Longitudinal Data Augmentation
5 Conclusions
References
Self-supervised Monocular Depth Estimation on Unseen Synthetic Cameras
1 Introduction
2 Related Work
2.1 Self-supervised Monocular Depth Estimation
2.2 Restrictions of Existing Methods
2.3 Approaches to Address Camera Restrictions
3 Method
3.1 Baseline Algorithm
3.2 Baseline Algorithm + Adversarial Training
4 Experiments
4.1 Setup
4.2 Analysis of the Results
5 Conclusion/Discussion
References
Novelty Detection in Human-Machine Interaction Through a Multimodal Approach
1 Introduction
2 Related Work
2.1 Terminology
2.2 Existing Modeling Architectures
3 Methodology
4 Datasets
5 Experiments
5.1 Distance-Based Experiment
5.2 Distribution and Density-Based Experiments
5.3 Non Threshold-Based Models
6 Performance Optimization
6.1 NCM-Based Algorithm
6.2 Distribution and Density-Based Approaches
7 Conclusions
References
Filtering Safe Temporal Motifs in Dynamic Graphs for Dissemination Purposes
1 Introduction
2 Related Work
3 Fundamental Concepts
3.1 Temporal Graph
3.2 Temporal Motifs
3.3 Transitive Reduction
4 Filtering Motifs on Temporal Graphs
5 Filtering Transitive Edges
6 Experiments
6.1 Experimental Setup
6.2 Evaluation and Discussion
7 Conclusion
References
Graph-Based Feature Learning from Image Markers
1 Introduction
2 Related Works
3 Graph-Based Feature Learning from Image Markers
3.1 Superpixel Graphs
3.2 Kernel Computation
3.3 User-Guided Decoder for Object Detection
4 Experiments
4.1 Architecture Parameters
4.2 Bounding Boxes Computation
4.3 Evaluation Measures
4.4 Results
5 Conclusion
References
Seabream Freshness Classification Using Vision Transformers
1 Introduction
2 State-of-the-Art Review
2.1 Traditional Systems
2.2 Deep Learning Based Systems
2.3 Discussion
3 Seabream Freshness Dataset
3.1 Image Acquisition
3.2 Equipment
3.3 Storage
4 Proposed Fish-Freshness Classification System
4.1 Image Preprocessing and Segmentation
4.2 Image Augmentation
4.3 Feature Extraction and Classification
5 Results and Discussion
5.1 Segmentation Model
5.2 Feature Extraction and Classification Model
6 Conclusions and Future Work
References
Explaining Semantic Text Similarity in Knowledge Graphs
1 Introduction
2 Related Work
3 Preliminary Definitions
3.1 Knowledge Graphs
3.2 Saliency Scores
3.3 Integrated Gradients
3.4 Explanations
4 Methodology
5 Experiments and Results
5.1 Experiment 1. Discriminating Power of the STS Metrics
5.2 Experiment 2. Linking Children to Parents
5.3 Explanations for the Concept Linking Task
6 Conclusions
References
Active Supervision: Human in the Loop
1 Introduction
2 Related Work
3 Proposal
4 Experiments
5 Discussion
6 Conclusion
References
Condition Invariance for Autonomous Driving by Adversarial Learning
1 Introduction
2 Related Work
2.1 Object Detection
2.2 Domain Adaptation and Feature Invariance
2.3 Domain Adaptation in Autonomous Driving
3 Proposal
4 Implementation
4.1 Data
4.2 Model
4.3 Training and Testing
4.4 Performance Metrics
5 Results and Discussion
6 Conclusion
References
YOLOMM - You Only Look Once for Multi-modal Multi-tasking
1 Introduction
2 State of the Art
2.1 Model Selection
2.2 Dataset Selection
3 Implementation
3.1 Multi-modality
3.2 Lidar Segmentation
3.3 Training Process
4 Experiments and Results
5 Conclusion
References
Classify NIR Iris Images Under Alcohol/Drugs/Sleepiness Conditions Using a Siamese Network
1 Introduction
1.1 Related Work
1.2 Method
1.3 Metrics
1.4 Database
1.5 Experiment and Results
2 Comparison with SOTA
3 Visualisation
4 Conclusion
References
Bipartite Graph Coarsening for Text Classification Using Graph Neural Networks
1 Introduction
2 Related Work
3 Proposed Method
3.1 Graph Creation and Coarsening Step
3.2 Graph Representation Learning
4 Experiments
4.1 Datasets
4.2 Hyperparameter Settings
4.3 Experimental Results
5 Concluding Remarks
References
Towards Robust Defect Detection in Casting Using Contrastive Learning
1 Introduction
2 Related Work
3 Method
4 Experiments
4.1 Dataset Description
4.2 Experimental Procedure and Preliminary Results
4.3 Robust Defect Detection
4.4 Discussion
5 Conclusions
References
Development and Testing of an MRI-Compatible Immobilization Device for Head and Neck Imaging
1 Introduction
2 Materials and Methods
2.1 The Immobilization Device
2.2 Software Developed to Analyse MRI Images
2.3 Digital Algorithm Tests
2.4 Movement Phantom Algorithm Tests
3 Results
4 Discussion
5 Conclusions
References
DIF-SR: A Differential Item Functioning-Based Sample Reweighting Method
1 Introduction
2 Background
2.1 Group Fairness Analysis
2.2 Item Response Theory
2.3 Differential Item Functioning
2.4 Related Work
3 Proposed Method: DIF-SR
3.1 Base Classifier Predictions-Step 1
3.2 Item Modeling-Step 2
3.3 IRT Calibration-Step 3
3.4 Sample Weighting-Step 4
4 Experimental Setting
4.1 Datasets
4.2 Classification Algorithms
4.3 Sample Reweighting Methods
4.4 Evaluation Measures
5 Results
6 Conclusion
References
IR-Guided Energy Optimization Framework for Depth Enhancement in Time of Flight Imaging
1 Introduction
2 Related Work
3 IR Image as Guidance
4 Proposed Model
4.1 Image Energy Function
4.2 Spatial Error Energy Term
4.3 Conditional Entropy Energy Term
4.4 Image Energy Minimization
5 Data and Preprocessing
6 Experiments and Results
7 Conclusions
References
Multi-conformation Aproach of ENM-NMA Dynamic-Based Descriptors for HIV Drug Resistance Prediction
1 Introduction
2 Materials and Methods
2.1 Dataset
2.2 Computational Prediction
3 Results and Discussion
4 Conclusions and Further Work
References
Replay-Based Online Adaptation for Unsupervised Deep Visual Odometry
1 Introduction
1.1 Unsupervised Deep Visual Odometry
1.2 Online Adaptation
2 Methodology
2.1 Self-supervision
2.2 Replay-Based Online Adaptation
2.3 Model Setup
3 Evaluation Protocol
4 Results
5 Conclusion
References
Facial Point Graphs for Stroke Identification
1 Introduction
2 Related Works
3 Theoretical Background
3.1 Graph Neural Networks
3.2 Graph Attention Networks
4 Experimental Methodology
4.1 Experimental Dataset
4.2 Pre-processing and Feature Extraction
4.3 Classification and Evaluation
4.4 Proposed Model
5 Results
6 Discussion and Conclusion
References
Fast, Memory-Efficient Spectral Clustering with Cosine Similarity
1 Introduction
2 Methodology
2.1 Learning from a Single Batch of Data
2.2 How to Choose the Batch Size s
2.3 Out of Sample Extension
3 Experiments
3.1 Real-World Benchmark Data Sets
3.2 Experimental Setup
3.3 Choosing the Optimal Sample Size
3.4 Method Comparisons
4 Conclusions and Future Work
References
An End-to-End Deep Learning Approach for Video Captioning Through Mobile Devices
1 Introduction
2 Related Works
3 Video Captioning Frameworks
3.1 FW1: Multiple Image Captioning with Audio Classification
3.2 FW2: Video Captioning with Audio Classification
4 Experiment Setup
4.1 Datasets and Backbones
4.2 Hardware, Software and Training Settings
5 Results and Discussion
5.1 Image Feature Extractors Comparison
5.2 Qualitative Assessment of Video Descriptions
5.3 Strategies Resources Consumption
6 Conclusion and Future Works
References
Stingless Bee Classification: A New Dataset and Baseline Results
1 Introduction
2 Related Work
3 Methodological Design
3.1 Data Acquisition
3.2 Classifier Description
3.3 Experimental Setup
4 Results and Discussions
5 Conclusion and Future Work
References
Author Index

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