Pattern Recognition Applications and Methods: 9th International Conference, ICPRAM 2020, Valletta, Malta, February 22–24, 2020, Revised Selected Papers

✍ Scribed by Maria De Marsico; Gabriella Sanniti di Baja; Ana Fred

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 150
• Series: Lecture Notes in Computer Science, 12594
• Category: Library

✦ Synopsis

This book contains revised and extended versions of selected papers from the 9th International Conference on Pattern Recognition, ICPRAM 2020, held in Valletta, Malta, in February 2020. The 7 full papers presented were carefully reviewed and selected from 102 initial submissions. The papers describe applications of pattern recognition techniques to real-world problems, interdisciplinary research, experimental and theoretical studies yielding new insights that advance pattern recognition methods are especially encouraged.

✦ Table of Contents

Preface
Organization
Contents
End to End Deep Neural Network Classifier Design for Universal Sign Recognition
1 Introduction
2 Related Work
3 Methodology
4 Experimentation and Results
5 Conclusion and Future Work
References
MaskADNet: MOTS Based on ADNet
1 Introduction
2 Related Work
2.1 Multi-object Tracking
2.2 Video Instance Segmentation
2.3 Segmentation for Tracking
2.4 Semi-automatic Segmentation
3 Multi-object Tracking and Segmentation Using MaskADNet
3.1 Baseline Method
3.2 MaskADNet
4 Results and Discussion
4.1 Dataset Description
4.2 Evaluation
5 Conclusion
References
Dimensionality Reduction and Attention Mechanisms for Extracting Affective State from Sound Spectrograms
1 Introduction
2 Related Literature Review
3 Methodology
3.1 BoVW Representation Building
3.2 Choosing Target Variables
3.3 Temporal Structure Preserving Representation
4 Experiment Details
4.1 Dataset Description
4.2 Experimental Procedure Description
5 Discussion on Results
6 Closing Remarks
References
Efficient Radial Distortion Correction for Planar Motion
1 Introduction
2 Related Work
2.1 Homography Estimation
2.2 Modelling Radial Distortion
3 The General Planar Motion Model
4 Polynomial Solvers
4.1 A Non-minimal Relaxation (4 Point)
4.2 Minimal Solver with Known Tilt (2 Point)
5 Experiments
5.1 Synthetic Data
5.2 Numerical Stability
5.3 Noise Sensitivity
5.4 Image Stitching
5.5 Application to Visual Odometry
5.6 Application to Aerial Imagery
6 Conclusions
References
A Preliminary Study on Tree-Top Detection and Deep Learning Classification Using Drone Image Mosaics of Japanese Mixed Forests
1 Introduction and State of the Art
1.1 State of Art
2 Data Gathering, Annotation and Preprocessing
2.1 Data Acquisition
2.2 Data Processing and Annotation
2.3 Challenges in Data and Limitations of This Study
3 Materials and Methods
3.1 Interest Region Extraction
3.2 Tree Top Detection Algorithm
3.3 Tree Top Classification
4 Experiments
4.1 Tree Top Detection
4.2 Interest Region Extraction
4.3 Tree Classification
4.4 Time Considerations
5 Conclusions
References
Investigating Similarity Metrics for Convolutional Neural Networks in the Case of Unstructured Pruning
1 Introduction
2 Related Work
2.1 Techniques for DNN Pruning
2.2 Comparisons Between Pruned and Unpruned DNNs
3 Tools
3.1 IMP
3.2 Similarity Metrics for Neural Networks Data Representations
3.3 Kernel-Based Metrics
4 Methods
4.1 Datasets
4.2 CNN Architectures and Optimizers Used
5 Results
5.1 Test-Set Accuracy
5.2 Layer-Wise Pruned vs. Unpruned Similarity
5.3 ResNet_fast
6 Discussion
6.1 Takeaways from Results
6.2 Comparing Output Layers
6.3 Considerations on the Rotational Invariance of Similarity Metrics for Convolutional Layers
7 Conclusions and Future Work
References
Encoding of Indefinite Proximity Data: A Structure Preserving Perspective
1 Introduction
2 Non-metric Proximity-Based Learning
2.1 Notation and Basic Concepts
2.2 Indefinite Proximities
2.3 Eigenspectrum Corrections
2.4 Limitations
3 Eigenvalue Modification via Nullspace Integrated Shifting
3.1 Advanced Shift Correction
3.2 Determination and Approximation of the Shift Parameter
3.3 Out-of-Sample Extension for New Test Points
3.4 Structure Preservation of the Eigenspectrum
4 Experiments
4.1 Datasets
4.2 Results
5 Conclusions
References
Author Index