book

Deep Learning for Robot Perception and Cognition

Name: Deep Learning for Robot Perception and Cognition
ISBN: 9780323885720

by Alexandros Iosifidis, Anastasios Tefas

February 2022

Beginner to intermediate

634 pages

22h 8m

English

Academic Press

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Cover image
Title page
Table of Contents
Copyright
List of contributors
Preface
Acknowledgements
Editors biographies
Chapter 1: Introduction
Abstract1.1. Artificial intelligence and machine learning1.2. Real world problems representation1.3. Machine learning tasks1.4. Shallow and deep learning1.5. Robotics and deep learningReferences
Chapter 2: Neural networks and backpropagation
Abstract2.1. Introduction2.2. Activation functions2.3. Cost functions2.4. Backpropagation2.5. Optimizers and training2.6. Overfitting2.7. Concluding remarksReferences

Chapter 3: Convolutional neural networks
Abstract3.1. Introduction3.2. Structure of convolutional neural networks3.3. Training convolutional neural networks3.4. ConclusionsReferences
Chapter 4: Graph convolutional networks
Abstract4.1. Introduction4.2. Spectral graph convolutional network4.3. Spatial graph convolutional network4.4. Graph attention network (GAT)4.5. Graph convolutional networks for large graphs4.6. Datasets and libraries4.7. ConclusionReferences
Chapter 5: Recurrent neural networks
Abstract5.1. Introduction5.2. Vanilla RNN5.3. Long-short term memory5.4. Gated recurrent unit5.5. Other RNN variants5.6. Applications5.7. Concluding remarksReferences
Chapter 6: Deep reinforcement learning
Abstract6.1. Introduction6.2. Value-based methods6.3. Policy-based methods6.4. Concluding remarksReferences
Chapter 7: Lightweight deep learning
Abstract7.1. Introduction7.2. Lightweight convolutional neural network architectures7.3. Regularization of lightweight convolutional neural networks7.4. Bag-of-features for improved representation learning7.5. Early exits for adaptive inference7.6. Concluding remarksReferences
Chapter 8: Knowledge distillation
Abstract8.1. Introduction8.2. Neural network distillation8.3. Probabilistic knowledge transfer8.4. Multilayer knowledge distillation8.5. Teacher training strategies8.6. Concluding remarksReferences
Chapter 9: Progressive and compressive learning
Abstract9.1. Introduction9.2. Progressive neural network learning9.3. Compressive learning9.4. ConclusionsReferences
Chapter 10: Representation learning and retrieval
Abstract10.1. Introduction10.2. Discriminative and self-supervised autoencoders10.3. Deep representation learning for content based image retrieval10.4. Model retraining methods for image retrieval10.5. Variance preserving supervised representation learning10.6. Concluding remarksReferences
Chapter 11: Object detection and tracking
Abstract11.1. Object detection11.2. Object tracking11.3. ConclusionReferences
Chapter 12: Semantic scene segmentation for robotics
Abstract12.1. Introduction12.2. Algorithms and architectures for semantic segmentation12.3. Loss functions for semantic segmentation12.4. Semantic segmentation using multiple inputs12.5. Semantic segmentation data sets and benchmarks12.6. Semantic segmentation metrics12.7. ConclusionReferences
Chapter 13: 3D object detection and tracking
Abstract13.1. Introduction13.2. 3D object detection13.3. 3D object tracking13.4. ConclusionReferences
Chapter 14: Human activity recognition
Abstract14.1. Introduction14.2. Trimmed action recognition14.3. Temporal action localization14.4. Spatiotemporal action localization14.5. Data sets for human activity recognition14.6. ConclusionReferences
Chapter 15: Deep learning for vision-based navigation in autonomous drone racing
Abstract15.1. Introduction15.2. System decomposition approach in drone racing navigation15.3. Transfer learning and end-to-end planning15.4. Useful tools for data collection and training15.5. Conclusions and future workReferences
Chapter 16: Robotic grasping in agile production
Abstract16.1. Introduction16.2. Grasping and object manipulation16.3. Grasp evaluation16.4. Manipulation benchmarking16.5. Data sets16.6. ConclusionReferences
Chapter 17: Deep learning in multiagent systems
Abstract17.1. Introduction17.2. Setting the scene17.3. Challenges17.4. Deep learning in multiagent systems17.5. ConclusionReferences
Chapter 18: Simulation environments
Abstract18.1. Introduction18.2. Robotic simulators18.3. ConclusionsReferences
Chapter 19: Biosignal time-series analysis
Abstract19.1. Introduction19.2. ECG classification and advance warning for arrhythmia19.3. Early prediction of mortality risk for COVID-19 patients19.4. ConclusionReferences
Chapter 20: Medical image analysis
Abstract20.1. Introduction20.2. Early detection of myocardial infarction using echocardiography20.3. COVID-19 recognition from X-ray images via convolutional sparse support estimator based classifier20.4. ConclusionReferences
Chapter 21: Deep learning for robotics examples using OpenDR
Abstract21.1. Introduction21.2. Structure of OpenDR toolkit and application examples21.3. Cointegration of simulation and training21.4. Concluding remarksReferences
Index

Content preview from Deep Learning for Robot Perception and Cognition

Chapter 20: Medical image analysis

Aysen Degerli^a; Mehmet Yamac^a; Mete Ahishali^a; Serkan Kiranyaz^b; Moncef Gabbouj^a ^aFaculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland^bDepartment of Electrical Engineering, Qatar University, Doha, Qatar

Abstract

This chapter presents deep learning methodologies for medical imaging tasks. The chapter starts with echocardiography for early detection of myocardial infarction (MI) or commonly known as heart attack. Early and fundamental signs of MI can be visible as the abnormality in one or several segments of the left ventricle (LV) wall, where a segment may move “abnormally” or “nonuniformly.” The primary tool to detect and identify such regional wall motion abnormalities ...

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Deep Learning for Robot Perception and Cognition

by Alexandros Iosifidis, Anastasios Tefas

Chapter 20: Medical image analysis

Abstract

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